From 7759a9540ed2dad4d200553a2dab1929138c8d98 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Mon, 22 May 2017 17:07:20 +0530 Subject: [PATCH 01/28] french wiki issue resolved --- gensim/models/wrappers/fasttext.py | 65 ++++++++++++++++++++---------- 1 file changed, 44 insertions(+), 21 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 49a6b6a925..b480d6c0ee 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -1,7 +1,8 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # -# Copyright (C) 2013 Radim Rehurek +# Author: Jayant Jain +# Copyright (C) 2017 Radim Rehurek # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html @@ -41,6 +42,8 @@ logger = logging.getLogger(__name__) +FASTTEXT_FILEFORMAT_MAGIC = 793712314 + class FastTextKeyedVectors(KeyedVectors): """ @@ -221,7 +224,7 @@ def load_word2vec_format(cls, *args, **kwargs): return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) @classmethod - def load_fasttext_format(cls, model_file): + def load_fasttext_format(cls, model_file, encoding='utf8'): """ Load the input-hidden weight matrix from the fast text output files. @@ -234,8 +237,8 @@ def load_fasttext_format(cls, model_file): """ model = cls() - model.wv = cls.load_word2vec_format('%s.vec' % model_file) - model.load_binary_data('%s.bin' % model_file) + model.wv = cls.load_word2vec_format('%s.vec' % model_file, encoding=encoding) + model.load_binary_data('%s.bin' % model_file, encoding=encoding) return model @classmethod @@ -248,17 +251,25 @@ def delete_training_files(cls, model_file): logger.debug('Training files %s not found when attempting to delete', model_file) pass - def load_binary_data(self, model_binary_file): + def load_binary_data(self, model_binary_file, encoding='utf8'): """Loads data from the output binary file created by FastText training""" with utils.smart_open(model_binary_file, 'rb') as f: self.load_model_params(f) - self.load_dict(f) + self.load_dict(f, encoding=encoding) self.load_vectors(f) def load_model_params(self, file_handle): - (dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t) = self.struct_unpack(file_handle, '@12i1d') + magic, version = self.struct_unpack(file_handle, '@2i') + if magic == FASTTEXT_FILEFORMAT_MAGIC : # newer format + self.new_format = True + dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@12i1d') + else: # older format + self.new_format = True + dim = magic + ws = v + epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@10i1d') # Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc) - self.size = dim + self.vector_size = dim self.window = ws self.iter = epoch self.min_count = minCount @@ -270,29 +281,41 @@ def load_model_params(self, file_handle): self.wv.max_n = maxn self.sample = t - def load_dict(self, file_handle): - (vocab_size, nwords, _) = self.struct_unpack(file_handle, '@3i') + def load_dict(self, file_handle, encoding='utf8'): + vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' - assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes' - ntokens, = self.struct_unpack(file_handle, '@q') - for i in range(nwords): + if len(self.wv.vocab) != vocab_size: + logger.warnings("If you are loading any model other than pretrained vector wiki.fr, ") + logger.warnings("Please report to gensim or fastText.") + ntokens= self.struct_unpack(file_handle, '@1q') + if self.new_format: + pruneidx_size = self.struct_unpack(file_handle, '@q') + for i in range(vocab_size): word_bytes = b'' char_byte = file_handle.read(1) # Read vocab word while char_byte != b'\x00': word_bytes += char_byte char_byte = file_handle.read(1) - word = word_bytes.decode('utf8') - count, _ = self.struct_unpack(file_handle, '@ib') - _ = self.struct_unpack(file_handle, '@i') - assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' - self.wv.vocab[word].count = count + word = word_bytes.decode(encoding) + count, _ = self.struct_unpack(file_handle, '@qb') + if word in self.wv.vocab: + # skip loading info about words in bin file which are not present in vec file + # handling mismatch in vocab_size in vec and bin files (ref: wiki.fr) + assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' + self.wv.vocab[word].count = count + + for j in range(pruneidx_size): + _,_ = self.struct_unpack(file_handle,'@2i') + def load_vectors(self, file_handle): + if self.new_format: + _ = self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') # Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc) - assert self.size == dim, 'mismatch between model sizes' + assert self.vector_size == dim, 'mismatch between model sizes' float_size = struct.calcsize('@f') if float_size == 4: dtype = np.dtype(np.float32) @@ -300,9 +323,9 @@ def load_vectors(self, file_handle): dtype = np.dtype(np.float64) self.num_original_vectors = num_vectors - self.wv.syn0_all = np.fromstring(file_handle.read(num_vectors * dim * float_size), dtype=dtype) + self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim) self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) - assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.size), \ + assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ 'mismatch between weight matrix shape and vocab/model size' self.init_ngrams() From 80257100228bfa580189186f224deeb6b99f7921 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Mon, 22 May 2017 17:59:06 +0530 Subject: [PATCH 02/28] bin and vec mismatch handled --- gensim/models/wrappers/fasttext.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 870f49193d..474462fa99 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -260,13 +260,13 @@ def load_binary_data(self, model_binary_file, encoding='utf8'): def load_model_params(self, file_handle): magic, version = self.struct_unpack(file_handle, '@2i') - if magic == FASTTEXT_FILEFORMAT_MAGIC : # newer format + if magic == FASTTEXT_FILEFORMAT_MAGIC: # newer format self.new_format = True dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@12i1d') else: # older format self.new_format = True dim = magic - ws = v + ws = version epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@10i1d') # Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc) self.vector_size = dim @@ -306,8 +306,8 @@ def load_dict(self, file_handle, encoding='utf8'): assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' self.wv.vocab[word].count = count - for j in range(pruneidx_size): - _,_ = self.struct_unpack(file_handle,'@2i') + for j in range(pruneidx_size): + _, _ = self.struct_unpack(file_handle, '@2i') def load_vectors(self, file_handle): if self.new_format: From 22c6710e37b0a4e5e654e86f585b9c31811cc19b Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Sat, 3 Jun 2017 02:22:56 +0530 Subject: [PATCH 03/28] added test from bin only loading --- gensim/test/test_fasttext_wrapper.py | 31 ++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index b0de495ad4..c9b8fc1ef1 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -171,6 +171,37 @@ def testLoadFastTextNewFormat(self): self.assertEquals(self.test_new_model.wv.min_n, 3) self.model_sanity(new_model) + def testLoadBinOnly(self): + """ Test model succesfully loaded from fastText (new format) .bin files only """ + new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file, bin_only = True) + vocab_size, model_size = 1763, 10 + self.assertEqual(self.test_new_model.wv.syn0.shape, (vocab_size, model_size)) + self.assertEqual(len(self.test_new_model.wv.vocab), vocab_size, model_size) + self.assertEqual(self.test_new_model.wv.syn0_all.shape, (self.test_new_model.num_ngram_vectors, model_size)) + + expected_vec_new = [-0.025627, + -0.11448, + 0.18116, + -0.96779, + 0.2532, + -0.93224, + 0.3929, + 0.12679, + -0.19685, + -0.13179] # obtained using ./fasttext print-word-vectors lee_fasttext_new.bin < queries.txt + + self.assertTrue(numpy.allclose(self.test_new_model["hundred"], expected_vec_new, 0.001)) + self.assertEquals(self.test_new_model.min_count, 5) + self.assertEquals(self.test_new_model.window, 5) + self.assertEquals(self.test_new_model.iter, 5) + self.assertEquals(self.test_new_model.negative, 5) + self.assertEquals(self.test_new_model.sample, 0.0001) + self.assertEquals(self.test_new_model.bucket, 1000) + self.assertEquals(self.test_new_model.wv.max_n, 6) + self.assertEquals(self.test_new_model.wv.min_n, 3) + self.model_sanity(new_model) + + def testLoadModelWithNonAsciiVocab(self): """Test loading model with non-ascii words in vocab""" model = fasttext.FastText.load_fasttext_format(datapath('non_ascii_fasttext')) From 61be6133d319d193bb3dcb65970bba442b4a217b Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Sat, 3 Jun 2017 03:22:45 +0530 Subject: [PATCH 04/28] [WIP] loading bin only --- gensim/models/wrappers/fasttext.py | 54 +++++++++++++++++++++--------- 1 file changed, 38 insertions(+), 16 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index a02a2b05d3..0c066d21af 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -35,11 +35,15 @@ import numpy as np from numpy import float32 as REAL, sqrt, newaxis from gensim import utils -from gensim.models.keyedvectors import KeyedVectors +from gensim.models.keyedvectors import KeyedVectors, Vocab from gensim.models.word2vec import Word2Vec from six import string_types +from numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\ + double, uint32, seterr, array, uint8, vstack, fromstring, sqrt, newaxis,\ + ndarray, empty, sum as np_sum, prod, ones, ascontiguousarray + logger = logging.getLogger(__name__) FASTTEXT_FILEFORMAT_MAGIC = 793712314 @@ -224,7 +228,7 @@ def load_word2vec_format(cls, *args, **kwargs): return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) @classmethod - def load_fasttext_format(cls, model_file, encoding='utf8'): + def load_fasttext_format(cls, model_file, bin_only = False, encoding='utf8'): """ Load the input-hidden weight matrix from the fast text output files. @@ -237,8 +241,11 @@ def load_fasttext_format(cls, model_file, encoding='utf8'): """ model = cls() - model.wv = cls.load_word2vec_format('%s.vec' % model_file, encoding=encoding) - model.load_binary_data('%s.bin' % model_file, encoding=encoding) + if bin_only: + model.load_binary_data('%s.bin' % model_file, bin_only, encoding=encoding) + else: + model.wv = cls.load_word2vec_format('%s.vec' % model_file, encoding=encoding) + model.load_binary_data('%s.bin' % model_file, encoding=encoding) return model @classmethod @@ -251,11 +258,11 @@ def delete_training_files(cls, model_file): logger.debug('Training files %s not found when attempting to delete', model_file) pass - def load_binary_data(self, model_binary_file, encoding='utf8'): + def load_binary_data(self, model_binary_file, bin_only = False, encoding='utf8'): """Loads data from the output binary file created by FastText training""" with utils.smart_open(model_binary_file, 'rb') as f: self.load_model_params(f) - self.load_dict(f, encoding=encoding) + self.load_dict(f, bin_only, encoding=encoding) self.load_vectors(f) def load_model_params(self, file_handle): @@ -281,13 +288,18 @@ def load_model_params(self, file_handle): self.wv.max_n = maxn self.sample = t - def load_dict(self, file_handle, encoding='utf8'): + def load_dict(self, file_handle, bin_only = False, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' - if len(self.wv.vocab) != vocab_size: - logger.warnings("If you are loading any model other than pretrained vector wiki.fr, ") - logger.warnings("Please report to gensim or fastText.") + if not bin_only: + assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + if len(self.wv.vocab) != vocab_size: + logger.warnings("If you are loading any model other than pretrained vector wiki.fr, ") + logger.warnings("Please report to gensim or fastText.") + else: + self.wv.syn0 = zeros((vocab_size, self.vector_size), dtype=REAL) + logger.info("here?") + # TO-DO : how to update this ntokens= self.struct_unpack(file_handle, '@1q') if self.new_format: pruneidx_size, = self.struct_unpack(file_handle, '@q') @@ -300,14 +312,24 @@ def load_dict(self, file_handle, encoding='utf8'): char_byte = file_handle.read(1) word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') - if word in self.wv.vocab: - # skip loading info about words in bin file which are not present in vec file - # handling mismatch in vocab_size in vec and bin files (ref: wiki.fr) + if bin_only and word not in self.wv.vocab: + self.wv.vocab[word] = Vocab(index=i, count=count) + elif not bin_only: assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' self.wv.vocab[word].count = count - for j in range(pruneidx_size): - self.struct_unpack(file_handle, '@2i') + if bin_only: + #self.wv.syn0[i] = weight # How to get weight vector for each word ? + self.wv.index2word.append(word) + + if bin_only: + if self.wv.syn0.shape[0] != len(self.wv.vocab): + logger.info( + "duplicate words detected, shrinking matrix size from %i to %i", + self.wv.syn0.shape[0], len(self.wv.vocab) + ) + self.wv.syn0 = ascontiguousarray(result.syn0[: len(self.wv.vocab)]) + assert (len(self.wv.vocab), self.vector_size) == self.wv.syn0.shape if self.new_format: for j in range(pruneidx_size): From e11ac442fb7441f3034ed3622ada041f045d8f8f Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 6 Jun 2017 12:52:50 +0530 Subject: [PATCH 05/28] word vec from its ngrams --- gensim/models/wrappers/fasttext.py | 39 +++++++++++++++++++++--------- 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 0c066d21af..95df452e3b 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -263,7 +263,7 @@ def load_binary_data(self, model_binary_file, bin_only = False, encoding='utf8') with utils.smart_open(model_binary_file, 'rb') as f: self.load_model_params(f) self.load_dict(f, bin_only, encoding=encoding) - self.load_vectors(f) + self.load_vectors(f, bin_only) def load_model_params(self, file_handle): magic, version = self.struct_unpack(file_handle, '@2i') @@ -296,11 +296,10 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): if len(self.wv.vocab) != vocab_size: logger.warnings("If you are loading any model other than pretrained vector wiki.fr, ") logger.warnings("Please report to gensim or fastText.") - else: - self.wv.syn0 = zeros((vocab_size, self.vector_size), dtype=REAL) - logger.info("here?") + #else: + #self.wv.syn0 = zeros((vocab_size, self.vector_size), dtype=REAL) # TO-DO : how to update this - ntokens= self.struct_unpack(file_handle, '@1q') + self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: pruneidx_size, = self.struct_unpack(file_handle, '@q') for i in range(vocab_size): @@ -312,7 +311,7 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): char_byte = file_handle.read(1) word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') - if bin_only and word not in self.wv.vocab: + if bin_only: self.wv.vocab[word] = Vocab(index=i, count=count) elif not bin_only: assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' @@ -322,20 +321,20 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): #self.wv.syn0[i] = weight # How to get weight vector for each word ? self.wv.index2word.append(word) - if bin_only: + """if bin_only: if self.wv.syn0.shape[0] != len(self.wv.vocab): logger.info( "duplicate words detected, shrinking matrix size from %i to %i", self.wv.syn0.shape[0], len(self.wv.vocab) ) self.wv.syn0 = ascontiguousarray(result.syn0[: len(self.wv.vocab)]) - assert (len(self.wv.vocab), self.vector_size) == self.wv.syn0.shape + assert (len(self.wv.vocab), self.vector_size) == self.wv.syn0.shape""" if self.new_format: for j in range(pruneidx_size): self.struct_unpack(file_handle, '@2i') - def load_vectors(self, file_handle): + def load_vectors(self, file_handle, bin_only = False): if self.new_format: self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') @@ -352,13 +351,13 @@ def load_vectors(self, file_handle): self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ 'mismatch between weight matrix shape and vocab/model size' - self.init_ngrams() + self.init_ngrams(bin_only) def struct_unpack(self, file_handle, fmt): num_bytes = struct.calcsize(fmt) return struct.unpack(fmt, file_handle.read(num_bytes)) - def init_ngrams(self): + def init_ngrams(self, bin_only = False): """ Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams. Vectors for other ngrams are initialized with a random uniform distribution in FastText. These @@ -368,7 +367,23 @@ def init_ngrams(self): self.wv.ngrams = {} all_ngrams = [] for w, v in self.wv.vocab.items(): - all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) + word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) + all_ngrams += word_ngrams + + + if bin_only: + self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) + word_vec = np.zeros(self.wv.syn0.shape[1]) + + num_word_ngram_vectors = len(word_ngrams) + for word_ngram in word_ngrams: + ngram_hash = self.ft_hash(word_ngram) + word_vec += np.array(self.wv.syn0_all[(len(self.wv.vocab) + ngram_hash) % self.bucket]) + + self.wv.syn0[self.wv.vocab[w].index] = word_vec / num_word_ngram_vectors + # Still not working + + all_ngrams = set(all_ngrams) self.num_ngram_vectors = len(all_ngrams) ngram_indices = [] From a63a3bcdc29afc60b47c7c66d4e772a85c95a0c1 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 6 Jun 2017 13:14:07 +0530 Subject: [PATCH 06/28] [WIP] word vec from ngrams --- gensim/models/wrappers/fasttext.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 95df452e3b..7e9d26985a 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -335,6 +335,7 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): self.struct_unpack(file_handle, '@2i') def load_vectors(self, file_handle, bin_only = False): + logger.info("here??") if self.new_format: self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') @@ -366,13 +367,15 @@ def init_ngrams(self, bin_only = False): """ self.wv.ngrams = {} all_ngrams = [] + if bin_only: + self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) for w, v in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) all_ngrams += word_ngrams if bin_only: - self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) + #self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) word_vec = np.zeros(self.wv.syn0.shape[1]) num_word_ngram_vectors = len(word_ngrams) From 454d74e181c0b22ab332522b72e030394596a90f Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Wed, 7 Jun 2017 11:25:11 +0530 Subject: [PATCH 07/28] [WIP] getting syn0 from all n-grams --- gensim/models/wrappers/fasttext.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 7e9d26985a..26de416272 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -369,22 +369,11 @@ def init_ngrams(self, bin_only = False): all_ngrams = [] if bin_only: self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) - for w, v in self.wv.vocab.items(): - word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) - all_ngrams += word_ngrams - + for w, v in self.wv.vocab.items(): + all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) if bin_only: - #self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) - word_vec = np.zeros(self.wv.syn0.shape[1]) - - num_word_ngram_vectors = len(word_ngrams) - for word_ngram in word_ngrams: - ngram_hash = self.ft_hash(word_ngram) - word_vec += np.array(self.wv.syn0_all[(len(self.wv.vocab) + ngram_hash) % self.bucket]) - - self.wv.syn0[self.wv.vocab[w].index] = word_vec / num_word_ngram_vectors - # Still not working + self.wv.syn0[self.wv.vocab[w].index] += np.array(self.wv.syn0_all[self.wv.vocab[w].index]) all_ngrams = set(all_ngrams) @@ -396,6 +385,16 @@ def init_ngrams(self, bin_only = False): self.wv.ngrams[ngram] = i self.wv.syn0_all = self.wv.syn0_all.take(ngram_indices, axis=0) + ngram_weights = self.wv.syn0_all + + if bin_only: + for w,v in self.wv.vocab.items(): + word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) + for word_ngram in word_ngrams: + self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) + + self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams)+1) + @staticmethod def compute_ngrams(word, min_n, max_n): ngram_indices = [] From e6b0d8bf9e1fc43f4c9b5aa4f3cb17073f46af04 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Wed, 7 Jun 2017 16:40:39 +0530 Subject: [PATCH 08/28] [TDD] test comparing word vector from bin_only and default loading --- gensim/test/test_fasttext_wrapper.py | 62 ++++++++++++++++------------ 1 file changed, 35 insertions(+), 27 deletions(-) diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index c9b8fc1ef1..26d70d6226 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -14,6 +14,7 @@ import tempfile import numpy +from decimal import Decimal from gensim.models.wrappers import fasttext from gensim.models import keyedvectors @@ -31,7 +32,7 @@ def testfile(): class TestFastText(unittest.TestCase): @classmethod def setUp(self): - ft_home = os.environ.get('FT_HOME', None) + ft_home = os.environ.get('/home/prakhar/fastText/fasttext', None) self.ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None self.corpus_file = datapath('lee_background.cor') self.test_model_file = datapath('lee_fasttext') @@ -171,35 +172,42 @@ def testLoadFastTextNewFormat(self): self.assertEquals(self.test_new_model.wv.min_n, 3) self.model_sanity(new_model) + def testLoadBinOnly(self): - """ Test model succesfully loaded from fastText (new format) .bin files only """ - new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file, bin_only = True) - vocab_size, model_size = 1763, 10 - self.assertEqual(self.test_new_model.wv.syn0.shape, (vocab_size, model_size)) - self.assertEqual(len(self.test_new_model.wv.vocab), vocab_size, model_size) - self.assertEqual(self.test_new_model.wv.syn0_all.shape, (self.test_new_model.num_ngram_vectors, model_size)) + """ Compare the word vectors obtained from .vec file with word vectors obtained using all the + ngrams from .bin file """ - expected_vec_new = [-0.025627, - -0.11448, - 0.18116, - -0.96779, - 0.2532, - -0.93224, - 0.3929, - 0.12679, - -0.19685, - -0.13179] # obtained using ./fasttext print-word-vectors lee_fasttext_new.bin < queries.txt + model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath('/home/prakhar/prakhar'), bin_only = True) + # compare with self.test_new_model + """ Note for @jayantj -- model_bin_only file will be trained here using bin_only = True, and + we can use already loaded file from bin and vec - self.test_new_model, right ? + Here, remodelling becuse I wanted to use different corpus. - self.assertTrue(numpy.allclose(self.test_new_model["hundred"], expected_vec_new, 0.001)) - self.assertEquals(self.test_new_model.min_count, 5) - self.assertEquals(self.test_new_model.window, 5) - self.assertEquals(self.test_new_model.iter, 5) - self.assertEquals(self.test_new_model.negative, 5) - self.assertEquals(self.test_new_model.sample, 0.0001) - self.assertEquals(self.test_new_model.bucket, 1000) - self.assertEquals(self.test_new_model.wv.max_n, 6) - self.assertEquals(self.test_new_model.wv.min_n, 3) - self.model_sanity(new_model) + For text8 modelled corpus, out of 71290, 64278 words doesn't match""" + model_fasttext_only = fasttext.FastText.load_fasttext_format(os.path.abspath('/home/prakhar/prakhar')) + + + self.assertEquals(len(model_bin_only.wv.syn0), len(model_fasttext_only.wv.syn0)) + + + #count =0 + + for i in xrange(len(model_bin_only.wv.syn0)): + a = model_bin_only.wv.syn0[i] + #a = [float(Decimal("%.5f" % e)) for e in a] # without this, np.allclose won't give true + b = model_fasttext_only.wv.syn0[i] + #b = [float(Decimal("%.5f" % e)) for e in b] + + self.assertTrue(numpy.allclose(a,b)) + + #try: + # self.assertTrue(numpy.allclose(a,b)) + #except: + # count +=1 + # logger.info(model_bin_only.wv.index2word[i]) + + #logger.info("count") + #logger.info(count) def testLoadModelWithNonAsciiVocab(self): From 9b03ea35dcae81c96e06b6145550b625350af080 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Fri, 9 Jun 2017 01:57:22 +0530 Subject: [PATCH 09/28] cleaned up test code --- gensim/test/test_fasttext_wrapper.py | 31 ++++++---------------------- 1 file changed, 6 insertions(+), 25 deletions(-) diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index 26d70d6226..d65b49dfca 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -32,7 +32,7 @@ def testfile(): class TestFastText(unittest.TestCase): @classmethod def setUp(self): - ft_home = os.environ.get('/home/prakhar/fastText/fasttext', None) + ft_home = os.environ.get('FT_HOME', None) self.ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None self.corpus_file = datapath('lee_background.cor') self.test_model_file = datapath('lee_fasttext') @@ -177,38 +177,19 @@ def testLoadBinOnly(self): """ Compare the word vectors obtained from .vec file with word vectors obtained using all the ngrams from .bin file """ - model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath('/home/prakhar/prakhar'), bin_only = True) + model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath('self.test_new_model_file'), bin_only = True) # compare with self.test_new_model - """ Note for @jayantj -- model_bin_only file will be trained here using bin_only = True, and - we can use already loaded file from bin and vec - self.test_new_model, right ? - Here, remodelling becuse I wanted to use different corpus. - For text8 modelled corpus, out of 71290, 64278 words doesn't match""" - model_fasttext_only = fasttext.FastText.load_fasttext_format(os.path.abspath('/home/prakhar/prakhar')) - - - self.assertEquals(len(model_bin_only.wv.syn0), len(model_fasttext_only.wv.syn0)) - - - #count =0 + self.assertEquals(len(model_bin_only.wv.syn0), len(self.test_new_model.wv.syn0)) for i in xrange(len(model_bin_only.wv.syn0)): a = model_bin_only.wv.syn0[i] - #a = [float(Decimal("%.5f" % e)) for e in a] # without this, np.allclose won't give true - b = model_fasttext_only.wv.syn0[i] - #b = [float(Decimal("%.5f" % e)) for e in b] + a = [float(Decimal("%.5f" % e)) for e in a] # rounding off to 5 deciml digits + b = self.test_new_model.wv.syn0[i] + b = [float(Decimal("%.5f" % e)) for e in b] self.assertTrue(numpy.allclose(a,b)) - #try: - # self.assertTrue(numpy.allclose(a,b)) - #except: - # count +=1 - # logger.info(model_bin_only.wv.index2word[i]) - - #logger.info("count") - #logger.info(count) - def testLoadModelWithNonAsciiVocab(self): """Test loading model with non-ascii words in vocab""" From c496be9b866bf5d0fd5883d468d052e5ac8e9553 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Fri, 9 Jun 2017 02:03:18 +0530 Subject: [PATCH 10/28] added docstring for bin_only --- gensim/models/wrappers/fasttext.py | 36 ++++++++++-------------------- 1 file changed, 12 insertions(+), 24 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 26de416272..28b3951f29 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -40,10 +40,6 @@ from six import string_types -from numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\ - double, uint32, seterr, array, uint8, vstack, fromstring, sqrt, newaxis,\ - ndarray, empty, sum as np_sum, prod, ones, ascontiguousarray - logger = logging.getLogger(__name__) FASTTEXT_FILEFORMAT_MAGIC = 793712314 @@ -239,7 +235,12 @@ def load_fasttext_format(cls, model_file, bin_only = False, encoding='utf8'): FastText outputs two training files - `/path/to/train.vec` and `/path/to/train.bin` Expected value for this example: `/path/to/train` + `bin_only` is the optional parameter added to load the entire fastText model using + the .bin file only. + + TO-DO : finally update here if it improves loading time """ + model = cls() if bin_only: model.load_binary_data('%s.bin' % model_file, bin_only, encoding=encoding) @@ -294,11 +295,9 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): if not bin_only: assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' if len(self.wv.vocab) != vocab_size: - logger.warnings("If you are loading any model other than pretrained vector wiki.fr, ") - logger.warnings("Please report to gensim or fastText.") - #else: - #self.wv.syn0 = zeros((vocab_size, self.vector_size), dtype=REAL) - # TO-DO : how to update this + logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") + logger.warning("Please report to gensim or fastText.") + self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: pruneidx_size, = self.struct_unpack(file_handle, '@q') @@ -311,31 +310,20 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): char_byte = file_handle.read(1) word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') + if bin_only: self.wv.vocab[word] = Vocab(index=i, count=count) - elif not bin_only: + self.wv.index2word.append(word) + else: assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' self.wv.vocab[word].count = count - if bin_only: - #self.wv.syn0[i] = weight # How to get weight vector for each word ? - self.wv.index2word.append(word) - - """if bin_only: - if self.wv.syn0.shape[0] != len(self.wv.vocab): - logger.info( - "duplicate words detected, shrinking matrix size from %i to %i", - self.wv.syn0.shape[0], len(self.wv.vocab) - ) - self.wv.syn0 = ascontiguousarray(result.syn0[: len(self.wv.vocab)]) - assert (len(self.wv.vocab), self.vector_size) == self.wv.syn0.shape""" if self.new_format: for j in range(pruneidx_size): self.struct_unpack(file_handle, '@2i') def load_vectors(self, file_handle, bin_only = False): - logger.info("here??") if self.new_format: self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') @@ -392,7 +380,7 @@ def init_ngrams(self, bin_only = False): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) for word_ngram in word_ngrams: self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) - + self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams)+1) @staticmethod From d2ab903a9e33dbb20bcf274200ae908a4c80cc19 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 13 Jun 2017 00:33:59 +0530 Subject: [PATCH 11/28] resolved wiki.fr issue --- gensim/models/wrappers/fasttext.py | 14 ++++++++++++-- gensim/test/test_fasttext_wrapper.py | 14 +++++++------- 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 83d121553f..f16e3a7e47 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -291,12 +291,16 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, bin_only = False, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') + self.nwords = nwords # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) if not bin_only: assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' if len(self.wv.vocab) != vocab_size: logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") logger.warning("Please report to gensim or fastText.") + logger.warning("For pretrained vector wiki.fr, please load using param bin_only = True") + else: + logger.info("loading projection weights") self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -318,6 +322,11 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' self.wv.vocab[word].count = count + # now assert + if bin_only: + #assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + assert len(self.wv.vocab) == vocab_size + if self.new_format: for j in range(pruneidx_size): @@ -338,7 +347,7 @@ def load_vectors(self, file_handle, bin_only = False): self.num_original_vectors = num_vectors self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim) self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) - assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ + assert self.wv.syn0_all.shape == (self.bucket + (self.nwords), self.vector_size), \ 'mismatch between weight matrix shape and vocab/model size' self.init_ngrams(bin_only) @@ -356,7 +365,7 @@ def init_ngrams(self, bin_only = False): self.wv.ngrams = {} all_ngrams = [] if bin_only: - self.wv.syn0 = zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) + self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) for w, v in self.wv.vocab.items(): all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) @@ -382,6 +391,7 @@ def init_ngrams(self, bin_only = False): self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams)+1) + logger.info("loaded %s matrix from %s" % (self.wv.syn0.shape)) @staticmethod def compute_ngrams(word, min_n, max_n): diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index be9a97a456..cabce16fde 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -222,18 +222,18 @@ def testLoadBinOnly(self): """ Compare the word vectors obtained from .vec file with word vectors obtained using all the ngrams from .bin file """ - model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath('self.test_new_model_file'), bin_only = True) - # compare with self.test_new_model + model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file), bin_only = True) + model = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file)) - self.assertEquals(len(model_bin_only.wv.syn0), len(self.test_new_model.wv.syn0)) + self.assertEquals(len(model_bin_only.wv.syn0), len(model.wv.syn0)) for i in xrange(len(model_bin_only.wv.syn0)): + a = model_bin_only.wv.syn0[i] - a = [float(Decimal("%.5f" % e)) for e in a] # rounding off to 5 deciml digits - b = self.test_new_model.wv.syn0[i] - b = [float(Decimal("%.5f" % e)) for e in b] + b = model.wv.syn0[i] - self.assertTrue(numpy.allclose(a,b)) + self.assertTrue(numpy.allclose(a,b,atol=1e-3)) + def testLoadModelWithNonAsciiVocab(self): From 82507d11d51b9cc1b8d7835affc0021cf4399ff8 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 13 Jun 2017 01:12:53 +0530 Subject: [PATCH 12/28] pep8 fixes --- gensim/models/wrappers/fasttext.py | 18 +++++++++--------- gensim/test/test_fasttext_wrapper.py | 9 ++------- 2 files changed, 11 insertions(+), 16 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index f16e3a7e47..0d535b7c18 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -224,7 +224,7 @@ def load_word2vec_format(cls, *args, **kwargs): return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) @classmethod - def load_fasttext_format(cls, model_file, bin_only = False, encoding='utf8'): + def load_fasttext_format(cls, model_file, bin_only=False, encoding='utf8'): """ Load the input-hidden weight matrix from the fast text output files. @@ -236,7 +236,7 @@ def load_fasttext_format(cls, model_file, bin_only = False, encoding='utf8'): Expected value for this example: `/path/to/train` `bin_only` is the optional parameter added to load the entire fastText model using - the .bin file only. + the .bin file only. TO-DO : finally update here if it improves loading time """ @@ -259,7 +259,7 @@ def delete_training_files(cls, model_file): logger.debug('Training files %s not found when attempting to delete', model_file) pass - def load_binary_data(self, model_binary_file, bin_only = False, encoding='utf8'): + def load_binary_data(self, model_binary_file, bin_only=False, encoding='utf8'): """Loads data from the output binary file created by FastText training""" with utils.smart_open(model_binary_file, 'rb') as f: self.load_model_params(f) @@ -289,7 +289,7 @@ def load_model_params(self, file_handle): self.wv.max_n = maxn self.sample = t - def load_dict(self, file_handle, bin_only = False, encoding='utf8'): + def load_dict(self, file_handle, bin_only=False, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') self.nwords = nwords # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) @@ -324,7 +324,7 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): # now assert if bin_only: - #assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + # assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' assert len(self.wv.vocab) == vocab_size @@ -332,7 +332,7 @@ def load_dict(self, file_handle, bin_only = False, encoding='utf8'): for j in range(pruneidx_size): self.struct_unpack(file_handle, '@2i') - def load_vectors(self, file_handle, bin_only = False): + def load_vectors(self, file_handle, bin_only=False): if self.new_format: self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') @@ -355,7 +355,7 @@ def struct_unpack(self, file_handle, fmt): num_bytes = struct.calcsize(fmt) return struct.unpack(fmt, file_handle.read(num_bytes)) - def init_ngrams(self, bin_only = False): + def init_ngrams(self, bin_only=False): """ Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams. Vectors for other ngrams are initialized with a random uniform distribution in FastText. These @@ -385,12 +385,12 @@ def init_ngrams(self, bin_only = False): ngram_weights = self.wv.syn0_all if bin_only: - for w,v in self.wv.vocab.items(): + for w, v in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) for word_ngram in word_ngrams: self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) - self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams)+1) + self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams) + 1) logger.info("loaded %s matrix from %s" % (self.wv.syn0.shape)) @staticmethod diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index cabce16fde..270744d704 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -14,7 +14,6 @@ import tempfile import numpy -from decimal import Decimal from gensim.models.wrappers import fasttext from gensim.models import keyedvectors @@ -217,12 +216,10 @@ def testLoadFastTextNewFormat(self): self.assertEquals(new_model.wv.min_n, 3) self.model_sanity(new_model) - def testLoadBinOnly(self): """ Compare the word vectors obtained from .vec file with word vectors obtained using all the ngrams from .bin file """ - - model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file), bin_only = True) + model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file), bin_only=True) model = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file)) self.assertEquals(len(model_bin_only.wv.syn0), len(model.wv.syn0)) @@ -232,9 +229,7 @@ def testLoadBinOnly(self): a = model_bin_only.wv.syn0[i] b = model.wv.syn0[i] - self.assertTrue(numpy.allclose(a,b,atol=1e-3)) - - + self.assertTrue(numpy.allclose(a, b, atol=1e-3)) def testLoadModelWithNonAsciiVocab(self): """Test loading model with non-ascii words in vocab""" From 0fc11591f6477ebd67eb783ce242aaa39f078f71 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Fri, 16 Jun 2017 12:49:19 +0530 Subject: [PATCH 13/28] default bin file loading only --- gensim/models/wrappers/fasttext.py | 74 ++++++++++------------------ gensim/test/test_fasttext_wrapper.py | 15 ------ 2 files changed, 26 insertions(+), 63 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index b0a95f4e44..8cb0c455d4 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -224,7 +224,7 @@ def load_word2vec_format(cls, *args, **kwargs): return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) @classmethod - def load_fasttext_format(cls, model_file, bin_only=False, encoding='utf8'): + def load_fasttext_format(cls, model_file, encoding='utf8'): """ Load the input-hidden weight matrix from the fast text output files. @@ -233,20 +233,13 @@ def load_fasttext_format(cls, model_file, bin_only=False, encoding='utf8'): `model_file` is the path to the FastText output files. FastText outputs two training files - `/path/to/train.vec` and `/path/to/train.bin` - Expected value for this example: `/path/to/train` + Expected value for this example: `/path/to/train`. However, you only need .bin + file to load the entire model. - `bin_only` is the optional parameter added to load the entire fastText model using - the .bin file only. - - TO-DO : finally update here if it improves loading time """ model = cls() - if bin_only: - model.load_binary_data('%s.bin' % model_file, bin_only, encoding=encoding) - else: - model.wv = cls.load_word2vec_format('%s.vec' % model_file, encoding=encoding) - model.load_binary_data('%s.bin' % model_file, encoding=encoding) + model.load_binary_data('%s.bin' % model_file, encoding=encoding) return model @classmethod @@ -259,12 +252,12 @@ def delete_training_files(cls, model_file): logger.debug('Training files %s not found when attempting to delete', model_file) pass - def load_binary_data(self, model_binary_file, bin_only=False, encoding='utf8'): + def load_binary_data(self, model_binary_file, encoding='utf8'): """Loads data from the output binary file created by FastText training""" with utils.smart_open(model_binary_file, 'rb') as f: self.load_model_params(f) - self.load_dict(f, bin_only, encoding=encoding) - self.load_vectors(f, bin_only) + self.load_dict(f, encoding=encoding) + self.load_vectors(f) def load_model_params(self, file_handle): magic, version = self.struct_unpack(file_handle, '@2i') @@ -289,18 +282,10 @@ def load_model_params(self, file_handle): self.wv.max_n = maxn self.sample = t - def load_dict(self, file_handle, bin_only=False, encoding='utf8'): + def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') - self.nwords = nwords # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - if not bin_only: - assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' - if len(self.wv.vocab) != vocab_size: - logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") - logger.warning("Please report to gensim or fastText.") - logger.warning("For pretrained vector wiki.fr, please load using param bin_only = True") - else: - logger.info("loading projection weights") + logger.info("loading projection weights") self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -315,24 +300,21 @@ def load_dict(self, file_handle, bin_only=False, encoding='utf8'): word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') - if bin_only: + if word != "__label__": self.wv.vocab[word] = Vocab(index=i, count=count) self.wv.index2word.append(word) - else: - assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' - self.wv.vocab[word].count = count - # now assert - if bin_only: - # assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' - assert len(self.wv.vocab) == vocab_size + assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + if len(self.wv.vocab) != vocab_size: + logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") + logger.warning("Please report to Gensim.") if self.new_format: for j in range(pruneidx_size): self.struct_unpack(file_handle, '@2i') - def load_vectors(self, file_handle, bin_only=False): + def load_vectors(self, file_handle): if self.new_format: self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') @@ -347,15 +329,15 @@ def load_vectors(self, file_handle, bin_only=False): self.num_original_vectors = num_vectors self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim) self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) - assert self.wv.syn0_all.shape == (self.bucket + (self.nwords), self.vector_size), \ + assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ 'mismatch between weight matrix shape and vocab/model size' - self.init_ngrams(bin_only) + self.init_ngrams() def struct_unpack(self, file_handle, fmt): num_bytes = struct.calcsize(fmt) return struct.unpack(fmt, file_handle.read(num_bytes)) - def init_ngrams(self, bin_only=False): + def init_ngrams(self): """ Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams. Vectors for other ngrams are initialized with a random uniform distribution in FastText. These @@ -364,14 +346,11 @@ def init_ngrams(self, bin_only=False): """ self.wv.ngrams = {} all_ngrams = [] - if bin_only: - self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) + self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) for w, v in self.wv.vocab.items(): all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) - if bin_only: - self.wv.syn0[self.wv.vocab[w].index] += np.array(self.wv.syn0_all[self.wv.vocab[w].index]) - + self.wv.syn0[self.wv.vocab[w].index] += np.array(self.wv.syn0_all[self.wv.vocab[w].index]) all_ngrams = set(all_ngrams) self.num_ngram_vectors = len(all_ngrams) @@ -384,14 +363,13 @@ def init_ngrams(self, bin_only=False): ngram_weights = self.wv.syn0_all - if bin_only: - for w, v in self.wv.vocab.items(): - word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) - for word_ngram in word_ngrams: - self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) + for w, v in self.wv.vocab.items(): + word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) + for word_ngram in word_ngrams: + self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) - self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams) + 1) - logger.info("loaded %s matrix from %s" % (self.wv.syn0.shape)) + self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams) + 1) + logger.info("loaded %s matrix from %s" % (self.wv.syn0.shape)) @staticmethod def compute_ngrams(word, min_n, max_n): diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index 270744d704..b21d3d5a1a 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -216,21 +216,6 @@ def testLoadFastTextNewFormat(self): self.assertEquals(new_model.wv.min_n, 3) self.model_sanity(new_model) - def testLoadBinOnly(self): - """ Compare the word vectors obtained from .vec file with word vectors obtained using all the - ngrams from .bin file """ - model_bin_only = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file), bin_only=True) - model = fasttext.FastText.load_fasttext_format(os.path.abspath(self.test_model_file)) - - self.assertEquals(len(model_bin_only.wv.syn0), len(model.wv.syn0)) - - for i in xrange(len(model_bin_only.wv.syn0)): - - a = model_bin_only.wv.syn0[i] - b = model.wv.syn0[i] - - self.assertTrue(numpy.allclose(a, b, atol=1e-3)) - def testLoadModelWithNonAsciiVocab(self): """Test loading model with non-ascii words in vocab""" model = fasttext.FastText.load_fasttext_format(datapath('non_ascii_fasttext')) From f421b053202f111963ee6c182116054ae57590c3 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 20 Jun 2017 01:13:46 +0530 Subject: [PATCH 14/28] logging info modified plus changes a/c review --- gensim/models/wrappers/fasttext.py | 36 +++++++++++++++++++++--------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 8cb0c455d4..9398cea598 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -219,9 +219,11 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) super(FastText, self).save(*args, **kwargs) + """ @classmethod def load_word2vec_format(cls, *args, **kwargs): return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) + """ @classmethod def load_fasttext_format(cls, model_file, encoding='utf8'): @@ -285,7 +287,7 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - logger.info("loading projection weights") + logger.info("loading vocabulary words") self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -300,12 +302,24 @@ def load_dict(self, file_handle, encoding='utf8'): word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') - if word != "__label__": - self.wv.vocab[word] = Vocab(index=i, count=count) - self.wv.index2word.append(word) + #if word != "__label__": + + + if i == nwords and i < vocab_size: + """ + To handle the error in pretrained vector wiki.fr (French). + For more info : https://github.com/facebookresearch/fastText/issues/218 + + """ + assert word == "__label__" + continue # don't add word to vocab + + self.wv.vocab[word] = Vocab(index=i, count=count) + self.wv.index2word.append(word) assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' if len(self.wv.vocab) != vocab_size: + logger.warning("mismatch between vocab sizes") logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") logger.warning("Please report to Gensim.") @@ -348,9 +362,9 @@ def init_ngrams(self): all_ngrams = [] self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) - for w, v in self.wv.vocab.items(): + for w, vocab in self.wv.vocab.items(): all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) - self.wv.syn0[self.wv.vocab[w].index] += np.array(self.wv.syn0_all[self.wv.vocab[w].index]) + self.wv.syn0[vocab.index] += np.array(self.wv.syn0_all[vocab.index]) all_ngrams = set(all_ngrams) self.num_ngram_vectors = len(all_ngrams) @@ -363,13 +377,15 @@ def init_ngrams(self): ngram_weights = self.wv.syn0_all - for w, v in self.wv.vocab.items(): + logger.info("loading vocabulary weights") + + for w, vocab in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) for word_ngram in word_ngrams: - self.wv.syn0[self.wv.vocab[w].index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) + self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) - self.wv.syn0[self.wv.vocab[w].index] /= (len(word_ngrams) + 1) - logger.info("loaded %s matrix from %s" % (self.wv.syn0.shape)) + self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1) + logger.info("loaded %s matrix", self.wv.syn0.shape) @staticmethod def compute_ngrams(word, min_n, max_n): From 68ec73bc7b01f8b1cb014904e24f6706ca39e621 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 20 Jun 2017 01:50:01 +0530 Subject: [PATCH 15/28] removed unused code in fasttext.py --- gensim/models/wrappers/fasttext.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 9398cea598..9eff6edb59 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -219,12 +219,6 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) super(FastText, self).save(*args, **kwargs) - """ - @classmethod - def load_word2vec_format(cls, *args, **kwargs): - return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) - """ - @classmethod def load_fasttext_format(cls, model_file, encoding='utf8'): """ @@ -302,9 +296,6 @@ def load_dict(self, file_handle, encoding='utf8'): word = word_bytes.decode(encoding) count, _ = self.struct_unpack(file_handle, '@qb') - #if word != "__label__": - - if i == nwords and i < vocab_size: """ To handle the error in pretrained vector wiki.fr (French). From f7b372e2d4f62a784f78e64879c4148b7511fa9c Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 20 Jun 2017 02:07:12 +0530 Subject: [PATCH 16/28] removed unused codes and vec files from test --- gensim/test/test_data/cp852_fasttext.vec | 172 -- gensim/test/test_data/lee_fasttext.vec | 1763 ----------------- gensim/test/test_data/lee_fasttext_new.vec | 1764 ------------------ gensim/test/test_data/non_ascii_fasttext.vec | 172 -- gensim/test/test_fasttext_wrapper.py | 5 +- 5 files changed, 2 insertions(+), 3874 deletions(-) delete mode 100644 gensim/test/test_data/cp852_fasttext.vec delete mode 100644 gensim/test/test_data/lee_fasttext.vec delete mode 100644 gensim/test/test_data/lee_fasttext_new.vec delete mode 100644 gensim/test/test_data/non_ascii_fasttext.vec diff --git a/gensim/test/test_data/cp852_fasttext.vec b/gensim/test/test_data/cp852_fasttext.vec deleted file mode 100644 index 9d1969b0f6..0000000000 --- a/gensim/test/test_data/cp852_fasttext.vec +++ /dev/null @@ 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0.67949 0.10776 0.53149 -industry. 0.0092273 -0.37565 -0.20037 -0.46483 0.31335 -1.4774 -0.096699 0.73231 0.21874 0.52656 -tour -0.43954 -0.19026 -0.11173 -0.83247 -0.33728 -0.96407 0.11174 0.34995 -0.56476 0.18356 -Suharto -0.87874 0.12042 -0.15994 -0.80527 0.14874 -1.0192 0.0061843 0.65136 0.49705 0.042594 -tomorrow. -0.068397 -0.16142 -0.099732 -0.31752 0.027595 -1.181 -0.19693 1.0481 0.10702 0.6485 -Hobart -0.40508 -0.23509 0.36173 -0.62247 -0.81449 -0.57674 -0.092663 0.94042 -0.4049 0.19496 -yesterday, -0.23715 -0.072551 0.42636 -0.44529 -0.046663 -1.0071 -0.38182 1.0655 -0.12396 0.27852 -2,000 0.099853 0.044741 -0.11929 -0.7598 -0.33545 -0.78934 -0.056185 0.4076 -0.25193 0.87933 -wicket -0.079426 0.40289 0.35853 -0.96778 -1.0911 -0.35977 0.72483 0.88639 -0.68603 0.52199 -Reid -0.046752 -0.63395 -0.092827 -0.45231 -0.016952 -1.2688 0.15978 0.32874 0.078602 0.41831 -cabinet -0.72927 -0.24877 0.22627 -0.6884 -0.21956 -0.81994 0.47646 1.2002 0.34203 -0.2746 -provide -0.63287 -0.41829 0.24012 -0.70966 -0.2431 -0.75058 -0.32633 0.29332 -0.11356 0.38158 -Richard -0.37644 -0.60922 0.21232 -0.66811 -0.19052 -0.988 0.063575 0.34944 -0.31023 0.29261 -share -0.7711 -0.56008 0.53573 -0.49884 -0.19793 -0.73914 -0.57008 0.75933 -0.38981 0.23133 -Hewitt -0.30787 -0.29882 0.021498 -0.3843 -0.33191 -0.94711 -0.12048 0.77761 0.0069131 0.3869 -federal -0.4838 -0.42478 0.20567 -0.63983 0.040881 -0.98377 0.014822 0.62376 0.23546 0.31461 -ever -0.64672 -0.16055 0.14861 -0.8194 -0.28796 -0.86134 0.35801 0.36622 0.37592 -0.035676 -tribal -0.64211 -0.54691 0.17191 -0.56734 -0.0063289 -1.1274 0.23998 0.42009 0.1412 -0.16763 -country -0.77805 -0.92748 0.30379 -0.65351 -0.030827 -0.92047 -0.16022 0.26628 0.17352 0.12659 -changed -0.52327 -0.55094 0.19295 -0.55853 -0.20834 -0.97609 -0.3652 0.071883 0.029538 0.54407 -starting -0.46169 -0.50999 -0.058123 -0.73978 -0.016958 -0.91 0.24025 0.42762 -0.035204 0.28767 -5,000 -0.29395 0.068369 -0.18029 -0.63574 0.048494 -0.85869 -0.18597 0.97646 0.14903 0.35857 -stage -0.66806 -0.34818 -0.01396 -0.67032 -0.060946 -0.85306 -0.21959 0.65349 0.53493 0.26162 -survey -0.22242 -0.087934 0.38811 -0.26126 -0.58625 -0.75421 -0.13878 1.0151 -0.42212 0.45977 -absolutely -0.63469 -0.74938 0.087538 -0.81309 0.12101 -0.91824 -0.17939 0.051154 0.12888 0.43506 -small -0.71332 -0.46958 0.1701 -0.61473 -0.037699 -0.92908 -0.093239 0.51409 0.30154 0.27425 -offices -0.99956 0.070539 0.077916 -0.95078 -0.044151 -0.73524 0.14409 0.78637 0.55758 -0.35419 -global -0.44703 -0.59839 0.24321 -0.70627 0.22237 -1.1103 0.0017645 0.26625 0.29455 0.35686 -nearly -0.5257 -0.13128 0.42142 -0.77345 -0.49131 -0.5598 0.23629 0.78728 -0.1401 0.0033709 -French -0.91901 -0.14773 -0.21897 -1.0051 0.25846 -1.1588 0.66086 0.65286 0.63917 -0.58954 -ministers -0.97549 -0.63506 -0.055398 -0.82027 0.44184 -1.3243 0.13017 0.79134 0.6692 -0.40888 -secretary -0.64314 -0.35139 -0.14915 -0.72097 0.1525 -1.0265 0.12711 0.39252 0.32861 0.21684 -area. -0.57663 -0.8352 0.34768 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0.20337 0.0018021 -0.86136 -0.46389 -0.59082 0.20338 0.69156 -0.066353 0.49193 -force, -0.9936 -0.53781 0.049585 -0.60097 0.017685 -0.95836 -0.17046 1.0351 0.61785 -0.22209 -Simon 0.024231 0.11378 -0.33675 -0.41 0.16936 -1.2598 0.10345 0.25713 0.10542 1.092 -rights -0.45751 -0.69076 0.015417 -0.32252 -0.38762 -0.79263 -0.034619 1.4039 -0.011613 0.14712 -Perth. -0.44169 0.040914 0.28577 -0.71437 -0.61802 -0.51368 -0.17772 0.65362 -0.23293 0.46822 -India. -0.20225 -0.92663 -0.008708 -0.34339 0.017222 -1.2478 -0.046154 0.47811 0.065459 0.42233 -Tony -0.82862 -0.31718 0.085526 -0.65926 0.2121 -0.97746 0.27143 0.71986 0.56379 -0.15878 -hour -0.45488 0.057377 0.19187 -0.61432 -0.81711 -0.44354 -0.13434 0.87384 -0.42005 0.28991 -months. -0.45802 0.19037 0.12636 -0.88762 -0.11615 -0.48853 0.15943 1.1086 0.039966 0.31866 -management -0.46919 -0.5156 -0.2898 -0.54294 0.507 -1.2844 -0.29579 0.57462 0.75766 0.53533 -began -0.43988 -0.81965 0.20006 -0.58888 -0.10307 -1.1743 0.046398 0.44548 0.055165 0.11476 -threat -0.56857 -0.14488 0.047821 -0.90026 -0.21183 -0.76336 -0.058288 0.35999 0.13608 0.30395 -freeze -0.48188 -0.99623 -0.087136 -0.34492 0.19773 -1.1128 -0.2401 0.93702 0.70452 0.40433 -time, -0.47249 -0.29166 0.048025 -0.50322 0.10961 -1.0954 0.062447 0.83041 0.22984 0.15488 -significant -0.55159 -0.24458 0.041006 -0.75474 -0.051686 -0.85181 -0.2545 0.29067 0.11777 0.65681 -mean -0.3003 -0.80873 -0.21211 -0.6164 0.025246 -1.3568 0.23852 -0.026676 0.10032 0.4789 -damaged -0.094224 0.074958 0.25549 -0.27227 -0.29047 -0.92231 -0.50933 1.0131 0.008574 0.80034 -quickly -0.21029 -0.20662 -0.05791 -0.73478 -0.066713 -0.9085 -0.023358 0.31106 0.12451 0.61123 -control. -0.61809 -0.51828 0.41046 -0.55019 -0.29782 -0.68075 -0.46778 0.77951 -0.17226 0.24028 -storms -0.532 -0.84614 0.4456 -0.27544 -0.42596 -0.71124 -0.63012 0.67107 -0.14738 0.6868 -added. -0.52311 -0.41385 0.4767 -0.81253 -0.13042 -0.82314 0.26649 0.18023 0.064419 0.23956 -operating -0.62039 -0.17246 -0.32408 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0.043261 -0.57266 0.10554 0.37008 0.1899 -0.55675 -winning -0.22717 0.11865 -0.16047 -0.82654 -0.92389 -0.63676 0.45987 0.94152 -0.75564 0.11819 -space -0.34117 -0.081156 0.077317 -1.0006 -0.111 -1.078 0.88261 0.52974 0.27809 -0.12185 -fell -0.82764 -0.01633 0.53834 -1.0465 -0.70195 -0.34097 0.44582 0.46338 -0.40653 0.11575 -Bank, -0.92318 0.31172 0.11852 -0.66204 0.23116 -1.0075 -0.43549 1.4725 0.52893 -0.30507 -Endeavour -0.35022 -0.19489 -0.15907 -0.67883 0.069576 -1.0339 0.17495 0.5124 0.25385 0.26299 -circumstances -0.63659 -0.77506 0.50847 -0.77507 -0.48278 -0.51886 -0.37034 0.2151 -0.33686 0.46979 -women -0.41519 0.3101 0.062625 -0.69796 -0.39412 -0.40408 -0.40204 0.91959 0.29827 0.76272 -Space -0.36815 0.053405 0.0049708 -0.92795 0.15598 -1.2032 0.70822 0.70597 0.73626 -0.3846 -rest 0.12569 0.007577 -0.076527 -0.20381 0.64936 -1.5388 -0.91919 0.91707 0.32751 1.1623 -signed -0.96387 -0.081043 0.0034198 -1.0349 -0.29718 -0.68574 0.00909 0.56496 0.4217 -0.42813 -determined 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0.21194 -0.29507 0.14895 -1.3583 -0.036847 1.1654 0.31113 0.03606 -rise -0.077866 -0.22049 0.22011 -0.47346 -0.30512 -0.95906 -0.057872 0.37387 -0.19434 0.70503 -charge -0.86248 -0.051487 0.048603 -0.64312 -0.00010967 -0.86583 -0.11844 0.75634 0.11575 0.073119 -sea -0.7474 -1.0115 0.60371 -0.35888 0.43891 -1.5558 -0.43485 0.21859 0.12402 -0.18674 -potential -0.42596 -0.3534 0.23037 -0.40394 -0.014354 -0.95996 -0.10039 0.90199 0.032214 0.56359 -above -0.53287 -0.28928 0.4774 -0.56148 -0.23827 -0.58381 0.065599 1.079 0.18444 0.44148 -fleeing -0.84926 -0.76438 -0.11185 -0.57513 0.29073 -1.1069 0.1947 0.92829 0.17003 -0.2704 -Europe -0.38223 -0.34957 -0.045988 -0.57922 -0.2931 -0.78356 -0.14583 0.2477 -0.087893 0.6427 -lower -0.5335 -0.3231 -0.06268 -0.53007 -0.28725 -1.232 -0.12202 0.42683 0.15183 0.28252 -minister -0.43709 -0.27269 -0.38187 -0.76738 0.46035 -1.6554 0.42401 0.62687 0.58361 -0.23962 -heart -0.39326 -0.12832 0.15468 -0.6608 -0.32367 -0.77607 -0.15168 0.63694 -0.18139 0.44495 -finished -0.67038 -0.048662 0.13816 -0.77501 -0.19421 -0.84423 0.024174 0.57359 0.30737 0.18812 -closed -0.57287 0.44651 -0.13124 -0.46533 0.026976 -1.0935 0.067834 1.4666 0.56178 -0.029738 -negotiating -0.35012 -0.3543 -0.43022 -0.65378 0.15563 -1.1104 -0.18287 0.95027 0.30599 0.21201 -seeking -0.57404 -0.71375 -0.14434 -0.72882 0.30653 -1.0936 0.1671 0.32702 0.030399 0.36929 -Ian -0.71635 -0.00012376 -0.30962 -0.53138 -0.092568 -1.0933 0.25445 1.1689 0.71892 -0.322 -wanted -0.3812 -0.48911 -0.15324 -0.70435 0.0031387 -1.034 -0.19402 0.4969 0.36911 0.2919 -Another -0.46125 -0.30933 0.2413 -0.56648 -0.0066463 -1.0208 -0.68572 0.48353 0.089791 0.26648 -disappointed -0.6308 -0.21818 0.29523 -0.90658 -0.18866 -0.81609 0.31732 0.20668 -0.01522 0.079493 -5 0.28596 0.13213 -0.14674 0.041771 0.037901 -1.5466 -0.2494 2.1441 0.66849 0.74964 -nothing -0.44537 -0.55143 -0.016104 -0.87902 -0.018592 -0.71993 -0.28876 -0.092611 -0.33466 0.71615 -treated -0.19926 -0.24637 0.26896 -0.36527 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-0.25745 1.1506 -0.16192 0.27062 -remaining -0.55161 -0.75945 0.3203 -0.32003 -0.089138 -1.0561 -0.075077 1.1939 -0.33212 -0.18617 -Harrison's -0.61345 -0.38108 0.38306 -0.72809 0.061453 -0.87301 -0.079877 0.57389 0.15727 0.26114 -attacked -1.0644 0.36265 0.068071 -1.1878 -0.3154 -0.70458 0.56742 1.0689 0.70446 -0.64012 -research -0.6432 -0.46935 0.40981 -0.49033 0.16988 -1.0492 -0.24628 0.40572 -0.086253 0.39218 -resume -0.31747 -0.053101 -0.23088 -0.67539 0.27546 -0.89318 -0.058767 0.60759 0.21509 0.87131 -twice -0.37067 0.013751 -0.029718 -0.60756 -0.077144 -1.1456 -0.060062 0.50773 0.0099828 0.35136 -greater -0.30812 -0.29925 0.24134 -0.37356 -0.26489 -1.0628 -0.68335 0.67603 -0.079222 0.38484 -payment -0.48479 -0.47769 -0.17756 -0.49198 0.21487 -0.88194 -0.38507 0.3617 0.73045 1.0702 -Four -0.58685 -0.1535 -0.11726 -0.79971 -0.12464 -0.71044 -0.19297 0.26679 -0.0357 0.32148 -Andy -0.51611 -0.4954 0.34116 -0.76084 -0.062984 -1.1413 0.40215 0.40645 -0.079982 0.10159 -themselves 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-0.63764 1.0004 -0.009161 0.55908 -lost -0.73649 -0.64426 0.34471 -0.57446 -0.26344 -0.6458 -0.70543 0.51709 0.068686 0.40585 -paid -0.64307 -0.52904 -0.35425 -0.95187 0.29365 -1.1792 0.53845 0.35666 0.7277 -0.066245 -almost -0.16952 -0.54433 -0.14685 -0.69176 -0.076787 -0.83305 -0.29928 0.3596 0.10827 0.57156 -continued -0.73168 -0.633 0.34137 -0.35451 0.185 -1.0547 -0.2348 0.98408 0.32034 0.12933 -served -0.95115 -0.13649 0.55585 -0.75458 -0.028908 -0.75814 0.22513 1.0112 0.19349 -0.2433 -400 0.17329 -1.1301 0.23687 0.013158 0.22161 -1.5552 -0.53748 -0.033441 -0.20977 1.4976 -negotiations -0.24323 -0.40577 -0.34974 -0.36282 0.42091 -1.4421 -0.67343 0.98938 0.36921 0.43059 -Earlier, -0.38021 -0.077476 -0.19047 -0.63779 -0.11568 -1.0439 0.4253 0.84275 0.10235 -0.025321 -there, -0.71154 -0.41022 0.07575 -1.1943 0.18108 -0.747 -0.69517 -0.41093 -0.0078294 0.64982 -explosives -0.71675 -0.45373 -0.10398 -0.83958 0.099734 -0.98912 0.059012 0.58717 0.38908 -0.025943 -condition -0.78898 -0.88513 0.51345 -0.02141 0.24176 -1.2663 -0.65179 0.84507 -0.19054 0.24913 -anyone -0.52063 -0.24368 0.19916 -0.77511 -0.22626 -0.87014 0.10614 0.18682 0.062871 0.39093 -central -0.14224 -0.24728 0.051184 -0.09678 0.32091 -1.3701 -0.069086 0.86578 0.15505 0.88979 -Ansett -0.61012 -0.38503 0.26395 -0.67446 -0.19639 -0.89481 0.52586 0.81564 0.4097 0.026401 -added -0.77801 -0.27049 0.73781 -0.84549 -0.175 -0.66939 0.37865 0.70827 0.35287 -0.20735 -gas -0.68971 0.002995 0.040364 -0.61843 -0.064144 -1.0287 -0.021282 0.78301 0.46465 0.088024 -reduce -0.13254 -0.55168 -0.18349 -0.38073 0.30553 -1.3317 -0.041928 0.45836 0.37863 0.81625 -wind 0.01676 0.011053 0.44959 -0.2895 -0.88346 -0.83642 -0.66846 0.50925 -0.78732 1.0372 -business -0.56746 0.030466 -0.03184 -0.52534 0.1639 -1.0686 -0.20115 0.89598 0.51513 0.03091 -happened -0.5839 -0.2995 0.0088426 -0.87379 -0.031497 -1.0332 0.1604 0.30282 0.3707 -0.02492 -Team -0.55458 -0.47983 0.21303 -0.36086 -0.63719 -0.59825 0.048769 1.1558 -0.055718 0.38894 -they're -0.61947 -0.23126 0.21671 -0.87331 0.081281 -0.77211 -0.31625 0.19805 -0.33885 0.39788 -keep -0.48505 0.17648 0.13125 -0.81713 -0.51152 -0.66909 0.12683 0.51107 0.017452 0.29977 -community. -0.30235 -0.64945 0.18375 -0.23166 0.24128 -1.2964 -0.46996 0.73128 0.18677 0.50784 -secret -0.30597 -0.48269 -0.14815 -0.45639 0.29181 -1.2114 0.05162 0.43957 0.22667 0.54962 -issued -0.40357 -0.40101 0.18686 -0.20543 -0.15462 -1.0097 -0.15629 0.86986 0.28126 0.38403 -son -0.57406 0.66552 -0.61765 -1.2902 0.4431 -1.0434 0.16439 0.31346 0.80057 0.34247 -Zimbabwe -0.18342 -0.23639 0.041692 -0.69877 -0.25602 -0.90343 -0.022852 0.33251 -0.13257 0.75017 -remains -0.5641 -0.48887 0.29707 -0.37141 -0.044511 -1.0516 -0.15169 1.1607 0.16012 -0.10809 -confirm -0.92827 -0.91479 0.42196 -0.71845 -0.014625 -0.79897 -0.43567 0.31674 0.21632 0.19484 -resolution -0.28282 -0.56169 -0.034739 -0.13168 0.32963 -1.3589 -0.36709 0.69269 0.18904 0.97081 -flames -0.37359 -0.27439 0.16758 -0.69841 -0.5872 -0.53536 -0.30249 0.62558 -0.22372 0.57932 -traditional -0.51658 -0.19143 0.13852 -0.33116 0.27134 -1.3176 0.037727 0.8468 0.2007 0.20787 -crisis -0.36888 -0.50933 0.40532 -0.5597 -0.28622 -0.78698 0.10827 0.41267 -0.34042 0.45985 -wounded -0.96573 -0.093044 0.10916 -0.86741 0.17784 -0.83806 -0.17602 0.75522 0.48707 -0.089299 -expects -0.29595 -0.09363 0.17259 -0.63269 -0.48286 -0.98858 0.14787 0.24335 -0.27856 0.37221 -bomber -0.95657 0.14516 -0.29366 -1.2656 0.14205 -0.88241 0.62466 1.0746 0.65196 -0.80174 -built -0.21852 -0.086783 -0.021369 -0.40611 0.073216 -1.18 -0.33345 0.66553 0.12574 0.54236 -ballot -0.21911 -0.28845 0.00015781 -0.54957 -0.1316 -1.091 -0.11803 0.1613 0.086627 0.60737 -seekers. -0.72668 -0.63053 0.18623 -0.70964 0.64312 -1.1117 -0.24962 0.36772 0.58697 0.39818 -pulled -0.78037 -0.42757 0.59824 -0.56507 -0.22896 -0.7064 -0.44501 1.0242 0.041913 0.028871 -destroyed -0.51319 -0.10766 0.3864 -0.29695 -0.27521 -0.98746 -0.6147 1.4069 0.047631 0.12296 -Argentina's -0.73978 -0.37996 0.094767 -0.5825 0.060034 -0.94228 0.27304 1.0931 0.31286 0.11477 -heading -0.78992 -0.7237 -0.067411 -0.52338 0.10131 -1.0484 0.084318 1.241 0.05349 -0.35835 -March -0.16825 -0.73122 0.55244 -0.26429 -0.19719 -0.97887 0.012126 0.4783 -0.52273 0.65238 -receive -0.44503 -0.78071 0.16036 -0.36106 0.32759 -1.1761 -0.41691 0.46564 0.20787 0.60061 -families -0.6256 -0.40671 -0.042632 -0.75843 0.10948 -0.89509 -0.17088 0.54344 0.11841 0.028458 -battle -0.44961 -0.1307 0.14764 -0.88923 -0.2379 -0.78026 -0.45971 -0.029767 -0.41351 0.66005 -25 -0.62277 0.33289 -0.21263 -0.65328 -0.29053 -0.3457 -0.37894 0.27504 0.024049 0.82694 -24 -0.59891 0.42361 -0.059484 -0.43372 0.16825 -0.84188 -0.74288 1.489 0.18249 0.31487 -Americans -0.5137 -0.31001 0.15299 -0.70313 -0.39243 -0.75289 0.18887 0.59962 -0.28452 0.27749 -recorded -0.63561 -0.34336 0.31014 -0.54778 -0.0091003 -0.91579 0.16056 0.93231 0.28095 -0.018036 -ahead -0.75305 -0.10704 0.10345 -0.88951 -0.13051 -0.5991 -0.10829 0.77203 0.31553 -0.037083 -nations -0.48055 -0.5455 -0.21083 -0.10165 1.0774 -1.9804 -0.64335 1.095 0.70228 0.36164 -smoke -0.36695 -0.3613 0.21178 -0.60241 -0.1107 -1.0038 -0.11663 0.15457 -0.17639 0.55449 -City -0.75666 0.73824 -0.4909 -1.0752 0.077998 -1.0304 0.27599 0.7961 0.79915 -0.22213 -voted -0.34779 -0.63259 0.27459 -0.82707 -0.14057 -0.86399 0.25788 0.29369 0.15979 0.20032 -bowling -0.2256 -0.54594 -0.018689 -0.80518 -0.36536 -0.82469 0.76306 0.67708 -0.39653 0.099574 -approach -0.63574 -0.19202 0.17657 -0.56612 -0.094668 -0.83295 -0.24304 0.40642 0.049721 0.38701 -office -0.6108 0.20389 -0.14368 -0.95576 0.13525 -1.0648 0.34122 0.36386 0.69364 -0.14392 -region, -0.82259 -0.76607 0.28848 -0.41972 0.18911 -0.87877 -0.34521 0.60793 -0.11695 0.34186 -Attorney-General -0.33575 -0.6594 0.14796 -0.55623 -0.10269 -1.0083 0.028815 0.54213 0.034525 0.49881 -million. -0.41883 -0.55038 -0.13194 -0.53825 0.33496 -1.3299 0.088733 0.68461 0.44226 0.26666 -resolve 0.14433 -0.91024 -0.024571 -0.18114 0.07301 -1.3323 -0.3851 0.4598 -0.028314 1.2437 -ask -0.28788 -0.24803 0.0056372 -0.58598 0.077801 -1.1193 -0.092019 0.87987 -0.0077501 0.39431 -follows -0.11481 0.24324 -0.21674 -0.45322 -0.28267 -0.96383 0.15343 1.1288 0.10938 0.40552 -boats -0.56041 -0.56254 0.50086 -0.45544 -0.34165 -0.60744 -0.49756 1.3218 -0.15814 0.21637 -directors -0.35043 -0.096135 -0.023412 -0.41065 -0.24078 -1.0219 -0.35351 0.64319 -0.17108 0.65636 -Transport -0.15396 -0.28132 0.043649 -0.30098 -0.15588 -1.1152 -0.3766 0.79702 -0.019799 0.59078 -dozens -0.43584 0.15446 -0.44461 -0.88022 -0.21872 -0.78923 -0.071961 0.74939 0.47329 0.18914 -Timor -0.27481 -0.02298 0.038229 -0.19085 0.25347 -0.91929 -0.28953 1.2586 0.3971 1.0559 -played -0.26017 -0.18451 -0.0013391 -0.66695 -0.37175 -0.86988 0.0026808 0.28809 -0.072808 0.57953 -squad -0.58022 -0.22433 0.52511 -0.92439 -0.98217 -0.24458 0.38699 0.92745 -0.46329 -0.054524 -Hundreds -0.95307 -0.22751 0.41827 -0.83195 -0.36429 -0.42904 -0.068293 1.1664 0.095221 -0.21325 -While -0.4784 -0.6118 0.5424 -0.34001 0.078717 -1.0963 -0.20868 0.63923 -0.25069 0.5543 -Earlier -0.50915 0.23615 -0.26927 -0.83902 -0.078582 -1.0567 0.40804 0.86419 0.22934 -0.26779 -Thursday -0.24365 -0.097618 0.23479 -0.42976 -0.50011 -0.76639 -0.014127 0.82728 -0.30068 0.56477 -Philip -0.56202 -0.64276 0.060441 -0.22912 -0.17125 -1.1206 -0.10934 0.93592 -0.2319 0.15078 -Eve -0.16119 -0.025266 0.13094 -0.69694 -0.50174 -0.59828 -0.19812 0.59119 -0.39546 0.45593 -tailenders -0.6572 -0.61944 0.07943 -0.87822 -0.20369 -0.72605 0.35912 0.60753 -0.035484 -0.047806 -you're -0.40822 -0.09406 -0.083973 -1.1108 -0.31341 -0.583 -0.22147 -0.29145 -0.30689 0.77449 -verdict -0.51799 -0.60479 0.061774 -0.47275 0.072947 -1.2532 -0.34 0.60858 0.11739 0.33176 -gun -0.1397 0.40225 -0.31123 -0.99803 0.2261 -0.70375 0.54652 0.23428 0.13501 0.54419 -prior -0.79825 -0.41036 0.026393 -0.85202 0.03713 -0.77956 -0.30915 0.36947 0.32151 0.41758 -organisation -0.44735 -0.31944 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0.33785 -0.033902 0.30372 -felt -0.64452 -0.71498 0.29313 -0.94339 -0.47062 -0.92105 0.17417 0.059793 -0.070285 0.1089 -deployed -0.35924 -0.1611 0.30717 -0.33261 -0.2788 -0.82795 -0.40214 0.95351 0.16133 0.49489 -Senior -0.988 -0.32384 0.12795 -0.69657 -0.1773 -0.68238 0.11492 1.0485 0.22596 -0.27194 -government, -0.43991 -0.39481 0.1478 -0.37462 0.19661 -1.2291 -0.28272 0.66684 0.58157 0.48765 -north. -0.56745 0.0053902 0.57629 -0.49077 -0.65436 -0.5898 -0.40565 1.1025 -0.24705 0.40958 -Michael -0.3538 -0.2687 0.21468 -0.62904 -0.0085589 -1.0052 0.10223 0.69651 0.21984 0.13036 -rain -0.54389 -0.49622 0.36386 -0.64919 -0.3071 -1.0217 0.36402 0.88917 -0.012351 -0.17749 -3,000 -0.23084 -0.1531 -0.27296 -1.1779 0.17124 -0.82424 -0.17888 0.52062 0.28952 0.26221 -organisations -0.46117 -0.39225 -0.06822 -0.51591 0.22902 -1.2177 -0.31987 0.63996 0.35054 0.31043 -runs -0.21645 -0.38813 0.041702 -0.31783 -0.72282 -0.76663 -0.062083 1.1507 -0.32174 0.47044 -revealed -0.37847 -0.43619 -0.066684 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0.20409 0.29204 0.7641 -0.054436 -Three -0.07702 0.12594 0.047245 -0.40953 -0.59036 -0.93803 0.010323 1.1526 -0.17064 0.38934 -drug -0.085196 -0.30057 0.20713 0.13801 0.28813 -1.5948 -0.9975 0.5553 -0.15491 0.94394 -News -0.72148 -0.339 0.18774 -0.51116 -0.29723 -0.67515 -0.21364 1.3068 0.29644 -0.073595 -Mohammad -0.74696 -0.32097 0.0062055 -0.84965 -0.19747 -0.84452 0.31409 0.57672 0.14823 -0.25099 -manager -0.53305 -0.36268 -0.13203 -0.63561 0.2805 -1.4038 -0.3425 0.29703 0.44548 0.18854 -hotel -0.44287 0.29931 0.12265 -0.8696 -0.061178 -0.78978 -0.13151 0.71317 0.15076 0.16443 -Home 0.11713 -0.60078 -0.11339 -0.25769 0.055567 -1.1023 -0.38286 0.90047 -0.2208 0.87745 -conducted -0.41266 -0.49199 0.31454 -0.34903 -0.33211 -0.84388 -0.2136 0.70612 -0.033055 0.39631 -escalating -0.26 -0.41234 -0.20578 -0.44951 0.31689 -1.311 0.18937 0.961 0.1147 0.094468 -tensions -0.34793 -0.64909 -0.23763 -0.51862 0.46339 -1.498 -0.46812 0.49961 0.41237 0.4275 -connection -0.5137 -0.30837 -0.1689 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0.45664 0.34252 -No -0.3187 0.019957 0.62398 -0.45226 -0.23304 -1.0947 -0.070949 1.3142 -0.095764 -0.14399 -worked -0.26301 -0.80365 -0.11384 -0.54922 0.18937 -1.1993 -0.28206 0.58087 0.53203 0.56808 -Neville -0.41622 -0.45576 0.20309 -0.86753 0.16475 -0.982 -0.060814 -0.0062292 0.19353 0.48508 -injuring -0.42267 0.11041 -0.15743 -0.47741 0.035203 -0.96988 0.055841 1.5173 0.11088 0.096089 -Hospital -0.45166 -0.21991 0.14398 -0.45193 -0.1458 -0.96499 -0.28009 1.0434 0.060135 0.055641 -month. -0.36601 0.020446 -0.10012 -0.94306 0.074607 -0.71996 0.091965 0.71052 0.066801 0.4361 -batsmen -0.046606 -0.32616 0.078243 -0.71783 -0.35011 -0.73889 -0.29144 0.34662 -0.15648 1.0207 -publicly -0.63298 -0.29862 0.046287 -0.74439 -0.096301 -0.83908 0.028825 0.34709 0.15339 0.1537 -India's -0.54255 -0.1699 -0.065914 -0.70319 0.029993 -1.0021 0.32475 0.75355 0.37114 -0.054248 -Costello -0.88724 0.18324 -0.062381 -1.2001 0.13558 -0.83053 0.33953 0.42682 0.60612 -0.15922 -Gary -0.1991 -0.19674 -0.096646 -0.5141 -0.0035475 -1.142 0.10815 0.55165 0.13443 0.4849 -doesn't -0.43326 -0.12545 0.0091349 -0.95674 -0.39486 -0.82684 0.035955 -0.12445 -0.17041 0.60218 -separate -0.49317 0.20422 -0.2914 -0.80541 0.29058 -1.0825 -0.020538 0.84016 0.76471 0.2627 -Boucher -0.34939 -0.22825 -0.009609 -0.9743 -0.19672 -1.0737 0.63755 0.5291 0.17776 -0.095003 -extremists -0.54226 0.1639 -0.18533 -0.83279 0.084935 -1.0717 0.31051 0.99856 0.44026 0.038325 -people. -0.55054 0.14984 -0.14874 -1.0109 0.13209 -0.55478 -0.039663 0.70374 0.27481 0.49672 -adding -0.67667 -0.52855 -0.090197 -0.74736 0.35528 -1.0056 0.28167 1.1456 0.13387 -0.32671 -Qantas' -0.57351 -1.3188 -0.046802 -0.41482 0.64392 -1.4333 -0.22622 0.86065 0.67279 0.12993 -low -0.18381 -0.5961 0.50435 -0.2 -0.53993 -0.86452 -0.36596 0.39651 -0.38978 0.88781 -system -0.52094 0.19486 -0.087316 -0.88516 0.13972 -0.99476 0.23663 0.92767 0.54983 0.079045 -locked -0.60252 -0.23596 0.13864 -0.48436 0.042304 -1.0553 -0.039618 1.1225 0.58516 -0.095255 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0.47959 -0.42694 -Mohammed -0.86758 -0.36718 0.02311 -0.7925 -0.078869 -0.96044 0.22806 0.94134 0.53718 -0.45077 -it. -0.099291 -0.40108 0.15706 -0.67756 -0.54238 -0.71547 -0.43756 -0.47841 -0.46095 1.3084 -light -0.63822 -0.28321 -0.094705 -0.64198 -0.50652 -0.55571 -0.12489 1.3116 0.52003 0.1888 -Nablus -0.88266 0.0081448 0.18366 -0.76124 0.018092 -0.77392 0.041746 0.96767 0.15035 -0.19823 -Washington. -0.67114 -0.43833 0.049182 -0.67074 -0.07035 -0.86556 0.19483 0.83248 0.1033 -0.00048737 -speech -1.0751 -0.56063 0.3614 -0.68355 0.25371 -1.0455 0.24976 0.78202 0.39921 -0.38407 -Jenin -0.92294 -0.18635 0.20531 -0.70481 -0.18684 -0.81776 0.35616 1.3796 0.32968 -0.39223 -Park -0.51748 -0.69925 -0.03443 -0.6245 0.27169 -1.0174 -0.44275 0.63562 0.34834 0.26234 -pace -0.89576 -0.55671 0.29962 -1.1158 0.13385 -0.95443 1.1019 0.58859 0.75165 -0.47553 -intelligence -0.53441 -0.068276 -0.19353 -1.0845 0.1409 -1.0836 0.52502 0.41111 0.61268 -0.1351 -Saturday -0.25256 -0.22396 0.014874 -0.57191 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0.074068 -0.7113 0.38478 0.023557 -0.25413 0.30862 -House -0.52806 -0.18135 0.26993 -1.1003 0.094986 -0.86281 0.36131 -0.048165 -0.020116 0.1606 -proposals -0.36694 -0.28187 0.27781 -0.93572 -0.45139 -0.71488 0.33804 0.18409 -0.24178 0.39308 -Steve -0.27019 0.21753 -0.072046 -0.91778 -0.060812 -0.91587 0.52003 -0.040411 -0.2842 -0.072086 -powers -0.29463 -0.19813 0.28684 -1.014 0.080471 -0.81738 0.36285 -0.052792 -0.21976 0.16303 -helicopter -0.25687 -0.064613 0.045338 -0.90035 0.38695 -1.0113 0.43999 0.0056159 -0.22388 -0.028703 -total -0.19574 -0.19443 0.19962 -0.90192 0.12229 -0.94611 0.41304 0.18708 -0.24157 0.10976 -well, -0.24042 -0.0048114 -0.10428 -0.98395 -0.24487 -0.74945 0.50611 -0.11992 -0.48088 0.26573 -terror -0.37502 -0.19371 0.22344 -0.89512 0.26076 -0.94791 0.40102 -0.02156 -0.27957 -0.12665 -list -0.31649 -0.10805 0.088525 -0.87109 0.39583 -0.94694 0.56579 -0.020434 -0.30059 -0.091529 -wickets -0.11094 0.25547 -0.028393 -0.98382 0.37106 -0.98481 0.64935 0.016147 -0.50319 -0.18784 -confidence -0.38178 -0.29464 0.069465 -1.0372 0.24086 -0.91127 0.42344 -0.060514 -0.059409 0.12219 -post -0.071096 -0.1008 0.19798 -0.95918 -0.16612 -0.8353 0.488 0.29332 -0.38116 0.10752 -base -0.21322 -0.1371 0.27562 -1.0178 -0.35491 -0.63268 0.34969 0.21059 -0.25413 0.38995 -commander -0.45147 -0.16981 0.063563 -0.87107 0.21224 -1.0843 0.49199 -0.015571 -0.080977 -0.022907 -increase -0.15533 -0.07075 0.19984 -0.90473 0.1906 -0.91756 0.4335 0.14786 -0.24569 0.043626 -moved -0.19785 -0.14604 0.12517 -1.0153 0.57268 -1.129 0.28721 -0.077822 0.065845 -0.14377 -Rural 0.10639 -0.29489 0.43193 -0.96248 -0.42105 -0.75275 0.23308 0.4188 -0.44515 0.33611 -Highway -0.25074 -0.13236 0.077874 -0.98353 0.16993 -0.94955 0.51033 -0.038909 -0.21028 0.021032 -overall -0.21224 -0.16805 0.365 -0.90428 -0.059735 -0.94327 0.37135 0.30456 -0.23009 0.17049 -coming -0.29379 -0.19742 0.12593 -0.80605 -0.24166 -0.93986 0.44044 0.10291 -0.2298 0.12655 -Tony -0.39816 -0.23335 0.021897 -0.85312 0.39238 -1.0179 0.43609 0.026345 -0.12991 -0.16952 -time, -0.26195 -0.045255 0.054461 -0.91151 0.019257 -0.91574 0.43555 0.0052445 -0.21838 0.060998 -Perth. -0.10565 -0.0084844 0.14234 -1.0362 0.054377 -0.90548 0.46144 0.083715 -0.19048 0.058815 -rights -0.20597 -0.081837 0.23318 -0.95121 0.41999 -1.0853 0.54029 0.19495 -0.12341 -0.20142 -Pacific -0.36132 -0.22185 0.15185 -0.91572 0.11814 -0.98048 0.49792 0.085878 -0.12503 0.16658 -Simon -0.11685 -0.038896 0.079094 -0.84486 -0.31179 -0.8822 0.37843 0.17594 -0.24671 0.21485 -fellow -0.45967 -0.043865 0.13649 -0.96167 0.12351 -0.86724 0.40706 -0.046775 -0.1364 -0.08718 -force, -0.34743 -0.29381 0.050942 -0.95045 0.59266 -1.2173 0.36728 0.035491 -0.18104 -0.20573 -freeze -0.49273 -0.15795 0.18215 -0.92833 0.079104 -0.90847 0.41997 0.11126 -0.19785 -0.084013 -damaged 0.0012511 -0.030074 0.25001 -0.92128 -0.0029533 -0.96183 0.34022 0.3789 -0.33441 0.096431 -mean -0.4804 -0.16951 0.048943 -0.8598 -0.24383 -0.88889 0.42948 -0.0015202 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0.0096728 0.084306 -0.88581 0.18665 -1.0543 0.50127 0.21423 -0.37771 -0.1417 -places -0.28233 -0.016887 0.11459 -0.98382 0.16488 -0.99112 0.47978 -0.0063082 -0.24797 0.044266 -hearings -0.072585 -0.033269 0.033601 -0.92202 0.20151 -0.98647 0.45259 0.17178 -0.35125 -0.016036 -control. -0.068946 -0.18804 0.15451 -0.96441 0.13366 -0.85439 0.44594 0.25796 -0.29269 0.21424 -began -0.50454 -0.24755 0.12876 -0.91359 0.13738 -0.95903 0.47035 -0.066543 -0.18543 0.0047818 -hour 0.045793 0.092578 0.14008 -0.95575 -0.041559 -0.93366 0.37378 0.18275 -0.45589 -0.011432 -airport -0.092699 -0.1598 0.17511 -0.98575 0.21607 -0.82413 0.43506 0.063688 -0.17872 0.20286 -management -0.30659 -0.33859 0.14841 -0.85527 0.066927 -0.95052 0.4215 0.204 -0.13698 0.074181 -areas. -0.051095 -0.24853 0.46468 -1.0684 0.18378 -0.69016 0.43616 0.13457 -0.18965 0.33674 -confirm -0.43539 -0.32098 0.1932 -1.0501 0.14579 -0.792 0.37887 -0.013652 -0.033518 0.20015 -direct -0.082884 -0.18384 0.26525 -0.93763 0.065223 -0.92336 0.21136 0.23335 -0.21433 -0.011837 -crackdown -0.33707 -0.15978 0.19845 -0.96674 0.18365 -0.92571 0.43693 0.018279 -0.18878 0.032471 -everything -0.28963 -0.19187 0.12756 -0.86967 -0.39614 -0.72188 0.42214 0.060443 -0.25329 0.29818 -Laden, -0.92949 -0.26304 0.38363 -1.1598 0.41546 -0.88893 0.15614 -0.41996 0.010165 -0.21182 -March -0.38713 0.086237 0.15301 -0.86982 -0.33435 -0.78235 0.48544 0.094351 -0.31309 0.13725 -Attorney-General -0.25057 -0.19638 0.18944 -0.93629 -0.090629 -0.86499 0.42347 0.20342 -0.21998 0.22308 -Endeavour -0.22169 -0.092432 0.035077 -0.8808 0.029427 -0.86335 0.45579 0.044855 -0.27194 0.15782 -Pakistan's -0.92088 -0.49816 0.079493 -1.014 0.8061 -1.2665 0.45884 -0.23544 0.10661 -0.39567 -Ian -0.3708 -0.067236 0.096163 -0.93513 0.45945 -1.062 0.56056 -0.14612 -0.066785 -0.0086675 -Bank, -0.26307 -0.19509 0.17071 -1.0483 0.64624 -1.0057 0.50766 -0.13061 -0.14266 -0.067282 -space -0.24785 0.031023 -0.20854 -0.93185 0.16903 -0.94983 0.57514 0.011342 -0.29523 0.03631 -remains -0.27094 0.020081 0.089926 -0.99737 0.34499 -0.91512 0.50734 0.072577 -0.15759 -0.05245 -explosives -0.36031 -0.15429 0.078313 -0.92549 0.08738 -0.9409 0.45157 0.0516 -0.18237 0.033363 -east -0.19675 0.12544 0.10072 -1.019 0.81313 -1.0884 0.62149 0.11888 -0.29042 -0.42242 -25 0.11895 -0.056397 0.090409 -1.0446 0.10077 -0.95928 0.33355 0.070212 -0.30139 0.21796 -battle -0.27846 -0.070352 0.15079 -0.98463 -0.38279 -0.72243 0.37669 -0.068275 -0.2686 0.37715 -Jason -0.187 -0.015456 0.11623 -0.84792 0.19114 -1.0272 0.57475 0.18859 -0.29507 -0.12471 -Lockett -0.46609 -0.11666 0.14205 -0.89711 -0.25063 -0.81773 0.36452 0.064722 -0.31159 0.10607 -capital -0.22541 -0.12107 0.028604 -0.9452 0.57696 -1.1292 0.53787 -0.050462 -0.10325 -0.17973 -ahead -0.24124 -0.16768 0.13893 -0.91959 0.26864 -0.91944 0.44784 0.040978 -0.18457 0.11707 -Party -0.23333 -0.12322 0.061631 -0.94488 0.12722 -0.97368 0.53133 0.095747 -0.20035 0.10882 -didn't -0.37202 -0.12961 0.099155 -0.96571 0.32674 -1.1425 0.39862 -0.089292 -0.15754 -0.16824 -storms -0.13308 -0.07898 0.3656 -1.0861 -0.076813 -0.763 0.3458 0.080008 -0.2326 0.2837 -signed -0.2608 -0.26592 0.21975 -1.0783 0.43314 -0.98418 0.34998 -0.14913 -0.069144 -0.058183 -January -0.57483 -0.064061 -0.0063107 -0.88573 0.20769 -1.0344 0.50173 -0.12499 -0.15397 -0.094807 -hopes -0.073326 0.014969 0.3224 -1.0171 -0.37621 -0.62876 0.36393 0.094118 -0.30377 0.4029 -private -0.38831 -0.14378 0.084549 -0.86793 -0.35893 -0.8119 0.42237 0.17419 -0.30477 0.311 -suspended -0.39136 -0.18597 0.041544 -1.016 0.60437 -1.0746 0.45986 -0.19855 -0.054115 -0.17317 -Shaun -0.32093 0.16317 -0.096377 -0.8842 -0.11604 -0.91614 0.56229 -0.06852 -0.32679 0.12307 -payment -0.25827 -0.31973 0.22182 -0.92272 -0.039587 -0.92623 0.39454 0.29695 -0.13683 0.10222 -remaining -0.26889 -0.12486 0.17687 -0.96169 0.25128 -0.94744 0.46965 0.082772 -0.19431 -0.088614 -Harrison's -0.34397 -0.081245 0.11922 -1.0049 0.15445 -0.9469 0.45309 -0.040188 -0.12697 -0.017896 -wanted 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-1.0087 0.46712 0.021911 -0.038718 0.030982 -wake -0.093672 0.055472 -0.019713 -0.92111 -0.11022 -0.97104 0.41747 0.10263 -0.27675 0.12806 -airline's -0.22412 -0.12503 0.25227 -0.95896 -0.059966 -0.86024 0.36567 0.09931 -0.19185 0.11832 -secret -0.458 -0.081446 0.11812 -0.8207 -0.17783 -0.88348 0.52513 -0.024678 -0.096435 0.16607 -Ruddock -0.34777 -0.029505 0.024112 -0.80824 0.11575 -0.93498 0.55054 0.086513 -0.1592 0.066195 -happened -0.31539 -0.17737 0.071333 -0.97106 0.22044 -0.94565 0.44359 -0.12155 -0.070321 0.088699 -rise -0.22078 0.016361 0.085381 -0.98863 -0.15084 -0.74209 0.47434 -0.081044 -0.16355 0.23253 -Sharon's -0.61083 0.081367 -0.020081 -0.89849 0.25259 -1.1607 0.51477 -0.4268 0.058208 -0.059512 -strategic -0.31828 -0.19848 0.17038 -0.9227 0.442 -0.9679 0.50203 -0.0076138 -0.17986 0.068369 -keep -0.10788 0.1237 -0.035003 -0.9761 0.023099 -0.89026 0.50539 0.036025 -0.34625 0.13666 -minister -0.81028 -0.045458 -0.015941 -0.78384 0.2495 -1.1529 0.58248 -0.26326 0.068149 0.055324 -sea -0.46396 -0.26388 0.2936 -0.98634 0.18721 -0.75769 0.46366 -0.25523 0.073609 0.27053 -Ray -0.074281 0.020983 0.096088 -1.0812 0.33056 -0.99169 0.58882 0.044375 -0.27237 -0.017101 -visit -0.33539 0.0099953 -0.054178 -0.83477 0.19101 -1.1189 0.42853 -0.007317 -0.099517 -0.107 -Road -0.084933 0.036377 0.27574 -1.0054 0.31223 -1.025 0.5488 0.036382 -0.2599 -0.061547 -peacekeepers -0.34258 -0.21414 0.063549 -0.93751 0.19598 -0.98197 0.48825 0.0060761 -0.1863 0.087055 -fleeing -0.37017 -0.16615 0.032975 -0.90687 0.22861 -1.0308 0.37704 0.0089835 -0.22146 -0.16206 -claim -0.26934 -0.15062 0.2185 -0.92438 0.093081 -0.92261 0.46575 0.047964 -0.29593 0.061419 -community. -0.21125 -0.31741 0.27287 -0.93479 -0.087283 -0.80433 0.36473 0.28058 -0.22082 0.25785 -Europe -0.15524 -0.028875 0.12736 -0.87264 -0.29675 -0.82058 0.43141 0.14404 -0.3574 0.22175 -avoid -0.3159 -0.13116 0.092537 -0.91962 -0.14937 -0.86592 0.46207 0.038993 -0.22333 0.14456 -twice -0.19078 -0.0097596 -0.043195 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-0.19235 0.12134 -0.95219 -0.040241 -0.88972 0.45739 0.019493 -0.1236 0.22679 -clearly -0.26136 -0.092334 0.22652 -0.97505 0.044356 -0.87222 0.45145 -0.10398 -0.31415 0.071187 -related -0.15942 -0.098811 0.056044 -0.88749 0.39262 -1.0702 0.53376 0.13411 -0.21892 -0.090192 -grant -0.2401 -0.14062 0.23954 -0.9333 0.071514 -0.91665 0.45179 0.081185 -0.11462 0.067239 -Perth -0.11122 0.03106 0.056859 -1.0016 0.31666 -0.97628 0.5172 -0.039563 -0.18637 -0.090776 -ceremony -0.14673 -0.22008 0.19464 -0.8457 -0.14645 -0.84613 0.456 0.33116 -0.29061 0.20251 -read -0.42176 -0.10929 0.18953 -0.92471 -0.1598 -0.78806 0.49239 -0.010597 -0.19399 0.16819 -nice -0.23009 -0.043847 -0.044621 -0.85689 -0.28957 -0.8679 0.40117 0.060147 -0.23787 0.36475 -charges -0.13571 -0.20754 0.25891 -0.91337 0.020319 -0.92752 0.29293 0.10693 -0.18216 0.11836 -singles -0.32386 0.072627 0.089566 -1.0061 0.0050874 -0.99319 0.54827 -0.032294 -0.22585 -0.050169 -tough -0.51517 0.01907 -0.059756 -0.91604 0.012925 -0.99575 0.56298 -0.19045 -0.12457 0.042528 -pilot -0.17787 -0.21168 0.19167 -1.0186 0.092057 -0.87213 0.42648 0.12392 -0.22186 0.18636 -Interlaken -0.31132 -0.028992 0.12936 -0.77558 -0.010251 -0.95471 0.45878 0.085206 -0.32529 -0.035028 -program -0.3165 -0.194 0.043699 -0.90067 -0.09108 -0.88954 0.41511 0.01183 -0.21771 0.11704 -possibility -0.17417 -0.22635 0.20271 -1.0031 0.16762 -0.94624 0.34064 0.076236 -0.21006 0.0031932 -finding -0.30603 -0.036248 0.20905 -0.89751 -0.20761 -0.84353 0.45065 0.054111 -0.33882 0.0029376 -now, -0.14095 -0.023827 0.24233 -0.97745 -0.30549 -0.70309 0.36732 -0.0015611 -0.35641 0.29118 -tomorrow -0.088968 -0.047691 0.21455 -0.88194 -0.08835 -0.9363 0.43395 0.30085 -0.30457 0.15748 -unity -0.3308 -0.27777 0.24292 -0.88044 0.088659 -0.96242 0.39934 0.30319 -0.17101 0.09274 -volunteers -0.26174 -0.057436 0.089196 -0.96116 0.074692 -0.85164 0.4566 -0.0054521 -0.20545 0.028282 -Assa -0.28009 -0.15646 0.12637 -0.92162 0.1677 -0.92001 0.53836 0.060919 -0.24258 -0.024064 -created -0.080767 0.047301 0.21443 -0.88478 0.14305 -0.98661 0.42311 0.14744 -0.27115 -0.017732 -wall -0.099802 -0.014132 0.060633 -0.85707 -0.038637 -0.96233 0.37083 0.19281 -0.28115 0.16652 -coach -0.16856 -0.29836 0.23797 -0.90876 -0.11726 -0.84583 0.42414 0.1564 -0.25516 0.15827 -recovery -0.20675 -0.18212 0.19319 -0.89779 -0.064241 -0.85309 0.38424 0.17494 -0.21933 0.17171 -Switzerland -0.079992 -0.13598 0.18184 -0.92639 0.054323 -0.93894 0.37569 0.18704 -0.28502 0.12325 -enter -0.45322 -0.01919 0.18559 -0.79178 0.14156 -1.0558 0.60923 0.11858 -0.19179 -0.02854 -doubt -0.32574 0.053422 0.0062525 -0.9487 -0.018581 -0.97107 0.45578 -0.15671 -0.24103 -0.0065333 -cause -0.50551 -0.12227 0.088099 -1.0827 0.016891 -0.88276 0.37197 -0.12661 -0.062636 0.15792 -crowd -0.40782 -0.23396 0.12535 -1.0146 0.051628 -0.88484 0.3862 -0.14947 -0.070023 0.21455 -students -0.19614 -0.093446 0.18796 -1.0112 0.32153 -0.915 0.4746 0.072986 -0.18092 0.036884 -yachts -0.10043 -0.13598 0.28857 -0.93148 0.16356 -0.94541 0.56432 0.17456 -0.27281 0.16088 -mountain -0.5485 -0.088163 0.27787 -1.0609 0.43525 -0.9536 0.4235 -0.0033296 -0.087574 -0.17432 -oil -0.49401 0.027284 0.086111 -0.77788 -0.26105 -0.89366 0.44204 0.070293 -0.21487 -0.035968 -names -0.12431 -0.10413 0.14273 -0.95384 0.0011781 -0.79293 0.37917 -0.00022532 -0.25695 0.13455 -Eve 0.026754 0.033638 0.13392 -0.94275 -0.146 -0.70248 0.46788 0.14457 -0.42444 0.19181 -boats -0.0297 -0.14933 0.3111 -1.04 0.14434 -0.76292 0.42072 0.15491 -0.32255 0.20952 -Philip -0.19464 -0.10752 0.22174 -0.94747 0.077592 -0.9701 0.41533 0.13309 -0.25159 -0.020048 -While -0.32228 -0.12627 0.1897 -0.89911 0.094805 -0.92193 0.48529 0.14728 -0.31592 0.074455 -property -0.24023 -0.26967 0.17315 -0.96327 -0.46607 -0.69738 0.31227 0.11607 -0.24229 0.4851 -River. -0.2681 -0.16975 0.18522 -0.90112 -0.08442 -0.99434 0.40489 0.13814 -0.1938 0.15909 -acting -0.53013 -0.14158 -0.054806 -0.86516 -0.18841 -0.89288 0.42395 0.0077935 -0.24085 -0.023856 -attacks, -0.26184 -0.064943 0.076111 -1.135 1.1445 -1.2878 0.43577 -0.25375 -0.021592 -0.66229 -80 0.19687 -0.0052728 0.11849 -0.99997 -0.26229 -0.74572 0.24241 0.16065 -0.50429 0.38195 -them," -0.12137 -0.21374 0.13169 -1.0482 -0.045658 -0.7064 0.35385 -0.059479 -0.23442 0.25225 -verdict -0.37523 -0.14589 0.21297 -0.90034 0.0082628 -0.88882 0.44354 -0.023509 -0.11883 0.14185 -together -0.41599 -0.11771 0.11297 -0.89298 -0.35122 -0.78593 0.44979 -0.018914 -0.2029 0.32439 -apparently -0.28669 -0.14803 0.16608 -0.90586 -0.11703 -0.78704 0.45045 0.14256 -0.27331 0.16938 -aboard -0.22315 -0.15286 0.22083 -0.9105 0.11552 -0.92815 0.47306 0.053682 -0.17567 0.17077 -area, 0.060098 -0.28676 0.48758 -1.1426 -0.19647 -0.58428 0.30434 0.084059 -0.22395 0.62962 -affected -0.31576 -0.14433 0.15533 -0.90465 0.35061 -1.0104 0.42994 0.02733 -0.076688 -0.10059 -reveal -0.29761 -0.14128 0.13282 -0.90104 -0.13577 -0.93424 0.41919 0.13418 -0.17131 0.14438 -Firefighters -0.14621 -0.1608 0.39446 -1.0887 0.32588 -0.87854 0.47296 0.21558 -0.23373 0.026986 -squad -0.11952 0.19318 0.11819 -1.0138 0.35206 -0.94827 0.57597 0.016621 -0.25446 -0.094914 -swept -0.254 -0.38474 0.3558 -0.89072 -0.2463 -0.79614 0.21236 0.2282 -0.14143 0.40372 -played -0.12248 -0.10612 0.044413 -0.99036 0.062518 -0.93714 0.40971 0.03221 -0.22713 0.076656 -agreed -0.47498 -0.17392 0.11952 -0.93832 0.12854 -1.0205 0.44601 0.026194 -0.083695 -0.075291 -hope -0.20079 -0.22468 0.37713 -1.0259 -0.34884 -0.56551 0.30759 0.068423 -0.3139 0.42521 -Hicks, -0.49856 -0.19744 0.00187 -0.99613 0.52396 -1.0283 0.50948 -0.19591 0.031717 -0.14309 -ready -0.30419 -0.20335 0.17166 -0.85772 -0.46007 -0.75412 0.38355 0.1726 -0.12917 0.40266 -department -0.29198 -0.26417 0.19732 -0.83206 0.12443 -1.0298 0.50087 0.37477 -0.10588 -0.0049605 -doubles -0.18303 0.085722 0.041611 -0.91097 -0.12315 -0.87427 0.58882 0.046346 -0.31335 0.081859 -Gillespie -0.21623 0.065488 0.036796 -0.91373 0.032026 -0.98446 0.523 0.035139 -0.25906 0.014605 -scored -0.17742 0.027689 0.062962 -0.92025 0.35127 -1.0697 0.48265 0.086444 -0.25782 -0.10568 -conflict -0.10585 -0.21113 0.17809 -0.99384 0.22581 -0.88548 0.41597 0.18941 -0.15521 0.13629 -dropped -0.32043 -0.087391 0.11451 -0.94564 0.29418 -1.0107 0.48245 0.031455 -0.16764 -0.093651 -years, -0.0011375 -0.20937 0.11103 -0.99005 0.27352 -0.91087 0.54879 0.21073 -0.28111 0.15536 -Fatah -0.42844 -0.21625 0.049941 -0.90511 0.30765 -1.0389 0.42033 -0.096833 -0.06178 -0.086817 -Friedli -0.31514 -0.2202 0.13265 -0.9082 0.12679 -0.93712 0.36831 0.074354 -0.084924 0.047795 -old -0.69719 -0.18566 0.070685 -1.0067 0.10258 -0.97605 0.44238 -0.1796 -0.052982 0.089902 -Transport -0.15508 -0.11403 0.15107 -0.92124 0.092459 -0.8699 0.4467 0.1271 -0.29248 0.15401 -agency -0.30065 -0.21441 0.035038 -0.91231 0.56437 -1.0848 0.57941 0.034436 -0.11448 -0.15499 -follows -0.017571 0.10862 0.0052477 -0.90609 0.073521 -1.0277 0.5345 0.17643 -0.28208 0.019561 -streets -0.16264 -0.015076 0.17619 -0.93528 0.30503 -0.97626 0.52447 0.13421 -0.26039 0.029433 -debt -0.29361 0.01649 0.11836 -0.9269 -0.1014 -0.8797 0.37439 -0.051927 -0.20218 0.14944 -factions -0.66215 -0.3437 0.0784 -0.91268 0.086813 -0.84447 0.43038 0.10023 -0.11111 0.12388 -dozens -0.020118 -0.041503 -0.0025768 -0.944 0.048652 -0.88489 0.45687 0.10902 -0.31275 0.12495 -Hundreds -0.18794 -0.071425 0.086088 -1.0112 0.47302 -1.0115 0.48967 0.04175 -0.17609 -0.1515 -capital, -0.12553 -0.055046 0.031502 -1.019 0.64319 -1.1797 0.51824 0.0011017 -0.14031 -0.23638 -fierce -0.32886 -0.061441 -0.0085193 -0.97711 0.18292 -0.86985 0.49664 -0.058466 -0.14711 0.11091 -Sunday -0.2073 -0.0074925 0.04898 -0.92262 0.16386 -1.0042 0.49381 -0.054376 -0.23898 -0.070552 -2001 -0.34211 -0.11376 0.19554 -0.89605 0.34288 -0.93222 0.43281 0.0060886 -0.18179 -0.055397 -attempting -0.20869 -0.03348 -0.12391 -0.94975 0.37949 -1.0764 0.48798 -0.10882 -0.1925 -0.18612 -races -0.012184 0.16111 0.060192 -0.99748 0.41683 -1.0134 0.55748 0.12327 -0.26316 -0.13532 -prior -0.39419 -0.24487 0.17659 -0.9468 0.046177 -0.91649 0.39508 -0.010464 -0.17617 0.29622 -Japanese -0.24729 -0.2236 0.2223 -0.88269 0.19642 -0.9013 0.48408 0.14182 -0.19541 -0.016586 -domestic -0.090836 -0.11496 0.22062 -0.94755 -0.021954 -0.86699 0.48926 0.26807 -0.34971 0.16311 -Internet -0.44494 -0.081498 0.22393 -0.86936 0.25624 -1.0817 0.53033 0.019088 -0.15582 -0.081715 -spread -0.34286 0.0085029 0.12235 -0.96069 -0.014924 -0.85794 0.43675 -0.0030413 -0.14867 0.16879 -create -0.16881 0.0015007 0.25886 -0.89012 -0.22962 -0.83982 0.39156 0.25736 -0.3609 0.21564 -playing -0.30897 -0.044987 -0.018034 -0.89053 -0.18541 -0.96471 0.48865 -0.052599 -0.31886 -0.044458 -growing 0.019896 -0.049953 0.18254 -0.98348 -0.16335 -0.84057 0.47503 0.18023 -0.3839 0.12714 -scheduled -0.3439 -0.11101 0.15367 -0.98378 0.19435 -1.0042 0.41828 0.024073 -0.067868 -0.007766 -factory -0.45105 -0.087521 0.030059 -0.97854 -0.027822 -1.0114 0.44714 -0.057401 -0.21847 0.092436 -knowledge -0.3498 -0.13386 0.19503 -0.9762 0.07868 -0.96534 0.3975 -0.048246 -0.17176 0.031962 -save -0.38017 -0.23925 0.33928 -0.94021 -0.35619 -0.73018 0.26854 -0.049871 -0.066799 0.41109 -holiday -0.17117 0.021268 0.12256 -0.95257 0.54454 -0.99889 0.51952 0.066868 -0.25515 -0.20265 -Timor -0.086932 -0.14251 0.27041 -0.80165 0.1293 -0.96345 0.48208 0.44571 -0.29487 0.001174 -Thursday -0.16804 0.032178 0.17198 -0.89441 0.064809 -0.94998 0.5231 0.14831 -0.32066 -0.046057 -recent -0.14141 -0.21349 0.34583 -0.80142 0.047077 -0.96236 0.44747 0.59265 -0.15062 0.12291 -revealed -0.311 -0.1451 0.11148 -0.9362 0.070507 -1.0092 0.43316 0.023024 -0.12374 0.015503 -rain -0.71506 0.070684 0.009715 -0.93109 0.32285 -0.98704 0.52257 -0.17777 -0.1588 -0.21132 -Professor -0.34146 -0.047432 0.047709 -0.85468 -0.34078 -0.80689 0.38297 0.011803 -0.29741 0.29715 -"But -0.23533 -0.072159 0.11926 -0.96619 -0.25722 -0.77987 0.46747 -0.052188 -0.22124 0.32704 -statement. -0.25781 -0.23333 0.21852 -1.0081 0.55942 -1.0465 0.4925 -0.026883 -0.06044 -0.095841 -Solomon -0.1112 -0.17747 0.30167 -0.94309 0.1185 -0.95132 0.39373 0.2722 -0.16205 0.048385 -organisations -0.31664 -0.15481 0.086478 -0.91277 0.24868 -0.91472 0.51023 0.1276 -0.26093 0.067923 -runs -0.081029 0.15482 0.10812 -0.95525 0.16914 -0.87168 0.50043 0.24514 -0.40289 -0.089365 -respond -0.12951 -0.13325 0.25617 -0.99682 -0.036371 -0.84081 0.34943 0.22771 -0.22782 0.15075 -Michael -0.27256 -0.1403 0.14789 -0.84897 0.11625 -0.89961 0.44581 -0.021928 -0.093597 0.08034 -When -0.3826 -0.067863 0.1181 -0.95835 -0.24689 -0.80123 0.4205 -0.12366 -0.28273 0.26885 -40 -0.080944 -0.030479 0.48167 -1.0393 -0.23531 -0.65996 0.23723 0.1509 -0.12914 0.17171 -Hayden -0.26217 0.13508 0.056591 -1.0181 0.060048 -0.92058 0.46848 -0.06749 -0.26086 -0.062866 -attack. -0.2187 -0.054543 0.15759 -1.1832 0.91744 -1.218 0.35545 -0.205 -0.083368 -0.48719 -Earlier -0.35822 0.034622 0.045342 -0.84545 0.2501 -1.0562 0.53407 -0.14539 -0.2124 -0.041862 -Indonesia -0.13118 0.068667 0.079238 -0.90011 -0.074519 -0.92243 0.52518 0.13945 -0.25327 0.099072 -Sarah -0.29148 -0.083626 0.10898 -0.96221 0.095324 -0.90043 0.48371 0.16895 -0.13041 0.13251 -detain -0.35919 -0.070197 0.25744 -0.8393 0.011666 -0.98708 0.45426 0.19846 -0.17789 0.032611 -Neil -0.4231 0.11533 0.060575 -0.94699 -0.31259 -0.88055 0.5079 0.012363 -0.18818 0.047914 -states 0.035746 -0.23303 0.46542 -1.061 0.50206 -0.87712 0.40336 0.011969 -0.23725 0.047316 -4,000 -0.062591 0.016686 -0.018896 -0.91001 -0.16483 -0.83083 0.41536 0.25611 -0.34508 0.053079 -things -0.051032 -0.15411 0.11416 -0.91539 -0.13999 -0.74606 0.36922 0.042876 -0.36282 0.21292 -toll -0.051031 -0.074063 -0.050378 -0.81443 -0.16458 -0.86624 0.50923 0.13427 -0.31809 0.16723 -you're -0.059447 -0.12139 0.1556 -1.0472 -0.35454 -0.73766 0.27145 0.065707 -0.33578 0.33058 -felt -0.43935 -0.029357 0.056699 -1.0074 -0.012995 -0.77477 0.40277 -0.15507 -0.20878 0.065392 -deployed 0.053162 -0.1647 0.22864 -0.97735 0.35141 -0.99622 0.42883 0.28513 -0.2113 0.045679 -Hamas, -0.33651 -0.27008 0.13846 -0.95793 0.30384 -0.96349 0.42221 0.066626 -0.14882 0.029652 -gun -0.19859 -0.12881 -0.085064 -0.77327 -0.15347 -1.0126 0.51969 -0.022194 -0.37624 -0.040505 -Senior -0.22561 -0.12378 0.17376 -0.97379 0.44761 -0.99742 0.52638 -0.025954 -0.082844 -0.089523 -plan -0.40868 -0.099606 0.13781 -0.87832 0.010708 -0.91702 0.48558 -0.034796 -0.17617 0.13689 -elected -0.13135 0.04798 0.14718 -0.88991 0.044746 -0.96795 0.40639 0.12036 -0.23581 -0.0068139 -government, -0.27946 -0.2585 0.1293 -0.85924 0.25008 -1.0282 0.60002 0.2802 -0.16703 0.092715 -north. 0.11999 0.082092 0.12268 -1.0197 0.096272 -0.83958 0.56722 0.2645 -0.31074 0.18826 -tailenders -0.37816 -0.044353 -0.0033328 -0.92389 0.26644 -1.0267 0.46643 -0.02207 -0.17795 -0.20222 -B-52 -0.33645 -0.14164 -0.034415 -1.0395 0.44756 -0.93329 0.52896 -0.1543 -0.19566 -0.048659 -advice -0.019011 -0.080755 0.13782 -1.0027 -0.14265 -0.75955 0.37166 0.25948 -0.27782 0.3537 -continues -0.31877 -0.08533 0.23952 -1.0301 0.1736 -0.92963 0.4279 0.22104 -0.16045 0.032999 -Lording -0.44379 -0.31877 -0.02095 -0.89717 0.27762 -1.0258 0.49945 -0.15816 -0.072751 -0.17291 -body -0.27807 -0.043922 -0.064788 -0.85849 0.4291 -1.0043 0.5301 -0.15222 -0.14431 -0.10526 -died. -0.28004 -0.15246 -0.050408 -0.89943 0.25872 -1.1047 0.51751 0.027965 -0.16932 0.011267 -Melbourne, -0.16467 -0.0016058 0.15283 -0.917 -0.25817 -0.83193 0.39381 0.033862 -0.38796 0.1885 -activity -0.4133 -0.23608 0.042298 -0.90558 0.024862 -0.93733 0.37843 0.10265 -0.13713 0.092931 -Krishna -0.37526 -0.19429 0.26769 -0.98535 0.13161 -0.93223 0.42722 -0.041666 -0.18585 0.028535 -crossed -0.070605 -0.052971 0.25836 -1.0456 0.26535 -1.0064 0.47807 0.18456 -0.31118 -0.08613 -described -0.21603 -0.1074 0.13553 -0.93613 0.040677 -0.93754 0.39861 0.10401 -0.22784 0.060417 -suffered -0.24294 -0.18708 0.019853 -0.95508 0.11403 -0.92028 0.46586 -0.0031084 -0.14647 0.12232 -500 -0.10623 0.18396 -0.090181 -1.01 0.1596 -0.86083 0.49228 0.10059 -0.22581 -0.014477 -militants. -0.45571 -0.19868 0.017953 -0.93778 0.71889 -1.1236 0.51809 -0.12618 -0.058003 -0.21578 -rescue -0.26812 -0.02935 0.10839 -0.92493 -0.17213 -0.96675 0.37002 0.15285 -0.25694 0.13041 -walk -0.22773 0.038052 -0.11351 -0.80286 0.32745 -1.1318 0.54419 0.0089137 -0.22752 -0.081451 -That -0.1364 -0.029955 0.15664 -0.88105 -0.49632 -0.69868 0.40827 0.12486 -0.43356 0.32152 -diplomatic -0.25385 -0.12728 0.056069 -0.94035 0.0083836 -0.90342 0.41262 -0.015823 -0.21035 0.12273 -directors -0.077749 -0.14422 0.21524 -0.95906 -0.036602 -0.91827 0.31253 0.2058 -0.25 0.12023 -concern -0.30573 -0.29248 0.31705 -0.99877 0.075426 -0.84557 0.39936 0.22679 -0.052918 0.24857 -Ricky -0.53879 -0.0035531 0.062384 -1.0496 0.017301 -0.8555 0.45923 -0.17582 -0.26331 -0.021441 -attacking -0.19124 -0.011663 0.046328 -1.0701 0.61407 -1.1884 0.46141 -0.13146 -0.16939 -0.36588 -handed -0.32916 -0.28368 0.13324 -1.0037 0.43082 -1.022 0.45323 -0.087054 -0.061207 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-0.8914 0.29613 -1.0714 0.49386 -0.16486 0.15936 0.069926 -Pakistan. -0.862 -0.55687 0.208 -1.0142 0.66765 -1.1684 0.38819 -0.14924 0.07302 -0.32486 -blaze -0.45721 -0.094917 0.039373 -1.0435 0.53644 -1.008 0.48946 -0.11726 -0.058373 -0.1909 -form -0.34916 -0.017953 -0.015494 -0.90177 0.14814 -1.154 0.44586 0.098764 -0.18015 -0.20346 -showed -0.29118 -0.22855 0.27023 -1.0458 0.11953 -0.83314 0.3252 -0.025419 -0.054188 0.13709 -field -0.18158 -0.17807 0.31801 -0.99627 -0.10492 -0.7835 0.45081 0.068907 -0.17304 0.29566 -period -0.24461 -0.075453 -0.0059673 -0.87738 0.42241 -1.0123 0.52005 0.092256 -0.21545 -0.044755 -action. -0.65352 -0.39539 0.19935 -0.9616 -0.052078 -0.81066 0.31832 0.10863 -0.05101 0.15773 -threatened -0.28797 -0.095794 0.1904 -0.98768 0.20751 -0.9698 0.3816 -0.079589 -0.1922 0.073733 -game -0.19809 -0.0086769 -0.066625 -0.93597 -0.36786 -0.73464 0.42883 0.018447 -0.48203 0.24025 -open -0.30496 0.025563 -0.024002 -0.92346 0.24914 -1.0943 0.49335 -0.27707 -0.17528 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0.51773 0.15336 -0.12766 -0.21983 -Adelaide. -0.025989 0.085196 0.11826 -0.84865 -0.073398 -0.90715 0.44745 0.21695 -0.47422 -0.03469 -month, -0.12487 -0.077932 0.027586 -0.85013 0.3364 -1.0947 0.47631 0.14132 -0.23213 -0.13515 -it. -0.16097 -0.0027678 0.30021 -0.9826 -0.52734 -0.59736 0.39112 0.12545 -0.3951 0.3784 -well," -0.35334 -0.018732 -0.051846 -0.95604 -0.12878 -0.79389 0.45722 -0.23372 -0.32752 0.14005 -hoped -0.37201 -0.2701 0.36301 -1.0897 0.35876 -0.81422 0.26953 -0.10798 -0.030669 0.067099 -"That -0.16934 -0.0016635 0.11367 -0.94955 -0.065852 -0.852 0.42582 -0.0042309 -0.21283 0.10859 -voice -0.21974 -0.20248 0.1113 -0.92958 -0.18162 -0.78683 0.37163 0.098087 -0.13725 0.30602 -Strip, -0.17654 -0.14265 0.1576 -0.99836 0.57331 -1.0703 0.49526 0.014806 -0.078684 -0.062875 -rival -0.18643 -0.082184 0.053126 -0.83016 -0.29496 -0.8513 0.42921 0.22511 -0.28777 0.25562 -documents -0.037684 -0.091584 0.076233 -0.89734 0.43422 -0.98438 0.59961 0.22531 -0.33378 -0.066055 -conducted 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0.42942 0.21574 -0.27447 -0.018415 -Hospital -0.069514 -0.17098 0.17142 -0.91389 0.21038 -0.94193 0.48858 0.10507 -0.16306 0.061756 -worked -0.3658 -0.18651 0.16302 -1.0202 0.20734 -0.93103 0.45036 -0.045388 0.011548 0.033307 -No -0.06644 0.047094 -0.0072464 -0.97361 0.35235 -1.0128 0.65638 0.1251 -0.27042 0.074347 -Home -0.19708 -0.16752 0.23055 -0.91908 -0.28299 -0.78355 0.37316 0.28967 -0.33267 0.1164 -finally -0.2225 0.023568 0.042913 -0.88631 -0.21756 -0.87086 0.41333 0.12565 -0.24964 0.24162 -system -0.32424 -0.11447 0.022804 -0.86424 0.41032 -1.087 0.59199 0.05113 -0.16408 -0.029677 -low -0.42441 0.058064 0.24926 -0.94843 -0.41338 -0.72884 0.31984 -0.049308 -0.14088 0.27766 -people. -0.12527 -0.067296 0.039527 -0.90466 0.085485 -0.90647 0.50767 0.094092 -0.33578 0.091639 -tell -0.32064 -0.16263 0.1469 -1.0216 -0.58306 -0.65198 0.35841 0.054218 -0.3 0.40143 -separate -0.27425 -0.12546 -0.021667 -0.87955 0.1923 -0.97197 0.5305 0.075712 -0.20981 0.095925 -Rumsfeld -0.20524 -0.15714 0.14062 -1.0314 0.0023828 -0.79159 0.43273 -0.032572 -0.2126 0.21579 -Timor's -0.14788 -0.19963 0.31471 -0.94354 0.028989 -0.85614 0.43924 0.35624 -0.2247 0.097835 -assisting -0.42728 -0.074091 -0.084522 -0.8508 0.32864 -1.0608 0.50128 -0.053758 -0.16812 -0.19792 -regional -0.38529 -0.035004 0.15997 -0.77454 0.25105 -1.0563 0.52642 0.19416 -0.086911 -0.093535 -real -0.36399 -0.25527 0.23231 -0.85246 -0.43477 -0.81053 0.37251 0.24543 -0.21747 0.35087 -travelled -0.30306 -0.11726 0.12499 -0.88953 0.034301 -0.99596 0.39319 0.059334 -0.14803 0.070926 -personnel -0.24849 -0.16882 0.1015 -1.0017 0.31558 -1.0015 0.45331 -0.048916 -0.13477 0.066244 -ability -0.27173 -0.19358 0.049492 -0.99239 0.61827 -1.1174 0.4664 -0.022641 -0.1291 -0.19477 -shopping -0.084733 0.028369 0.081703 -0.91519 0.18997 -0.90579 0.52277 0.094239 -0.38535 -0.065638 -offered -0.28105 -0.32388 0.079595 -0.95111 0.095603 -0.92738 0.43216 0.026027 -0.059018 0.13014 -well." -0.31512 -0.066211 0.095542 -0.90755 -0.52851 -0.67737 0.41563 0.076345 -0.40165 0.30207 -republic -0.29533 -0.10822 0.024253 -0.89019 -0.13072 -0.883 0.40501 0.12325 -0.22216 0.1724 -tragedy. -0.2544 -0.075438 0.20771 -0.87111 -0.17515 -0.9306 0.40912 0.14662 -0.34255 0.01546 -Sharon, -0.54722 0.14504 -0.09222 -0.86272 0.29718 -1.2241 0.55062 -0.51179 0.04589 -0.062425 -waiting -0.33496 -0.061463 -0.06793 -0.86166 -0.10127 -0.94691 0.42146 0.071077 -0.2626 -0.007498 -Health -0.12165 -0.15446 0.21313 -0.90986 -0.06678 -0.81807 0.35183 0.025407 -0.17704 0.19281 -track -0.33687 -0.28419 0.27461 -0.85502 0.094422 -0.93155 0.43375 0.019345 -0.14151 0.15344 -problems -0.23635 -0.057152 0.13733 -0.96691 -0.28434 -0.78862 0.46773 0.060729 -0.28674 0.3308 -seriously -0.29599 -0.20022 0.10944 -0.90622 0.39272 -1.011 0.52848 -0.049441 -0.11413 0.075811 -Illawarra -0.12312 -0.10931 0.231 -0.94276 -0.040194 -0.88484 0.44864 0.1819 -0.33259 0.13047 -Virgin -0.43737 -0.059672 0.022511 -0.87982 0.25952 -1.0294 0.55749 0.038893 -0.22717 -0.13892 -television -0.40775 -0.15439 0.0078305 -0.89733 0.12706 -1.0102 0.4374 -0.1198 -0.047093 0.11185 -hours, -0.16732 -0.082988 0.24497 -1.0274 -0.028222 -0.85568 0.27882 0.06291 -0.27767 0.13924 -south-west -0.032608 -0.049815 0.19882 -1.0478 0.52558 -0.92695 0.59419 0.12515 -0.38462 0.03899 -Mohammed -0.43229 -0.12045 0.13958 -0.96673 0.4586 -1.025 0.45631 -0.13927 -0.044081 -0.13244 -Washington. -0.28267 0.034559 0.095128 -1.0139 0.13886 -0.91053 0.44615 0.050118 -0.28358 -0.058168 -"His -0.40711 -0.089346 0.022946 -0.9222 -0.045735 -0.92028 0.47429 -0.021138 -0.25581 -0.011387 -landed -0.22248 -0.17765 0.11213 -0.9833 0.64399 -1.1873 0.47176 -0.053128 -0.032894 -0.17665 -individuals -0.30371 -0.097623 0.12036 -0.86899 -0.12237 -0.91775 0.39886 0.080975 -0.15119 0.13273 -resistance -0.61039 -0.31171 0.15754 -0.96852 0.24458 -0.94637 0.43058 0.0090633 -0.098713 -0.037902 -Mayor -0.27184 -0.093265 0.19896 -0.9807 -0.039304 -0.88642 0.3541 0.054288 -0.12868 0.012767 -criminal -0.2211 -0.12247 0.23261 -0.85552 0.067805 -0.98377 0.47484 0.19956 -0.25257 0.014181 -representation -0.37881 -0.21401 0.17627 -0.88162 0.080312 -0.96165 0.42385 0.2562 -0.16246 0.053188 -His -0.4522 -0.28315 0.15234 -0.87023 0.22684 -0.91788 0.45055 -0.13313 -0.10547 0.12085 -territories -0.32634 -0.23142 0.095182 -0.9543 0.14923 -0.91172 0.4212 -0.0087514 -0.13068 0.099214 -observers -0.28079 -0.11129 0.12121 -0.97759 0.33095 -0.95931 0.50448 -0.056571 -0.13655 0.021243 -Owen -0.40788 -0.0014133 -0.11052 -0.94474 0.53525 -1.2078 0.64991 -0.091348 -0.11122 -0.25833 -sending -0.51309 -0.1859 0.084034 -0.88158 0.019714 -0.9883 0.49863 -0.031452 -0.19263 -0.072645 -26 -0.0006202 0.10724 -0.022284 -0.82976 0.068064 -0.98822 0.51966 0.14629 -0.40722 -0.11155 -Sector -0.31445 -0.17617 0.15852 -0.88677 -0.069458 -0.83895 0.38908 0.031061 -0.23409 0.11124 -embassy -0.26549 -0.017364 0.21213 -0.90806 0.069404 -0.94696 0.49348 0.20868 -0.21777 -0.020424 -shuttle -0.20957 0.014762 0.13739 -1.0199 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0.056346 -0.1973 0.097429 -months, -0.10046 -0.069789 0.10003 -0.9277 0.24753 -1.0747 0.44745 0.18699 -0.26068 -0.08329 -murder -0.42637 -0.24334 0.12645 -0.78126 0.10648 -1.0982 0.47767 0.159 -0.11915 0.041442 -Whiting -0.25833 -0.023759 -0.047414 -0.88992 0.080393 -0.95249 0.46532 0.0084415 -0.28506 -0.07071 -convicted -0.087382 -0.14507 0.21741 -0.97476 0.12423 -0.92747 0.37899 0.18639 -0.23692 0.10655 -positions -0.45257 -0.27477 0.085941 -0.85921 0.18458 -0.953 0.46735 0.14453 -0.21068 0.086495 -ethnic -0.3314 -0.013393 0.139 -0.9376 0.002369 -0.81834 0.44175 -0.056331 -0.19796 0.11059 -About -0.1777 -0.16339 0.3381 -0.82766 0.019878 -0.88386 0.32885 0.3276 -0.32283 0.036568 -success -0.27618 -0.13046 0.098235 -0.9085 0.16715 -0.91378 0.51658 0.01549 -0.19144 0.0049447 -Matthew -0.40921 -0.082844 -0.0025165 -0.93614 0.19289 -0.94652 0.47355 -0.17839 -0.17524 -0.056866 -adding -0.23709 -0.1576 0.04681 -0.92208 0.30419 -1.0458 0.53208 0.030716 -0.21109 -0.13296 -afternoon, -0.54182 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0.5294 0.071495 -0.28827 0.068427 -trade -0.15353 -0.076031 0.26592 -0.97467 -0.03202 -0.86541 0.34478 0.050255 -0.34897 0.12444 -lack -0.28125 -0.049111 0.12849 -0.858 0.024126 -0.87174 0.43113 0.075511 -0.28163 0.027479 -document -0.097176 -0.17444 0.14257 -0.85602 0.30242 -0.96306 0.56906 0.25605 -0.2711 -0.016344 -explanation -0.38645 -0.18002 0.055084 -0.86557 0.18408 -0.97789 0.48657 0.18924 -0.18177 0.083669 -Sultan -0.41645 -0.16765 0.21599 -1.0014 0.30501 -1.0955 0.46808 0.10096 -0.14567 -0.054145 -reduced -0.21167 -0.080213 0.11708 -0.94597 -0.17172 -0.89563 0.39573 0.26074 -0.2101 0.15443 -violent -0.11532 -0.13904 0.23495 -0.86285 0.3395 -1.0167 0.46774 0.32004 -0.18438 -0.10924 -understanding -0.36627 -0.18224 0.14645 -0.93409 0.068406 -0.91532 0.43653 0.039594 -0.22393 -0.033249 -farm -0.2685 -0.094387 0.36153 -1.0836 0.22668 -0.88753 0.28388 0.019861 -0.054735 -0.082909 -Lord -0.44769 -0.071266 -0.13184 -0.99477 0.64386 -1.1481 0.62058 -0.35432 0.088442 -0.19185 -nearby 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-0.9244 0.088088 -0.93542 0.44833 0.070584 -0.22506 -0.058827 -normal -0.33125 -0.08666 0.21988 -0.91425 -0.26755 -0.74247 0.3954 0.059246 -0.2519 0.29009 -Amin -0.67553 -0.21073 0.21461 -1.0165 -0.20907 -0.78149 0.34416 -0.081891 -0.18986 0.18107 -Adam -0.44946 -0.048925 0.12921 -0.91341 -0.258 -0.85795 0.404 0.10336 -0.19082 0.18 -crashed -0.28983 -0.19277 0.28008 -1.0251 0.36846 -1.0111 0.34961 0.023464 -0.10933 -0.062239 -natural -0.07196 -0.27232 0.38912 -0.98727 -0.1257 -0.81443 0.29644 0.32534 -0.29026 0.20611 -begin -0.67268 -0.064473 -0.036203 -1.0396 0.39318 -1.0634 0.52304 -0.39897 -0.10272 -0.20808 -Up -0.12987 -0.31596 0.37794 -0.92846 0.34058 -0.87118 0.55491 0.18954 -0.065122 0.20761 -celebrations -0.35697 -0.24729 0.14783 -0.91635 0.17408 -0.84252 0.47401 0.19342 -0.26351 0.14232 -reject -0.36215 -0.083687 0.019168 -0.88026 -0.34821 -0.80624 0.35637 0.060272 -0.22834 0.17106 -options -0.43006 -0.32979 0.099825 -0.8432 0.28397 -0.94099 0.45111 0.18693 -0.12492 0.077929 -single -0.36768 -0.0077289 0.12405 -0.89668 -0.13589 -0.91868 0.43771 0.023178 -0.19012 0.020062 -handling -0.19383 -0.086433 0.073078 -0.84088 -0.022429 -0.96951 0.52248 0.18913 -0.26988 -0.062818 -match. -0.10931 0.18295 -0.0038214 -0.83137 -0.24983 -0.95995 0.53487 0.20493 -0.49541 -0.1036 -summit -0.31419 0.0098797 0.035068 -0.96421 0.14451 -1.0258 0.50231 -0.018111 -0.23626 -0.029974 -talks. -0.35229 -0.15515 0.07993 -0.99309 0.11575 -0.89788 0.43005 0.018662 -0.17911 0.030351 -All -0.82992 -0.12558 0.23378 -0.8514 -0.078518 -0.93991 0.46519 -0.30953 0.013705 0.18509 -settlement -0.34912 -0.2379 0.10761 -0.96062 0.31773 -1.0578 0.52877 0.033297 -0.049265 -0.010945 -searching -0.24236 -0.17512 0.17781 -0.95078 -0.16358 -0.74868 0.38088 0.085915 -0.27398 0.19703 -dollars -0.35512 -0.096205 0.13233 -1.0636 0.25495 -0.91864 0.53955 -0.11415 -0.17314 -0.015355 -guess -0.27593 -0.11835 0.12093 -0.93565 -0.078513 -0.84341 0.43357 0.059761 -0.35287 0.10868 -Kieren -0.28648 -0.052006 0.019997 -0.93716 0.048119 -1.0201 0.5268 -0.082165 -0.20706 0.12905 -23 -0.21086 -0.057727 -0.088208 -0.96071 0.56566 -1.1161 0.58002 -0.23208 -0.13051 -0.046464 -Bonn -0.63015 -0.090793 -0.010465 -0.89356 0.37004 -1.1065 0.49491 -0.13038 -0.12322 -0.18684 -... -0.43566 -0.18061 0.06388 -0.86017 0.013587 -0.82546 0.47717 -0.017943 -0.095205 0.12177 -prepare -0.27096 -0.23845 0.17099 -0.88899 -0.17308 -0.79854 0.33358 0.18857 -0.27852 0.22166 -champion -0.37096 -0.075525 0.033513 -0.87721 0.032021 -1.0175 0.42515 0.086005 -0.22466 0.0095515 -Pollock -0.17072 0.075854 0.034762 -0.99117 0.025959 -0.84027 0.51192 0.054958 -0.34076 0.13271 -television. -0.44044 -0.21914 0.064641 -0.95829 0.15258 -0.9742 0.43971 -0.15748 -0.021531 0.11953 -begun -0.44643 -0.063631 0.062254 -0.89464 -0.26594 -0.77289 0.43425 -0.022602 -0.29919 0.059812 -coast -0.096154 -0.098287 0.25499 -0.86603 0.41534 -0.9861 0.60581 0.24712 -0.30546 -0.11746 -leave -0.48798 -0.14912 0.15468 -1.0082 0.28873 -0.94628 0.46997 -0.14209 0.015677 0.16177 -St -0.15052 -0.038151 0.074615 -0.94029 0.62285 -0.93937 0.64598 0.084345 -0.21029 -0.10022 -Sydney. 0.26468 0.080405 0.32812 -1.0438 0.026161 -0.72776 0.57149 0.22449 -0.45732 0.20119 -losing -0.30082 -0.14351 0.13768 -0.94164 -0.073193 -0.87297 0.40935 -0.035951 -0.20497 0.083264 -work. -0.33936 -0.20084 0.24815 -1.0307 0.14337 -0.84725 0.4851 0.038437 -0.18419 0.15475 -counts -0.18804 -0.080086 0.15436 -0.93251 0.17173 -0.92948 0.56095 0.42776 -0.34996 0.025217 -26-year-old -0.23247 -0.16833 0.10058 -0.95923 0.12781 -0.89816 0.45605 0.021855 -0.15746 0.094273 -suggested -0.22162 -0.0057453 0.067216 -1.0141 0.46976 -1.0677 0.51246 -0.14477 -0.10022 -0.052623 -projects -0.38763 -0.26072 0.012426 -1.01 0.17994 -0.91495 0.52402 -0.090687 -0.25331 0.078616 -understood -0.39868 -0.13931 0.061138 -0.91247 0.04974 -0.9366 0.44174 -0.038707 -0.21788 0.022325 -various -0.42015 -0.30838 0.073304 -0.85863 0.37029 -1.0839 0.46362 0.028696 -0.00035373 -0.010771 -debate -0.12791 -0.10166 0.26514 -0.92209 0.13826 -0.92997 0.45138 0.26024 -0.26033 0.064546 -Bill -0.39838 -0.033529 0.094462 -0.87625 -0.2477 -0.78666 0.481 0.13671 -0.29902 0.21293 -happens -0.21991 -0.11012 -0.0028922 -0.9438 0.12635 -0.90128 0.55503 0.0095414 -0.22928 0.17408 -Commissioner -0.49284 -0.16213 0.12342 -0.90232 0.031972 -0.97126 0.46812 -0.01738 -0.030934 0.15521 -Deputy -0.36846 -0.16084 0.078157 -0.82351 -0.050522 -0.92733 0.5193 0.07008 -0.23301 0.11732 -civilians -0.26415 -0.15822 0.068405 -0.98221 0.50329 -1.0065 0.52038 -0.038207 -0.18997 -0.052481 -threatening -0.25182 -0.045476 0.10861 -0.89748 0.2866 -1.0112 0.42588 -0.062959 -0.30542 -0.11057 -women's -0.19161 -0.071387 0.19565 -1.0621 0.22142 -1.0313 0.47957 0.15791 -0.22421 0.099062 -containment -0.24544 -0.31776 0.35774 -1.0603 0.27667 -0.94787 0.45011 0.25615 -0.13414 0.083895 -stand -0.32437 -0.29159 0.29872 -0.96611 0.10026 -0.85177 0.31083 0.053036 -0.15696 0.15546 -MacGill -0.31908 0.030595 0.093019 -0.94111 -0.093146 -0.86372 0.49992 0.057094 -0.24437 0.11818 -putting -0.38698 -0.14535 0.03712 -0.90819 0.081707 -0.9084 0.42326 -0.057029 -0.26089 -0.021234 -determine -0.4176 -0.097098 0.14469 -0.93155 0.11262 -1.0324 0.43527 0.012073 -0.099626 0.040556 -Israel. -0.47031 -0.063841 -0.37841 -0.97351 1.1357 -1.3919 0.65755 -0.52246 0.1681 -0.27245 -forecast -0.19474 -0.079622 0.15075 -1.103 0.64718 -1.1086 0.38859 0.13428 -0.16916 -0.21521 -During -0.34549 -0.037444 0.073162 -0.8123 -0.10379 -0.97766 0.4863 0.040554 -0.3512 -0.056979 -bureau -0.18654 -0.084556 0.22349 -0.94008 0.17144 -0.88225 0.50717 0.042983 -0.26211 0.16669 -findings -0.15385 0.024256 0.097106 -0.90701 -0.0062973 -0.87097 0.45966 0.092639 -0.31016 0.048742 -fear -0.13347 -0.033548 0.051886 -1.0621 0.69087 -1.067 0.49215 -0.082951 -0.19496 -0.24658 -data -0.34699 -0.17287 -0.01897 -0.93141 0.24327 -0.91246 0.46874 -0.18289 -0.18046 0.081533 -gone -0.26956 -0.29501 0.25143 -1.0867 0.19435 -0.93538 0.33428 0.0059832 -0.084104 0.060041 -record -0.3523 0.061825 0.089374 -0.8624 0.10975 -0.93357 0.47165 0.088856 -0.19873 -0.037258 -hoping -0.18615 -0.052057 0.080318 -0.91618 0.10148 -0.88347 0.48114 -0.0095034 -0.35875 -0.1065 -Israelis. -0.23908 -0.12511 -0.23259 -1.0042 0.8512 -1.1663 0.54631 -0.34526 -0.007403 -0.12883 -Hamid -0.30894 -0.25919 0.073896 -0.86696 -0.060983 -0.83277 0.46301 0.015398 -0.12849 0.15373 -present -0.28584 -0.21305 0.1917 -0.82763 -0.2293 -0.86695 0.41269 0.28361 -0.18485 0.14715 -live -0.36521 -0.27784 0.21715 -1.0707 0.52157 -1.1198 0.27499 -0.065779 0.10911 -0.14034 -ahead. -0.18178 -0.16656 0.22029 -0.91903 0.26257 -0.96658 0.42367 0.15003 -0.19482 0.13553 -warning -0.057134 -0.10892 0.21894 -0.93471 0.075207 -0.93845 0.32891 0.098712 -0.29838 -0.10296 -trapped -0.39018 -0.17152 0.16364 -0.90232 0.39176 -1.0995 0.41738 -0.031247 -0.14006 -0.14978 -markets -0.22304 0.049703 0.023357 -0.89652 0.028235 -0.93915 0.54958 0.10373 -0.31918 -0.015176 -Sergeant -0.22138 -0.073868 0.18847 -0.89862 -0.12118 -0.88672 0.44595 0.059898 -0.20127 0.20083 -Seven -0.15834 0.012167 0.075471 -0.9344 -0.0060505 -0.87636 0.57467 -0.091161 -0.18515 0.16282 -firm -0.20924 0.13692 0.229 -1.0004 0.087825 -0.74557 0.46068 0.063433 -0.19736 -0.013484 -welcomed -0.33084 -0.13075 -0.031394 -0.933 0.12591 -0.93544 0.43404 -0.0071708 -0.19188 0.02752 -responding -0.27598 -0.17165 0.25794 -0.96352 -0.021708 -0.90139 0.36233 0.13162 -0.20719 0.013575 -law -0.23204 0.098585 0.084543 -0.87951 0.045764 -1.0408 0.39644 0.083872 -0.19391 0.06397 -deputy -0.35117 -0.094578 0.069262 -0.8022 0.21323 -1.0385 0.49627 0.14174 -0.18405 -0.036922 -unidentified -0.21788 -0.19765 0.16739 -0.96515 0.26464 -1.0681 0.45954 0.19657 -0.10865 -0.040348 -clashes -0.18525 -0.090192 0.13899 -0.92914 0.19958 -0.99479 0.48018 0.0076089 -0.22087 0.057853 -ago, -0.12196 -0.25092 0.21986 -0.78537 -0.43743 -0.83485 0.35358 0.5775 -0.36967 0.27372 -replied: -0.53654 -0.30794 0.0668 -0.95327 -0.32244 -0.86974 0.32233 0.0035099 -0.12836 0.27396 -path -0.29637 -0.0050163 -0.09227 -0.91761 0.61522 -1.2084 0.51978 -0.070516 -0.022066 -0.26732 -search -0.33437 -0.14509 0.16727 -0.8863 0.019863 -0.77068 0.47079 0.019677 -0.24613 0.18844 -hundred -0.025627 -0.11448 0.18116 -0.96779 0.2532 -0.93224 0.3929 0.12679 -0.19685 -0.13179 -state. -0.18528 -0.23642 0.26504 -1.0479 0.57506 -1.0035 0.41344 -0.17807 -0.09178 -0.023322 -efforts -0.2879 -0.13298 0.14961 -0.93882 0.13162 -0.93926 0.49649 0.17908 -0.18039 0.022261 -tree -0.13421 0.049938 0.039293 -0.85099 -0.054489 -0.95255 0.48989 0.20728 -0.25372 0.081726 -telephone -0.31696 -0.22565 0.13317 -1.0219 0.2304 -1.0449 0.39416 -0.015374 -0.058222 0.064146 -problem -0.23808 -0.055263 0.21232 -0.94406 -0.52183 -0.69284 0.40925 0.16283 -0.35028 0.39899 -approached -0.3755 -0.24085 0.045111 -0.96186 0.31842 -0.96877 0.4262 -0.091481 -0.06434 0.076188 -chairman -0.24927 -0.10477 0.17401 -0.90386 0.013637 -0.93125 0.41858 0.049651 -0.20749 0.15118 -Afroz -0.3492 -0.11356 0.046444 -0.96179 0.49956 -1.1739 0.50381 -0.029897 -0.15488 -0.24651 -Monday, -0.18335 0.075194 0.19071 -0.85914 -0.074304 -0.8325 0.45894 0.23434 -0.32902 0.11567 -advance -0.35487 -0.13718 0.083065 -1.0344 0.076132 -0.86051 0.39686 0.031053 -0.1538 0.12614 diff --git a/gensim/test/test_data/non_ascii_fasttext.vec b/gensim/test/test_data/non_ascii_fasttext.vec deleted file mode 100644 index ea179a2ff5..0000000000 --- a/gensim/test/test_data/non_ascii_fasttext.vec +++ /dev/null @@ -1,172 +0,0 @@ -171 2 -ji -1.5308 2.0551 -který -0.99211 1.4997 -jen -1.1228 1.3667 -podle -1.1469 1.4473 -zde -1.0191 1.4011 -už -0.91921 1.3531 -být -1.0086 1.4582 -více -1.1058 1.3376 -bude -1.2032 1.7383 -již -1.3136 1.4792 -než -1.0664 1.6635 -vás -1.1113 1.5703 -by -1.1698 1.966 -které -1.1295 1.6275 -co -0.93518 1.1776 -nebo -1.0791 1.5071 -ten -1.1881 1.415 -tak -1.4548 1.8457 -má -1.0658 1.5255 -při -1.3464 1.6107 -od -0.79486 1.5585 -po -1.2758 1.9186 -tipy -0.69335 1.0799 -ještě -0.87116 1.1618 -až -1.2688 1.6518 -bez -0.99627 1.423 -také -1.141 1.4808 -pouze -0.94181 1.4076 -první -1.1166 1.5035 -vaše -0.9672 1.4975 -která -1.1102 1.5806 -nás -1.1328 1.5253 -nový -0.85553 1.1462 -jsou -1.0792 1.8008 -pokud -1.0427 1.3178 -může -1.1269 1.419 -strana -0.84973 1.1957 -jeho -1.1644 1.5879 -své -1.0546 1.6185 -jiné -0.95046 1.2816 -zprávy -0.88762 1.3374 -nové -1.0588 1.619 -není -1.0321 1.5566 -tomu -1.0753 1.5211 -ona -1.21 1.6992 -ono -1.0733 1.6574 -oni -1.1153 1.643 -ony -1.0926 1.5244 -my -0.92689 1.6378 -vy -1.3708 1.8 -jí -1.205 1.6606 -mě -0.96436 1.4713 -mne -1.0956 1.6333 -jemu -1.1181 1.4661 -on -1.0062 1.4124 -těm -0.90732 1.2586 -těmu -0.90621 1.4096 -němu -1.0823 1.4396 -němuž -1.0786 1.3892 -jehož -1.1649 1.4418 -jíž -1.0574 1.6338 -jelikož -1.0449 1.3625 -jež -1.2657 1.7032 -jakož -1.3373 1.6112 -načež -1.0127 1.3696 -ze -1.1784 1.7095 -jak -1.2097 1.5224 -další -0.7288 0.96256 -ale -1.1029 1.4153 -si -1.1097 1.5884 -se -1.2981 1.7707 -ve 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1.6688 -pravé -0.9391 1.5172 -nad -1.3404 1.7661 -nejsou -0.85023 1.5033 diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index b21d3d5a1a..ca1c84347b 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -64,7 +64,6 @@ def testTraining(self): self.model_sanity(trained_model) # Tests temporary training files deleted - self.assertFalse(os.path.exists('%s.vec' % testfile())) self.assertFalse(os.path.exists('%s.bin' % testfile())) def testMinCount(self): @@ -115,7 +114,7 @@ def testNormalizedVectorsNotSaved(self): self.assertTrue(loaded_kv.syn0_all_norm is None) def testLoadFastTextFormat(self): - """Test model successfully loaded from fastText .vec and .bin files""" + """Test model successfully loaded from fastText .bin files""" try: model = fasttext.FastText.load_fasttext_format(self.test_model_file) except Exception as exc: @@ -166,7 +165,7 @@ def testLoadFastTextFormat(self): self.model_sanity(model) def testLoadFastTextNewFormat(self): - """ Test model successfully loaded from fastText (new format) .vec and .bin files """ + """ Test model successfully loaded from fastText (new format) .bin files """ try: new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file) except Exception as exc: From 5f7fe02b8990c9a2e87b7d1fcea571309bcf1114 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Tue, 20 Jun 2017 11:22:09 +0530 Subject: [PATCH 17/28] added lee_fasttext vec files again --- gensim/test/test_data/lee_fasttext.vec | 1763 +++++++++++++++++++ gensim/test/test_data/lee_fasttext_new.vec | 1764 ++++++++++++++++++++ 2 files changed, 3527 insertions(+) create mode 100644 gensim/test/test_data/lee_fasttext.vec create mode 100644 gensim/test/test_data/lee_fasttext_new.vec diff --git a/gensim/test/test_data/lee_fasttext.vec b/gensim/test/test_data/lee_fasttext.vec new file mode 100644 index 0000000000..62b0a7005e --- /dev/null +++ b/gensim/test/test_data/lee_fasttext.vec @@ 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0.045111 -0.96186 0.31842 -0.96877 0.4262 -0.091481 -0.06434 0.076188 +chairman -0.24927 -0.10477 0.17401 -0.90386 0.013637 -0.93125 0.41858 0.049651 -0.20749 0.15118 +Afroz -0.3492 -0.11356 0.046444 -0.96179 0.49956 -1.1739 0.50381 -0.029897 -0.15488 -0.24651 +Monday, -0.18335 0.075194 0.19071 -0.85914 -0.074304 -0.8325 0.45894 0.23434 -0.32902 0.11567 +advance -0.35487 -0.13718 0.083065 -1.0344 0.076132 -0.86051 0.39686 0.031053 -0.1538 0.12614 From 8bd56cf585e6597f25a0778c85e1bc70bcb92efa Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Wed, 21 Jun 2017 12:17:11 +0530 Subject: [PATCH 18/28] re-added removed files and unused codes --- gensim/models/wrappers/fasttext.py | 4 + gensim/test/test_data/cp852_fasttext.vec | 172 +++++++++++++++++++ gensim/test/test_data/non_ascii_fasttext.vec | 172 +++++++++++++++++++ 3 files changed, 348 insertions(+) create mode 100644 gensim/test/test_data/cp852_fasttext.vec create mode 100644 gensim/test/test_data/non_ascii_fasttext.vec diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 9eff6edb59..bd18e12750 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -219,6 +219,10 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) super(FastText, self).save(*args, **kwargs) + @classmethod + def load_word2vec_format(cls, *args, **kwargs): + return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) + @classmethod def load_fasttext_format(cls, model_file, encoding='utf8'): """ diff --git a/gensim/test/test_data/cp852_fasttext.vec b/gensim/test/test_data/cp852_fasttext.vec new file mode 100644 index 0000000000..9d1969b0f6 --- /dev/null +++ b/gensim/test/test_data/cp852_fasttext.vec @@ -0,0 +1,172 @@ +171 2 +ji -0.79132 1.9605 +kter -0.90811 1.6411 +jen -0.91547 2.0157 +podle -0.64689 1.6221 +zde -0.79732 2.4019 +u -0.69159 1.7167 +bt -0.455 1.3266 +vce -0.75901 1.688 +bude -0.71114 2.0771 +ji -0.73027 1.267 +ne -0.97888 1.8332 +vs -0.72803 1.6653 +by -0.75761 1.9683 +kter -0.68791 1.6069 +co -1.0059 1.6869 +nebo -0.94393 1.9611 +ten -0.71975 2.124 +tak -0.80566 2.0783 +m -0.83065 1.3732 +pi -0.62158 1.8313 +od -0.44113 1.7755 +po -0.7059 2.2615 +tipy -0.60682 1.7247 +jet -0.68854 1.7517 +a -0.63201 1.4618 +bez -0.52021 1.4513 +tak -0.67762 1.8138 +pouze -0.62611 1.82 +prvn -0.42235 1.6216 +vae -0.7407 1.5659 +kter -0.70914 1.7359 +ns -0.38286 1.6016 +nov -0.83421 1.7609 +jsou -0.82699 1.9694 +pokud -0.35516 1.5075 +me -0.78928 1.6357 +strana -0.57276 1.4149 +jeho -0.78568 2.0226 +sv -0.44488 1.459 +jin -0.90751 1.9602 +zprvy -0.90152 1.9703 +nov -0.78853 1.8593 +nen -0.63949 1.5191 +tomu -0.68126 1.8729 +ona -0.74442 1.825 +ono -0.78171 1.9268 +oni -0.64023 2.0525 +ony -0.78142 1.7097 +my -0.61062 1.8857 +vy -0.9356 1.8875 +j -0.44615 0.92715 +m -0.73676 1.4089 +mne -0.71006 1.7072 +jemu -0.92237 2.1452 +on -0.71417 1.9224 +tm -0.65242 1.8779 +tmu -0.83376 2.054 +nmu -0.79287 1.8645 +nmu -0.51786 1.7297 +jeho -0.88721 1.7431 +j -0.12627 0.68014 +jeliko -0.61809 1.7576 +je -0.8843 1.6723 +jako -0.94336 1.827 +nae -0.76919 1.8106 +ze -0.8277 2.0542 +jak -0.97146 1.9164 +dal -0.5719 1.5148 +ale -0.79733 1.8867 +si -0.61439 1.7134 +se -0.80843 1.8957 +ve -0.7186 1.7891 +to -0.84494 2.3933 +jako -1.1045 2.2656 +za -0.7136 1.9602 +zpt -0.79965 1.6329 +jejich -0.49038 1.6366 +do -0.69806 1.8364 +pro -0.7878 2.2066 +je -1.1291 3.0005 +na -1.0203 2.4399 +atd -0.70418 1.7405 +atp -0.69278 1.5772 +jakmile -0.87231 1.6896 +piem -0.64617 1.4417 +j -0.7135 1.5517 +nm -0.42164 1.7603 +jej -0.77603 1.9544 +zda -0.76742 2.0163 +pro -0.47241 1.7053 +mte -0.75963 1.9814 +tato -0.64318 2.0382 +kam -0.45101 1.498 +tohoto -0.73702 1.8305 +kdo -0.80535 1.8551 +kte -0.72498 1.6669 +mi -0.46791 1.7784 +tyto -0.50319 1.7659 +tom -0.59138 1.8657 +tomuto -0.74312 1.7725 +mt -0.27199 1.1315 +nic -0.56441 1.8591 +proto -0.6649 1.946 +kterou -0.84109 1.7498 +byla -0.58737 1.941 +toho -0.76081 1.8002 +protoe -0.55749 1.6686 +asi -0.51689 1.7079 +bude -0.55392 1.6052 +s -0.74207 1.8989 +k -0.61082 2.079 +o -0.76465 1.8956 +i -0.85412 1.6611 +u -0.68535 1.5332 +v -0.73033 1.3855 +z -0.60751 1.9108 +dnes -0.6001 1.7531 +cz -0.59754 1.4239 +tmto -0.69011 1.6643 +ho -0.55961 1.6968 +budem -0.54027 1.7894 +byli -0.60956 1.793 +jse -0.63127 1.5972 +mj -0.48904 1.2814 +svm -0.48494 1.8751 +ta -0.78131 2.4286 +tomto -0.60948 1.7083 +tohle -0.74747 1.7907 +tuto -0.74687 1.9464 +neg -0.60997 1.7777 +pod -0.49619 1.914 +tma -0.55525 1.6668 +mezi -0.46979 1.3583 +pes -0.5712 1.9908 +ty -0.78637 2.2804 +pak -0.60084 1.7026 +vm -0.48545 1.4611 +ani -0.65672 1.7897 +kdy -0.42318 1.4884 +vak -0.60908 1.6867 +i -0.36843 1.7586 +jsem -0.54047 1.827 +tento -0.64813 1.9799 +lnku -0.65578 1.9129 +lnky -0.55868 1.8642 +aby -0.80989 1.8384 +jsme -0.60673 1.843 +ped -0.53861 2.0502 +pta -0.49464 1.714 +a -0.63056 2.2477 +aj -0.62546 1.6357 +nai -0.5915 1.6066 +napite -0.50964 1.777 +re -0.95733 1.9544 +co -0.54673 1.6466 +tm -0.70952 1.8565 +take -0.55439 1.8013 +svch -0.36878 1.4883 +jej -0.7694 1.6612 +svmi -0.63149 2.1581 +jste -0.68444 2.0978 +byl -0.57205 1.7836 +tu -0.88384 2.2256 +tedy -0.62474 2.0469 +teto -0.63187 1.884 +bylo -0.56362 2.0282 +kde -0.7308 2.0316 +ke -0.60918 1.9317 +prav -0.52626 1.9058 +nad -0.54689 1.8666 +nejsou -0.66814 1.8323 diff --git a/gensim/test/test_data/non_ascii_fasttext.vec b/gensim/test/test_data/non_ascii_fasttext.vec new file mode 100644 index 0000000000..ea179a2ff5 --- /dev/null +++ b/gensim/test/test_data/non_ascii_fasttext.vec @@ -0,0 +1,172 @@ +171 2 +ji -1.5308 2.0551 +který -0.99211 1.4997 +jen -1.1228 1.3667 +podle -1.1469 1.4473 +zde -1.0191 1.4011 +už -0.91921 1.3531 +být -1.0086 1.4582 +více -1.1058 1.3376 +bude -1.2032 1.7383 +již -1.3136 1.4792 +než -1.0664 1.6635 +vás -1.1113 1.5703 +by -1.1698 1.966 +které -1.1295 1.6275 +co -0.93518 1.1776 +nebo -1.0791 1.5071 +ten -1.1881 1.415 +tak -1.4548 1.8457 +má -1.0658 1.5255 +při -1.3464 1.6107 +od -0.79486 1.5585 +po -1.2758 1.9186 +tipy -0.69335 1.0799 +ještě -0.87116 1.1618 +až -1.2688 1.6518 +bez -0.99627 1.423 +také -1.141 1.4808 +pouze -0.94181 1.4076 +první -1.1166 1.5035 +vaše -0.9672 1.4975 +která -1.1102 1.5806 +nás -1.1328 1.5253 +nový -0.85553 1.1462 +jsou -1.0792 1.8008 +pokud -1.0427 1.3178 +může -1.1269 1.419 +strana -0.84973 1.1957 +jeho -1.1644 1.5879 +své -1.0546 1.6185 +jiné -0.95046 1.2816 +zprávy -0.88762 1.3374 +nové -1.0588 1.619 +není -1.0321 1.5566 +tomu -1.0753 1.5211 +ona -1.21 1.6992 +ono -1.0733 1.6574 +oni -1.1153 1.643 +ony -1.0926 1.5244 +my -0.92689 1.6378 +vy -1.3708 1.8 +jí -1.205 1.6606 +mě -0.96436 1.4713 +mne -1.0956 1.6333 +jemu -1.1181 1.4661 +on -1.0062 1.4124 +těm -0.90732 1.2586 +těmu -0.90621 1.4096 +němu -1.0823 1.4396 +němuž -1.0786 1.3892 +jehož -1.1649 1.4418 +jíž -1.0574 1.6338 +jelikož -1.0449 1.3625 +jež -1.2657 1.7032 +jakož -1.3373 1.6112 +načež -1.0127 1.3696 +ze -1.1784 1.7095 +jak -1.2097 1.5224 +další -0.7288 0.96256 +ale -1.1029 1.4153 +si -1.1097 1.5884 +se -1.2981 1.7707 +ve -1.256 1.7985 +to -1.6894 2.2424 +jako -1.2333 1.5942 +za -1.0376 1.6162 +zpět -0.83657 1.354 +jejich -0.97548 1.4219 +do -0.93685 1.4001 +pro -1.4367 1.9498 +je -1.9446 2.5147 +na -1.5543 2.2901 +atd -0.98175 1.3697 +atp -0.83266 1.1085 +jakmile -1.0954 1.2764 +přičemž -1.0533 1.4279 +já -1.1496 1.4432 +nám -1.0246 1.6043 +jej -1.203 1.6252 +zda -0.93651 1.2363 +proč -0.90395 1.3144 +máte -0.99962 1.4802 +tato -1.3248 1.5575 +kam -0.63468 1.246 +tohoto -0.9737 1.3422 +kdo -0.88982 1.4152 +kteří -0.92973 1.4696 +mi -1.343 1.7217 +tyto -0.99375 1.3067 +tom -1.1636 1.608 +tomuto -1.0103 1.3488 +mít -1.1538 1.6326 +nic -0.76497 1.0685 +proto -1.1781 1.6367 +kterou -1.0561 1.563 +byla -0.9338 1.7033 +toho -1.1263 1.5702 +protože -1.1777 1.4984 +asi -1.0555 1.4401 +budeš -0.98208 1.5432 +s -1.3733 1.6447 +k -1.0223 1.6019 +o -1.4531 1.879 +i -1.0985 1.2956 +u -0.91038 1.6173 +v -1.2536 1.5998 +z -0.96962 1.7437 +dnes -0.92891 1.2478 +cz -0.84461 1.0881 +tímto -0.98475 1.3061 +ho -0.74774 1.4925 +budem -1.0178 1.4333 +byli -0.90776 1.4799 +jseš -1.0297 1.4975 +můj -0.891 1.2674 +svým -1.0586 1.5377 +ta -1.4932 2.0156 +tomto -1.1626 1.5135 +tohle -1.2215 1.6529 +tuto -1.0516 1.3583 +neg -0.94527 1.5529 +pod -1.0601 1.578 +téma -0.93273 1.3456 +mezi -0.96807 1.3465 +přes -1.1927 1.5099 +ty -1.3733 1.7374 +pak -1.0392 1.5592 +vám -0.89801 1.3586 +ani -1.2113 1.5634 +když -1.0124 1.5112 +však -0.75634 1.1299 +či -0.79489 1.2817 +jsem -1.0435 1.4903 +tento -1.0861 1.5053 +článku -0.93302 1.3758 +články -0.98897 1.4387 +aby -1.0874 1.6114 +jsme -1.0547 1.6846 +před -1.0538 1.5186 +pta -1.062 1.6063 +a -1.3116 2.0391 +aj -1.1578 1.5193 +naši -1.2075 1.3714 +napište -1.0436 1.4646 +re -1.3115 1.5453 +což -1.1731 1.3545 +tím -1.0296 1.5885 +takže -1.1014 1.3574 +svých -0.82606 1.1187 +její -1.1029 1.3696 +svými -1.1052 1.4953 +jste -1.1003 1.7465 +byl -0.89449 1.4131 +tu -1.1255 1.5505 +tedy -1.1693 1.6446 +teto -1.2134 1.546 +bylo -0.86091 1.3805 +kde -1.3468 1.7507 +ke -1.0699 1.6688 +pravé -0.9391 1.5172 +nad -1.3404 1.7661 +nejsou -0.85023 1.5033 From b9161873ecc7afa673936347a88addbec88dae24 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Wed, 21 Jun 2017 12:52:45 +0530 Subject: [PATCH 19/28] added file name in logging info --- gensim/models/wrappers/fasttext.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index bd18e12750..76d12f9eba 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -237,8 +237,8 @@ def load_fasttext_format(cls, model_file, encoding='utf8'): file to load the entire model. """ - model = cls() + model.file_name = model_file model.load_binary_data('%s.bin' % model_file, encoding=encoding) return model @@ -285,7 +285,7 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - logger.info("loading vocabulary words") + logger.info("loading vocabulary words for fastText model from %s.bin", self.file_name) self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -372,7 +372,7 @@ def init_ngrams(self): ngram_weights = self.wv.syn0_all - logger.info("loading vocabulary weights") + logger.info("loading weights for %s vocabulary words for fastText models from %s.bin", len(self.wv.vocab), self.file_name) for w, vocab in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) @@ -380,7 +380,7 @@ def init_ngrams(self): self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1) - logger.info("loaded %s matrix", self.wv.syn0.shape) + logger.info("loaded %s weight matrix for fastText model from %s.bin", self.wv.syn0.shape, self.file_name) @staticmethod def compute_ngrams(word, min_n, max_n): From 1a0bfc034d88754108019bdde6e57f9e5b51daa5 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Thu, 22 Jun 2017 13:59:21 +0530 Subject: [PATCH 20/28] removing unused load_word2vec_format code --- gensim/models/wrappers/fasttext.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 76d12f9eba..64caacc433 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -219,10 +219,6 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) super(FastText, self).save(*args, **kwargs) - @classmethod - def load_word2vec_format(cls, *args, **kwargs): - return FastTextKeyedVectors.load_word2vec_format(*args, **kwargs) - @classmethod def load_fasttext_format(cls, model_file, encoding='utf8'): """ From 98e028760e62370654cb104c5963811177110d8a Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Thu, 22 Jun 2017 14:45:22 +0530 Subject: [PATCH 21/28] updated logging info and comments --- gensim/models/wrappers/fasttext.py | 18 +++++++----------- gensim/test/test_fasttext_wrapper.py | 4 ++-- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 64caacc433..69c2aa304b 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -281,7 +281,7 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - logger.info("loading vocabulary words for fastText model from %s.bin", self.file_name) + logger.info("loading %s vocabulary words for fastText model from %s.bin file", vocab_size, self.file_name) self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -297,23 +297,19 @@ def load_dict(self, file_handle, encoding='utf8'): count, _ = self.struct_unpack(file_handle, '@qb') if i == nwords and i < vocab_size: - """ - To handle the error in pretrained vector wiki.fr (French). - For more info : https://github.com/facebookresearch/fastText/issues/218 + # To handle the error in pretrained vector wiki.fr (French). + # For more info : https://github.com/facebookresearch/fastText/issues/218 - """ assert word == "__label__" continue # don't add word to vocab self.wv.vocab[word] = Vocab(index=i, count=count) self.wv.index2word.append(word) - assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + assert len(self.wv.vocab) == nwords, 'mismatch between final vocab size ({} words), and expected number of words (nwords), ({} words))'.format(len(self.wv.vocab), nwords) if len(self.wv.vocab) != vocab_size: - logger.warning("mismatch between vocab sizes") - logger.warning("If you are loading any model other than pretrained vector wiki.fr, ") - logger.warning("Please report to Gensim.") - + # expecting to log this warning only for pretrained french vector, wiki.fr + logger.warning("mismatch between final vocab size (%s words), and expected vocab size (vocab_size) (%s words)", len(self.wv.vocab), vocab_size) if self.new_format: for j in range(pruneidx_size): @@ -368,7 +364,7 @@ def init_ngrams(self): ngram_weights = self.wv.syn0_all - logger.info("loading weights for %s vocabulary words for fastText models from %s.bin", len(self.wv.vocab), self.file_name) + logger.info("loading weights for %s vocabulary words for fastText model from %s.bin file", len(self.wv.vocab), self.file_name) for w, vocab in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index ca1c84347b..bcfc56a07b 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -114,7 +114,7 @@ def testNormalizedVectorsNotSaved(self): self.assertTrue(loaded_kv.syn0_all_norm is None) def testLoadFastTextFormat(self): - """Test model successfully loaded from fastText .bin files""" + """Test model successfully loaded from fastText .bin file""" try: model = fasttext.FastText.load_fasttext_format(self.test_model_file) except Exception as exc: @@ -165,7 +165,7 @@ def testLoadFastTextFormat(self): self.model_sanity(model) def testLoadFastTextNewFormat(self): - """ Test model successfully loaded from fastText (new format) .bin files """ + """ Test model successfully loaded from fastText (new format) .bin file """ try: new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file) except Exception as exc: From f3d2032e365c0b9967e08ff4be33bcfe6f953132 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Thu, 22 Jun 2017 15:17:44 +0530 Subject: [PATCH 22/28] input file name with or without .bin both accepted --- gensim/models/wrappers/fasttext.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 69c2aa304b..4236bde9d1 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -228,14 +228,16 @@ def load_fasttext_format(cls, model_file, encoding='utf8'): with a model loaded this way, though you can query for word similarity etc. `model_file` is the path to the FastText output files. - FastText outputs two training files - `/path/to/train.vec` and `/path/to/train.bin` - Expected value for this example: `/path/to/train`. However, you only need .bin - file to load the entire model. + FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin` + + Expected value for this example: `/path/to/model` or `/path/to/model.bin`, + as gensim requires only `.bin` file to load entire fastText model. """ model = cls() - model.file_name = model_file - model.load_binary_data('%s.bin' % model_file, encoding=encoding) + file_name, file_ext = os.path.splitext(model_file) + model.file_name = file_name + file_ext + model.load_binary_data(model.file_name, encoding=encoding) return model @classmethod @@ -281,7 +283,7 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - logger.info("loading %s vocabulary words for fastText model from %s.bin file", vocab_size, self.file_name) + logger.info("loading %s vocabulary words for fastText model from %s file", vocab_size, self.file_name) self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -364,7 +366,7 @@ def init_ngrams(self): ngram_weights = self.wv.syn0_all - logger.info("loading weights for %s vocabulary words for fastText model from %s.bin file", len(self.wv.vocab), self.file_name) + logger.info("loading weights for %s vocabulary words for fastText model from %s file", len(self.wv.vocab), self.file_name) for w, vocab in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) @@ -372,7 +374,7 @@ def init_ngrams(self): self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1) - logger.info("loaded %s weight matrix for fastText model from %s.bin", self.wv.syn0.shape, self.file_name) + logger.info("loaded %s weight matrix for fastText model from %s file", self.wv.syn0.shape, self.file_name) @staticmethod def compute_ngrams(word, min_n, max_n): From bd7e7f6d5db206b212484d836b6177414cb9a06a Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Thu, 22 Jun 2017 15:48:37 +0530 Subject: [PATCH 23/28] resolved typo mistake --- gensim/models/wrappers/fasttext.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 4236bde9d1..9c8b184796 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -236,7 +236,7 @@ def load_fasttext_format(cls, model_file, encoding='utf8'): """ model = cls() file_name, file_ext = os.path.splitext(model_file) - model.file_name = file_name + file_ext + model.file_name = file_name + file_ext if file_ext else file_name + '.bin' model.load_binary_data(model.file_name, encoding=encoding) return model From 800cd01017277b4a466cbc2472cf4b82069f8bb8 Mon Sep 17 00:00:00 2001 From: Prakhar Pratyush Date: Thu, 22 Jun 2017 21:02:34 +0530 Subject: [PATCH 24/28] test for file name --- gensim/test/test_fasttext_wrapper.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py index bcfc56a07b..bf6ac7db98 100644 --- a/gensim/test/test_fasttext_wrapper.py +++ b/gensim/test/test_fasttext_wrapper.py @@ -215,6 +215,11 @@ def testLoadFastTextNewFormat(self): self.assertEquals(new_model.wv.min_n, 3) self.model_sanity(new_model) + def testLoadFileName(self): + """ Test model accepts input as both `/path/to/model` or `/path/to/model.bin` """ + self.assertTrue(fasttext.FastText.load_fasttext_format(datapath('lee_fasttext_new'))) + self.assertTrue(fasttext.FastText.load_fasttext_format(datapath('lee_fasttext_new.bin'))) + def testLoadModelWithNonAsciiVocab(self): """Test loading model with non-ascii words in vocab""" model = fasttext.FastText.load_fasttext_format(datapath('non_ascii_fasttext')) From a15233a1540d57e0383e742701565ed0b2afebaf Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Fri, 23 Jun 2017 14:27:54 +0530 Subject: [PATCH 25/28] minor change to input filename handling in ft wrapper --- gensim/models/wrappers/fasttext.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 9c8b184796..91333a7d27 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -235,9 +235,10 @@ def load_fasttext_format(cls, model_file, encoding='utf8'): """ model = cls() - file_name, file_ext = os.path.splitext(model_file) - model.file_name = file_name + file_ext if file_ext else file_name + '.bin' - model.load_binary_data(model.file_name, encoding=encoding) + if not model_file.endswith('.bin'): + model_file += '.bin' + model.file_name = model_file + model.load_binary_data(encoding=encoding) return model @classmethod @@ -250,9 +251,9 @@ def delete_training_files(cls, model_file): logger.debug('Training files %s not found when attempting to delete', model_file) pass - def load_binary_data(self, model_binary_file, encoding='utf8'): + def load_binary_data(self, encoding='utf8'): """Loads data from the output binary file created by FastText training""" - with utils.smart_open(model_binary_file, 'rb') as f: + with utils.smart_open(self.file_name, 'rb') as f: self.load_model_params(f) self.load_dict(f, encoding=encoding) self.load_vectors(f) From 431aebfd20ace4d2431c2af082a977ee20c03320 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Fri, 23 Jun 2017 14:40:35 +0530 Subject: [PATCH 26/28] changes to logging and assert messages, pep8 fixes --- gensim/models/wrappers/fasttext.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py index 91333a7d27..5f059a7328 100644 --- a/gensim/models/wrappers/fasttext.py +++ b/gensim/models/wrappers/fasttext.py @@ -284,7 +284,7 @@ def load_model_params(self, file_handle): def load_dict(self, file_handle, encoding='utf8'): vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i') # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - logger.info("loading %s vocabulary words for fastText model from %s file", vocab_size, self.file_name) + logger.info("loading %s words for fastText model from %s", vocab_size, self.file_name) self.struct_unpack(file_handle, '@1q') # number of tokens if self.new_format: @@ -303,16 +303,21 @@ def load_dict(self, file_handle, encoding='utf8'): # To handle the error in pretrained vector wiki.fr (French). # For more info : https://github.com/facebookresearch/fastText/issues/218 - assert word == "__label__" + assert word == "__label__", ( + 'mismatched vocab_size ({}) and nwords ({}), extra word "{}"'.format(vocab_size, nwords, word)) continue # don't add word to vocab self.wv.vocab[word] = Vocab(index=i, count=count) self.wv.index2word.append(word) - assert len(self.wv.vocab) == nwords, 'mismatch between final vocab size ({} words), and expected number of words (nwords), ({} words))'.format(len(self.wv.vocab), nwords) + assert len(self.wv.vocab) == nwords, ( + 'mismatch between final vocab size ({} words), ' + 'and expected number of words ({} words)'.format(len(self.wv.vocab), nwords)) if len(self.wv.vocab) != vocab_size: # expecting to log this warning only for pretrained french vector, wiki.fr - logger.warning("mismatch between final vocab size (%s words), and expected vocab size (vocab_size) (%s words)", len(self.wv.vocab), vocab_size) + logger.warning( + "mismatch between final vocab size (%s words), and expected vocab size (%s words)", + len(self.wv.vocab), vocab_size) if self.new_format: for j in range(pruneidx_size): @@ -323,7 +328,8 @@ def load_vectors(self, file_handle): self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc num_vectors, dim = self.struct_unpack(file_handle, '@2q') # Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc) - assert self.vector_size == dim, 'mismatch between model sizes' + assert self.vector_size == dim, ( + 'mismatch between vector size in model params ({}) and model vectors ({})'.format(self.vector_size, dim)) float_size = struct.calcsize('@f') if float_size == 4: dtype = np.dtype(np.float32) @@ -334,7 +340,9 @@ def load_vectors(self, file_handle): self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim) self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ - 'mismatch between weight matrix shape and vocab/model size' + 'mismatch between actual weight matrix shape {} and expected shape {}'.format( + self.wv.syn0_all.shape, (self.bucket + len(self.wv.vocab), self.vector_size)) + self.init_ngrams() def struct_unpack(self, file_handle, fmt): @@ -367,7 +375,7 @@ def init_ngrams(self): ngram_weights = self.wv.syn0_all - logger.info("loading weights for %s vocabulary words for fastText model from %s file", len(self.wv.vocab), self.file_name) + logger.info("loading weights for %s words for fastText model from %s", len(self.wv.vocab), self.file_name) for w, vocab in self.wv.vocab.items(): word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) @@ -375,7 +383,7 @@ def init_ngrams(self): self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1) - logger.info("loaded %s weight matrix for fastText model from %s file", self.wv.syn0.shape, self.file_name) + logger.info("loaded %s weight matrix for fastText model from %s", self.wv.syn0.shape, self.file_name) @staticmethod def compute_ngrams(word, min_n, max_n): From e52fee47fec7cd4971c185205ba71856bef900b6 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Fri, 23 Jun 2017 17:00:53 +0530 Subject: [PATCH 27/28] removes redundant .vec files --- gensim/test/test_data/cp852_fasttext.vec | 172 -- gensim/test/test_data/lee_fasttext_new.vec | 1764 ------------------ gensim/test/test_data/non_ascii_fasttext.vec | 172 -- 3 files changed, 2108 deletions(-) delete mode 100644 gensim/test/test_data/cp852_fasttext.vec delete mode 100644 gensim/test/test_data/lee_fasttext_new.vec delete mode 100644 gensim/test/test_data/non_ascii_fasttext.vec diff --git a/gensim/test/test_data/cp852_fasttext.vec b/gensim/test/test_data/cp852_fasttext.vec deleted file mode 100644 index 9d1969b0f6..0000000000 --- a/gensim/test/test_data/cp852_fasttext.vec +++ /dev/null @@ -1,172 +0,0 @@ -171 2 -ji -0.79132 1.9605 -kter -0.90811 1.6411 -jen -0.91547 2.0157 -podle -0.64689 1.6221 -zde -0.79732 2.4019 -u -0.69159 1.7167 -bt -0.455 1.3266 -vce -0.75901 1.688 -bude -0.71114 2.0771 -ji -0.73027 1.267 -ne -0.97888 1.8332 -vs -0.72803 1.6653 -by -0.75761 1.9683 -kter -0.68791 1.6069 -co -1.0059 1.6869 -nebo -0.94393 1.9611 -ten -0.71975 2.124 -tak -0.80566 2.0783 -m -0.83065 1.3732 -pi -0.62158 1.8313 -od -0.44113 1.7755 -po -0.7059 2.2615 -tipy -0.60682 1.7247 -jet -0.68854 1.7517 -a -0.63201 1.4618 -bez -0.52021 1.4513 -tak -0.67762 1.8138 -pouze -0.62611 1.82 -prvn -0.42235 1.6216 -vae -0.7407 1.5659 -kter -0.70914 1.7359 -ns -0.38286 1.6016 -nov -0.83421 1.7609 -jsou -0.82699 1.9694 -pokud -0.35516 1.5075 -me -0.78928 1.6357 -strana -0.57276 1.4149 -jeho -0.78568 2.0226 -sv -0.44488 1.459 -jin -0.90751 1.9602 -zprvy -0.90152 1.9703 -nov -0.78853 1.8593 -nen -0.63949 1.5191 -tomu -0.68126 1.8729 -ona -0.74442 1.825 -ono -0.78171 1.9268 -oni -0.64023 2.0525 -ony -0.78142 1.7097 -my -0.61062 1.8857 -vy -0.9356 1.8875 -j -0.44615 0.92715 -m -0.73676 1.4089 -mne -0.71006 1.7072 -jemu -0.92237 2.1452 -on -0.71417 1.9224 -tm -0.65242 1.8779 -tmu -0.83376 2.054 -nmu -0.79287 1.8645 -nmu -0.51786 1.7297 -jeho -0.88721 1.7431 -j -0.12627 0.68014 -jeliko -0.61809 1.7576 -je -0.8843 1.6723 -jako -0.94336 1.827 -nae -0.76919 1.8106 -ze -0.8277 2.0542 -jak -0.97146 1.9164 -dal -0.5719 1.5148 -ale -0.79733 1.8867 -si -0.61439 1.7134 -se -0.80843 1.8957 -ve -0.7186 1.7891 -to -0.84494 2.3933 -jako -1.1045 2.2656 -za -0.7136 1.9602 -zpt -0.79965 1.6329 -jejich -0.49038 1.6366 -do -0.69806 1.8364 -pro -0.7878 2.2066 -je -1.1291 3.0005 -na -1.0203 2.4399 -atd -0.70418 1.7405 -atp -0.69278 1.5772 -jakmile -0.87231 1.6896 -piem -0.64617 1.4417 -j -0.7135 1.5517 -nm -0.42164 1.7603 -jej -0.77603 1.9544 -zda -0.76742 2.0163 -pro -0.47241 1.7053 -mte -0.75963 1.9814 -tato -0.64318 2.0382 -kam -0.45101 1.498 -tohoto -0.73702 1.8305 -kdo -0.80535 1.8551 -kte -0.72498 1.6669 -mi -0.46791 1.7784 -tyto -0.50319 1.7659 -tom -0.59138 1.8657 -tomuto -0.74312 1.7725 -mt -0.27199 1.1315 -nic -0.56441 1.8591 -proto -0.6649 1.946 -kterou -0.84109 1.7498 -byla -0.58737 1.941 -toho -0.76081 1.8002 -protoe -0.55749 1.6686 -asi -0.51689 1.7079 -bude -0.55392 1.6052 -s -0.74207 1.8989 -k -0.61082 2.079 -o -0.76465 1.8956 -i -0.85412 1.6611 -u -0.68535 1.5332 -v -0.73033 1.3855 -z -0.60751 1.9108 -dnes -0.6001 1.7531 -cz -0.59754 1.4239 -tmto -0.69011 1.6643 -ho -0.55961 1.6968 -budem -0.54027 1.7894 -byli -0.60956 1.793 -jse -0.63127 1.5972 -mj -0.48904 1.2814 -svm -0.48494 1.8751 -ta -0.78131 2.4286 -tomto -0.60948 1.7083 -tohle -0.74747 1.7907 -tuto -0.74687 1.9464 -neg -0.60997 1.7777 -pod -0.49619 1.914 -tma -0.55525 1.6668 -mezi -0.46979 1.3583 -pes -0.5712 1.9908 -ty -0.78637 2.2804 -pak -0.60084 1.7026 -vm -0.48545 1.4611 -ani -0.65672 1.7897 -kdy -0.42318 1.4884 -vak -0.60908 1.6867 -i -0.36843 1.7586 -jsem -0.54047 1.827 -tento -0.64813 1.9799 -lnku -0.65578 1.9129 -lnky -0.55868 1.8642 -aby -0.80989 1.8384 -jsme -0.60673 1.843 -ped -0.53861 2.0502 -pta -0.49464 1.714 -a -0.63056 2.2477 -aj -0.62546 1.6357 -nai -0.5915 1.6066 -napite -0.50964 1.777 -re -0.95733 1.9544 -co -0.54673 1.6466 -tm -0.70952 1.8565 -take -0.55439 1.8013 -svch -0.36878 1.4883 -jej -0.7694 1.6612 -svmi -0.63149 2.1581 -jste -0.68444 2.0978 -byl -0.57205 1.7836 -tu -0.88384 2.2256 -tedy -0.62474 2.0469 -teto -0.63187 1.884 -bylo -0.56362 2.0282 -kde -0.7308 2.0316 -ke -0.60918 1.9317 -prav -0.52626 1.9058 -nad -0.54689 1.8666 -nejsou -0.66814 1.8323 diff --git 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0.52003 -0.040411 -0.2842 -0.072086 -powers -0.29463 -0.19813 0.28684 -1.014 0.080471 -0.81738 0.36285 -0.052792 -0.21976 0.16303 -helicopter -0.25687 -0.064613 0.045338 -0.90035 0.38695 -1.0113 0.43999 0.0056159 -0.22388 -0.028703 -total -0.19574 -0.19443 0.19962 -0.90192 0.12229 -0.94611 0.41304 0.18708 -0.24157 0.10976 -well, -0.24042 -0.0048114 -0.10428 -0.98395 -0.24487 -0.74945 0.50611 -0.11992 -0.48088 0.26573 -terror -0.37502 -0.19371 0.22344 -0.89512 0.26076 -0.94791 0.40102 -0.02156 -0.27957 -0.12665 -list -0.31649 -0.10805 0.088525 -0.87109 0.39583 -0.94694 0.56579 -0.020434 -0.30059 -0.091529 -wickets -0.11094 0.25547 -0.028393 -0.98382 0.37106 -0.98481 0.64935 0.016147 -0.50319 -0.18784 -confidence -0.38178 -0.29464 0.069465 -1.0372 0.24086 -0.91127 0.42344 -0.060514 -0.059409 0.12219 -post -0.071096 -0.1008 0.19798 -0.95918 -0.16612 -0.8353 0.488 0.29332 -0.38116 0.10752 -base -0.21322 -0.1371 0.27562 -1.0178 -0.35491 -0.63268 0.34969 0.21059 -0.25413 0.38995 -commander 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0.54029 0.19495 -0.12341 -0.20142 -Pacific -0.36132 -0.22185 0.15185 -0.91572 0.11814 -0.98048 0.49792 0.085878 -0.12503 0.16658 -Simon -0.11685 -0.038896 0.079094 -0.84486 -0.31179 -0.8822 0.37843 0.17594 -0.24671 0.21485 -fellow -0.45967 -0.043865 0.13649 -0.96167 0.12351 -0.86724 0.40706 -0.046775 -0.1364 -0.08718 -force, -0.34743 -0.29381 0.050942 -0.95045 0.59266 -1.2173 0.36728 0.035491 -0.18104 -0.20573 -freeze -0.49273 -0.15795 0.18215 -0.92833 0.079104 -0.90847 0.41997 0.11126 -0.19785 -0.084013 -damaged 0.0012511 -0.030074 0.25001 -0.92128 -0.0029533 -0.96183 0.34022 0.3789 -0.33441 0.096431 -mean -0.4804 -0.16951 0.048943 -0.8598 -0.24383 -0.88889 0.42948 -0.0015202 -0.15628 0.17191 -tennis -0.24646 -0.071839 0.15165 -0.98835 0.22813 -0.84679 0.51183 0.010715 -0.28158 0.15038 -him. -0.27782 -0.08642 0.096749 -0.80777 -0.36018 -0.76288 0.38563 0.046946 -0.23796 0.26555 -threat -0.26372 -0.050867 0.14379 -1.0282 0.031866 -0.79964 0.37458 -0.098354 -0.31621 0.17716 -significant 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0.010165 -0.21182 -March -0.38713 0.086237 0.15301 -0.86982 -0.33435 -0.78235 0.48544 0.094351 -0.31309 0.13725 -Attorney-General -0.25057 -0.19638 0.18944 -0.93629 -0.090629 -0.86499 0.42347 0.20342 -0.21998 0.22308 -Endeavour -0.22169 -0.092432 0.035077 -0.8808 0.029427 -0.86335 0.45579 0.044855 -0.27194 0.15782 -Pakistan's -0.92088 -0.49816 0.079493 -1.014 0.8061 -1.2665 0.45884 -0.23544 0.10661 -0.39567 -Ian -0.3708 -0.067236 0.096163 -0.93513 0.45945 -1.062 0.56056 -0.14612 -0.066785 -0.0086675 -Bank, -0.26307 -0.19509 0.17071 -1.0483 0.64624 -1.0057 0.50766 -0.13061 -0.14266 -0.067282 -space -0.24785 0.031023 -0.20854 -0.93185 0.16903 -0.94983 0.57514 0.011342 -0.29523 0.03631 -remains -0.27094 0.020081 0.089926 -0.99737 0.34499 -0.91512 0.50734 0.072577 -0.15759 -0.05245 -explosives -0.36031 -0.15429 0.078313 -0.92549 0.08738 -0.9409 0.45157 0.0516 -0.18237 0.033363 -east -0.19675 0.12544 0.10072 -1.019 0.81313 -1.0884 0.62149 0.11888 -0.29042 -0.42242 -25 0.11895 -0.056397 0.090409 -1.0446 0.10077 -0.95928 0.33355 0.070212 -0.30139 0.21796 -battle -0.27846 -0.070352 0.15079 -0.98463 -0.38279 -0.72243 0.37669 -0.068275 -0.2686 0.37715 -Jason -0.187 -0.015456 0.11623 -0.84792 0.19114 -1.0272 0.57475 0.18859 -0.29507 -0.12471 -Lockett -0.46609 -0.11666 0.14205 -0.89711 -0.25063 -0.81773 0.36452 0.064722 -0.31159 0.10607 -capital -0.22541 -0.12107 0.028604 -0.9452 0.57696 -1.1292 0.53787 -0.050462 -0.10325 -0.17973 -ahead -0.24124 -0.16768 0.13893 -0.91959 0.26864 -0.91944 0.44784 0.040978 -0.18457 0.11707 -Party -0.23333 -0.12322 0.061631 -0.94488 0.12722 -0.97368 0.53133 0.095747 -0.20035 0.10882 -didn't -0.37202 -0.12961 0.099155 -0.96571 0.32674 -1.1425 0.39862 -0.089292 -0.15754 -0.16824 -storms -0.13308 -0.07898 0.3656 -1.0861 -0.076813 -0.763 0.3458 0.080008 -0.2326 0.2837 -signed -0.2608 -0.26592 0.21975 -1.0783 0.43314 -0.98418 0.34998 -0.14913 -0.069144 -0.058183 -January -0.57483 -0.064061 -0.0063107 -0.88573 0.20769 -1.0344 0.50173 -0.12499 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0.18715 -0.90246 0.045488 -0.98513 0.44541 0.24472 -0.21424 0.13154 -Americans -0.14396 -0.053435 0.091117 -0.97989 0.13175 -0.92375 0.50287 0.10374 -0.3305 0.049118 -market -0.25229 0.091016 0.12493 -0.82617 -0.36877 -0.84742 0.43253 0.21714 -0.35559 0.13219 -wounded -0.3291 -0.25446 0.083525 -1.0483 0.48991 -1.0708 0.47048 -0.087657 -0.073512 -0.02871 -provisional -0.28291 -0.14185 0.011425 -0.79579 0.055761 -0.99861 0.54567 0.1421 -0.211 0.1105 -measures -0.19868 -0.14164 0.28929 -0.90265 0.025215 -0.83739 0.41811 0.21035 -0.22273 0.10311 -added. -0.42632 -0.17106 0.12843 -0.92895 0.14204 -0.93931 0.47269 -0.059101 -0.11808 0.034853 -mission -0.61278 -0.16954 0.00067001 -0.76193 0.061803 -1.0087 0.46712 0.021911 -0.038718 0.030982 -wake -0.093672 0.055472 -0.019713 -0.92111 -0.11022 -0.97104 0.41747 0.10263 -0.27675 0.12806 -airline's -0.22412 -0.12503 0.25227 -0.95896 -0.059966 -0.86024 0.36567 0.09931 -0.19185 0.11832 -secret -0.458 -0.081446 0.11812 -0.8207 -0.17783 -0.88348 0.52513 -0.024678 -0.096435 0.16607 -Ruddock -0.34777 -0.029505 0.024112 -0.80824 0.11575 -0.93498 0.55054 0.086513 -0.1592 0.066195 -happened -0.31539 -0.17737 0.071333 -0.97106 0.22044 -0.94565 0.44359 -0.12155 -0.070321 0.088699 -rise -0.22078 0.016361 0.085381 -0.98863 -0.15084 -0.74209 0.47434 -0.081044 -0.16355 0.23253 -Sharon's -0.61083 0.081367 -0.020081 -0.89849 0.25259 -1.1607 0.51477 -0.4268 0.058208 -0.059512 -strategic -0.31828 -0.19848 0.17038 -0.9227 0.442 -0.9679 0.50203 -0.0076138 -0.17986 0.068369 -keep -0.10788 0.1237 -0.035003 -0.9761 0.023099 -0.89026 0.50539 0.036025 -0.34625 0.13666 -minister -0.81028 -0.045458 -0.015941 -0.78384 0.2495 -1.1529 0.58248 -0.26326 0.068149 0.055324 -sea -0.46396 -0.26388 0.2936 -0.98634 0.18721 -0.75769 0.46366 -0.25523 0.073609 0.27053 -Ray -0.074281 0.020983 0.096088 -1.0812 0.33056 -0.99169 0.58882 0.044375 -0.27237 -0.017101 -visit -0.33539 0.0099953 -0.054178 -0.83477 0.19101 -1.1189 0.42853 -0.007317 -0.099517 -0.107 -Road 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-Nauru -0.30785 -0.11305 0.076707 -0.97418 0.019464 -0.79503 0.38329 -0.092909 -0.2986 0.085795 -nothing -0.10046 -0.26032 0.2425 -0.96312 -0.45507 -0.6761 0.31895 0.033381 -0.3226 0.35858 -school -0.34002 -0.14079 0.17584 -0.94491 -0.032758 -0.86983 0.4427 0.1536 -0.13169 0.20775 -started -0.10147 -0.15964 0.25274 -0.98902 -0.00099649 -0.82592 0.40647 0.064374 -0.25575 0.18136 -Force -0.5079 -0.25116 0.06707 -0.91445 0.017578 -0.89055 0.43581 0.033313 -0.10564 0.07492 -negotiating -0.22875 -0.10236 0.016135 -0.93902 0.1019 -0.97418 0.47083 0.13294 -0.3142 -0.034064 -terrorism -0.3095 -0.16914 0.23158 -0.8955 0.14668 -0.97299 0.43941 0.13505 -0.27607 -0.068449 -include -0.3025 -0.13224 0.062704 -0.87496 0.17456 -0.96419 0.46202 0.036182 -0.23544 -0.11814 -issued -0.16907 0.062066 0.13286 -0.93521 -0.03815 -0.85031 0.36243 0.24582 -0.17024 0.062843 -finished -0.35034 -0.14011 0.069695 -1.0225 0.30062 -1.0641 0.44014 0.024424 -0.13101 -0.032742 -Some -0.0099117 -0.061962 0.16397 -0.87882 -0.35958 -0.73887 0.56596 0.35597 -0.39751 0.26644 -operating -0.33804 -0.14838 -0.063496 -0.88383 0.38855 -1.0461 0.57688 0.090779 -0.27263 -0.19365 -whole -0.25506 -0.21451 0.22861 -0.94329 -0.2153 -0.81776 0.36984 0.018867 -0.24345 0.20418 -son -0.34367 -0.12143 -0.30906 -0.8543 0.24156 -1.0248 0.59752 -0.26618 -0.1898 0.14002 -crisis -0.28756 -0.073788 0.18835 -0.94887 -0.066948 -0.88405 0.40472 0.088737 -0.24188 0.017432 -bomber -0.19771 -0.11944 -0.1017 -0.94034 0.73826 -1.2021 0.55556 -0.29073 -0.034958 -0.1653 -saw -0.25176 0.00058522 0.12581 -0.95181 -0.56601 -0.74178 0.27863 0.078124 -0.32676 0.34685 -accompanied -0.23652 -0.11722 0.15679 -0.91396 0.24583 -1.1026 0.46984 0.14369 -0.20654 -0.088014 -bowling -0.23753 0.10104 -0.10521 -0.89953 0.093848 -1.0557 0.55039 0.049629 -0.286 -0.23985 -circumstances -0.24159 -0.13832 0.22577 -1.0123 -0.014924 -0.705 0.44815 0.094114 -0.27955 0.1786 -added -0.39582 -0.20179 0.18945 -1.0291 0.60074 -1.0893 0.4444 -0.14892 0.037658 -0.16329 -severe -0.4236 -0.24805 0.24307 -1.0071 -0.0098305 -0.86059 0.38742 0.055473 -0.097104 0.26985 -closed -0.1375 -0.094965 0.16604 -0.95943 0.46679 -1.093 0.47617 0.036413 -0.14127 -0.1315 -there, -0.23426 -0.23311 0.20568 -1.0534 -0.69502 -0.4636 0.26911 -0.031192 -0.30147 0.63743 -employees -0.053637 -0.15401 0.26868 -0.92355 -0.18449 -0.77002 0.42827 0.27962 -0.35416 0.27443 -Victorian -0.21411 -0.093511 0.1154 -0.93202 0.029805 -0.89468 0.40768 0.10296 -0.28345 0.1034 -condition -0.39908 -0.34224 0.27793 -0.94165 0.15307 -0.91617 0.39146 0.23894 -0.17391 0.1092 -almost -0.21181 -0.14618 0.14846 -0.90683 -0.283 -0.71038 0.36562 0.20319 -0.32717 0.21449 -ballot -0.2257 -0.12386 0.15275 -0.92709 -0.38533 -0.68577 0.35565 0.095352 -0.21546 0.40701 -pulled -0.13349 -0.16285 0.30139 -1.0546 0.34589 -0.96727 0.35756 -0.079435 -0.0053248 0.082715 -action, -0.61402 -0.28428 0.10996 -0.95448 -0.058881 -0.8188 0.33729 0.11333 -0.14163 0.15422 -sides -0.12322 0.033408 0.051573 -0.88149 -0.018843 -0.99042 0.42088 0.043824 -0.32997 -0.04907 -400 -0.41921 -0.17601 0.22295 -0.76943 -0.9355 -0.50706 0.30725 0.19276 -0.27032 0.49706 -reduce -0.30168 -0.076541 -0.036801 -0.86058 -0.19464 -0.90828 0.47634 0.19276 -0.17134 0.20792 -Earlier, -0.34794 0.023292 0.075406 -0.84766 0.15437 -0.99986 0.49732 -0.050574 -0.22073 -0.038666 -families -0.28825 -0.14532 0.16689 -0.92058 -0.13739 -0.78522 0.39078 -0.0039667 -0.14736 0.15144 -winning -0.06874 0.10324 -0.016348 -0.98934 -0.053567 -0.95126 0.46299 0.063867 -0.5229 -0.13015 -resolution -0.31964 -0.17339 0.17081 -0.8143 -0.12043 -0.96715 0.44535 0.41145 -0.26246 0.052698 -smoke -0.26105 -0.14285 0.11699 -0.98785 -0.27744 -0.8044 0.35207 0.056853 -0.23205 0.31568 -office -0.218 -0.14524 -0.037394 -0.85397 0.37119 -1.1472 0.56784 -0.041094 0.015932 0.028657 -receive -0.23989 -0.21716 0.34527 -0.91211 -0.23018 -0.80992 0.24342 0.29718 -0.22186 0.27324 -destroyed -0.046881 -0.092095 0.24425 -1.0475 0.45445 -1.0208 0.50193 0.10671 -0.19005 -0.011066 -continued -0.35375 -0.13996 0.16789 -1.0129 0.37804 -1.0076 0.43033 0.20117 -0.089802 -0.060949 -paid -0.44553 -0.24882 0.05324 -0.84873 0.10563 -1.0162 0.44625 -0.11117 0.0057727 0.037079 -virus -0.25906 -0.14378 0.026083 -0.96655 -0.25376 -0.8274 0.44257 -0.033073 -0.13939 0.28351 -rest -0.14087 -0.24237 0.08868 -0.86292 0.12032 -0.92578 0.42126 0.35075 -0.26013 0.14123 -flames -0.079384 -0.057751 0.22134 -0.99779 -0.12274 -0.77179 0.44596 0.15104 -0.36885 0.13729 -Government's -0.35662 -0.36318 0.17055 -0.91577 0.23494 -0.97414 0.53677 0.186 -0.10494 0.067981 -carry -0.36108 0.022253 0.025248 -0.95315 -0.020318 -0.8818 0.44329 -0.11946 -0.25438 0.028633 -lower -0.54397 -0.03934 0.066307 -0.89938 -0.020153 -0.9651 0.46286 -0.13729 -0.082547 0.28365 -knew -0.2977 0.02006 0.044883 -0.95161 -0.12807 -0.83868 0.43772 -0.015755 -0.39014 0.017092 -charge -0.044502 -0.19017 0.23235 -0.93397 0.17141 -1.0606 0.26509 0.14126 -0.18608 0.073823 -cars -0.20463 -0.10096 0.020315 -0.98449 0.22193 -0.92139 0.51828 -0.020477 -0.17681 0.033465 -themselves -0.27441 -0.19666 0.094191 -0.94752 0.011452 -0.82052 0.43824 -0.084641 -0.19891 0.2043 -built -0.05453 -0.20536 0.23449 -0.87066 0.0043947 -0.87834 0.4078 0.15113 -0.27461 0.18582 -traditional -0.31808 -0.14846 0.10253 -0.77127 0.21081 -1.0488 0.5409 0.2533 -0.21733 -0.030549 -reach -0.44067 -0.070178 0.19759 -0.81569 -0.47873 -0.84046 0.42358 0.2951 -0.2969 0.25419 -heart -0.090321 -0.17419 0.20768 -0.92797 0.18419 -0.91507 0.45489 0.14948 -0.29072 0.040668 -W 0.072293 -0.22586 0.2214 -0.95216 0.099508 -0.79726 0.35051 0.078391 -0.34478 0.17774 -bit -0.37841 -0.14545 0.16727 -1.1463 0.81219 -1.125 0.36784 -0.13697 0.10096 -0.38367 -I've -0.14223 0.086352 0.035407 -1.0594 0.028174 -0.87599 0.4268 0.071434 -0.23468 0.065687 -alongside -0.35098 -0.1316 0.11102 -0.99051 0.19609 -0.92738 0.4183 -0.061194 -0.21929 -0.036609 -24 0.031811 -0.038653 0.16645 -0.90522 0.06096 -0.7654 0.39738 0.035196 -0.34309 0.28498 -Karzai -0.31044 -0.17659 0.09338 -0.94382 0.16063 -0.955 0.48382 0.070387 -0.24624 0.10667 -determined -0.39606 -0.12691 0.11161 -0.90735 0.063893 -1.0105 0.41939 0.03563 -0.13198 0.068664 -served -0.31224 -0.1469 0.13778 -0.99746 0.56113 -1.0421 0.45573 -0.11786 -0.032157 -0.034298 -negotiations -0.24608 -0.22317 0.10986 -0.9195 0.14543 -0.93048 0.43894 0.26543 -0.34712 0.051059 -disappointed -0.36286 -0.17775 0.10179 -0.95963 0.29768 -1.024 0.45008 -0.078212 -0.10802 -0.015059 -million. -0.55679 -0.27167 0.15737 -0.88907 0.096537 -1.0085 0.39062 0.045772 -0.07645 0.030597 -5 -0.12427 0.14708 -0.01595 -0.81057 0.21726 -1.0829 0.6152 0.3897 -0.15517 0.0022669 -hold -0.37219 -0.10254 0.074666 -1.0612 -0.07405 -0.90405 0.45018 0.0064788 -0.11655 0.22733 -vote -0.64369 -0.17007 0.024417 -0.93574 -0.092902 -0.94459 0.54972 -0.12877 -0.11921 0.0079356 -nations -0.46773 -0.40285 0.086514 -0.84019 0.45642 -1.03 0.53014 0.32809 -0.30576 -0.03859 -voted -0.38662 -0.17358 0.070854 -0.95296 0.01016 -0.96932 0.41977 -0.067503 -0.032331 0.007658 -City -0.15933 -0.054625 -0.16483 -0.91163 0.3106 -1.0914 0.41468 -0.16155 -0.20879 0.021867 -attacked -0.18449 -0.040412 0.064541 -1.2025 1.1638 -1.3294 0.41003 -0.30483 0.020549 -0.56813 -approach -0.31461 -0.27648 0.10962 -0.91722 0.018954 -0.86706 0.39329 -0.02701 -0.202 0.22297 -resolve -0.3028 -0.13288 0.23066 -0.88434 -0.37935 -0.84542 0.42759 0.29187 -0.31587 0.16513 -region, -0.43459 -0.081776 0.20824 -0.97695 0.065396 -0.91439 0.40789 0.10106 -0.1185 0.03497 -stopped -0.45032 -0.19204 0.24023 -0.98554 0.14211 -0.917 0.3598 -0.08316 -0.10664 -0.0010918 -recorded -0.30823 -0.098121 0.14835 -0.89809 0.33321 -1.0245 0.4314 0.073016 -0.13257 -0.15369 -facility -0.3068 -0.10924 0.11019 -1.0301 0.048937 -0.88394 0.35113 -0.0053479 -0.17994 0.1186 -seekers. -0.42313 -0.39104 0.10196 -0.92719 0.033158 -0.72428 0.49647 -0.058764 -0.12636 0.32512 -Andy -0.4128 -0.086311 0.067352 -0.93239 0.0876 -1.046 0.39893 -0.0093731 -0.030056 -0.063049 -Team -0.13118 0.083289 0.27665 -0.98178 0.24685 -1.0792 0.53747 0.19225 -0.1943 -0.18648 -they're -0.26309 -0.20177 0.16846 -0.99154 -0.19031 -0.78186 0.40193 0.048799 -0.25378 0.31591 -Argentina's -0.38195 -0.069379 0.077963 -0.86022 0.37379 -1.0591 0.51619 0.073333 -0.11085 -0.10816 -operation -0.45542 -0.28483 0.031488 -0.85024 0.36917 -1.0292 0.56994 0.27726 -0.20864 -0.020713 -1 -0.061946 -0.036152 0.088015 -0.94501 -0.56587 -0.69479 0.33479 0.057671 -0.39652 0.54583 -company's -0.24646 -0.25499 0.12094 -0.8555 -0.065341 -0.87628 0.4523 0.11422 -0.23467 0.20347 -above -0.28101 -0.062013 0.3105 -0.96122 0.42124 -0.9899 0.41511 0.071726 -0.21365 -0.13909 -Zimbabwe -0.19476 -0.041701 0.14611 -0.90516 -0.14532 -0.86157 0.48187 0.11272 -0.32375 0.21594 -lost -0.39719 -0.26721 0.35998 -1.033 -0.022575 -0.68873 0.37287 0.052676 -0.30927 0.21901 -business -0.34458 -0.21672 0.054013 -0.89455 0.43415 -1.0089 0.56624 -0.071759 -0.13534 -0.0083832 -Four -0.18358 -0.19732 0.13511 -0.92191 -0.021036 -0.79206 0.3596 -0.030203 -0.32091 0.19374 -Airlines -0.2057 -0.18542 0.25906 -0.86197 -0.21638 -0.78212 0.42764 0.14729 -0.29109 0.19843 -potential -0.13066 -0.045827 0.19466 -0.88089 0.11807 -1.0111 0.46835 0.29623 -0.22057 0.011827 -treated -0.11018 -0.10551 0.28109 -0.95026 0.094927 -0.93671 0.37664 0.17521 -0.24142 0.098328 -Another -0.14594 -0.33391 0.35765 -1.0676 0.011197 -0.74568 0.34726 -0.013992 -0.19294 0.28499 -little -0.186 -0.083573 0.19209 -1.0366 -0.41438 -0.70943 0.33344 0.066882 -0.24142 0.41275 -tape -0.16302 0.10371 0.14223 -1.0517 -0.060557 -0.82127 0.36696 0.13335 -0.34453 0.074391 -lung -0.29184 0.0063357 0.070592 -0.87495 0.083939 -1.0114 0.55092 0.11732 -0.23754 -0.0058427 -fell -0.37211 0.012797 0.082664 -1.0298 0.13061 -0.93115 0.46598 -0.10459 -0.30063 -0.10094 -greater -0.21466 0.0079097 0.25273 -0.95012 -0.054123 -0.80416 0.44873 0.032628 -0.24437 0.34358 -done -0.014793 -0.0067683 0.086637 -0.97805 -0.033787 -0.92564 0.40541 0.02535 -0.19899 0.024775 -out. -0.50085 -0.27089 0.22356 -0.92793 -0.30325 -0.74575 0.29876 0.02653 -0.15032 0.28228 -organisation -0.34536 -0.10234 0.087125 -0.87965 0.15625 -0.92826 0.50642 0.15174 -0.2366 0.08671 -suspect -0.31396 -0.15012 0.17595 -1.0813 0.14394 -0.76811 0.37105 -0.17661 -0.23903 0.048445 -sentence -0.44284 -0.21888 0.052154 -0.90839 0.26553 -0.95631 0.56531 0.080551 -0.10793 -0.038337 -ask -0.34655 -0.11991 0.10353 -0.88798 0.2319 -1.0485 0.52059 0.19465 -0.28768 -0.14825 -incident -0.28375 -0.29921 0.1971 -0.86209 0.067924 -0.95959 0.4549 0.20993 -0.16057 0.085392 -Williams, -0.39511 -0.083276 0.081542 -0.97308 0.1662 -0.90865 0.4451 -0.083423 -0.24119 -0.028967 -3,000 -0.19204 -0.10646 -0.066139 -1.0132 -0.034052 -0.84198 0.47702 -0.044811 -0.21841 0.096795 -greatest -0.044005 -0.039616 0.16731 -0.97915 0.11921 -0.90514 0.52257 0.15667 -0.41196 0.20309 -Affairs -0.31136 -0.050722 0.026881 -0.87813 0.12223 -0.99836 0.53118 -0.032373 -0.19953 -0.037852 -freeze. -0.40264 -0.1233 0.077674 -0.86703 -0.018707 -0.86975 0.42649 0.14071 -0.21533 0.056504 -Doug -0.35976 -0.12714 0.11614 -0.90225 -0.14585 -0.85466 0.44812 -0.090822 -0.21548 0.21651 -Washington, -0.21142 0.11706 0.04237 -1.0057 0.17401 -0.93255 0.48629 0.046844 -0.30952 -0.08279 -spokeswoman -0.34143 -0.12734 0.20297 -0.95821 0.30567 -1.0166 0.45681 0.073869 -0.12169 -0.105 -appears -0.25347 -0.17913 0.09815 -0.98478 0.077997 -0.87975 0.39589 -0.024444 -0.1752 0.1295 -custody -0.38955 -0.10267 0.06916 -0.8303 0.18757 -1.0082 0.46587 -0.060263 -0.22609 -0.062007 -battling -0.11528 -0.076738 0.16785 -0.95093 -0.11965 -0.85803 0.47492 0.13973 -0.37859 0.062873 -giant -0.19474 -0.19235 0.12134 -0.95219 -0.040241 -0.88972 0.45739 0.019493 -0.1236 0.22679 -clearly -0.26136 -0.092334 0.22652 -0.97505 0.044356 -0.87222 0.45145 -0.10398 -0.31415 0.071187 -related -0.15942 -0.098811 0.056044 -0.88749 0.39262 -1.0702 0.53376 0.13411 -0.21892 -0.090192 -grant -0.2401 -0.14062 0.23954 -0.9333 0.071514 -0.91665 0.45179 0.081185 -0.11462 0.067239 -Perth -0.11122 0.03106 0.056859 -1.0016 0.31666 -0.97628 0.5172 -0.039563 -0.18637 -0.090776 -ceremony -0.14673 -0.22008 0.19464 -0.8457 -0.14645 -0.84613 0.456 0.33116 -0.29061 0.20251 -read -0.42176 -0.10929 0.18953 -0.92471 -0.1598 -0.78806 0.49239 -0.010597 -0.19399 0.16819 -nice -0.23009 -0.043847 -0.044621 -0.85689 -0.28957 -0.8679 0.40117 0.060147 -0.23787 0.36475 -charges -0.13571 -0.20754 0.25891 -0.91337 0.020319 -0.92752 0.29293 0.10693 -0.18216 0.11836 -singles -0.32386 0.072627 0.089566 -1.0061 0.0050874 -0.99319 0.54827 -0.032294 -0.22585 -0.050169 -tough -0.51517 0.01907 -0.059756 -0.91604 0.012925 -0.99575 0.56298 -0.19045 -0.12457 0.042528 -pilot -0.17787 -0.21168 0.19167 -1.0186 0.092057 -0.87213 0.42648 0.12392 -0.22186 0.18636 -Interlaken -0.31132 -0.028992 0.12936 -0.77558 -0.010251 -0.95471 0.45878 0.085206 -0.32529 -0.035028 -program -0.3165 -0.194 0.043699 -0.90067 -0.09108 -0.88954 0.41511 0.01183 -0.21771 0.11704 -possibility -0.17417 -0.22635 0.20271 -1.0031 0.16762 -0.94624 0.34064 0.076236 -0.21006 0.0031932 -finding -0.30603 -0.036248 0.20905 -0.89751 -0.20761 -0.84353 0.45065 0.054111 -0.33882 0.0029376 -now, -0.14095 -0.023827 0.24233 -0.97745 -0.30549 -0.70309 0.36732 -0.0015611 -0.35641 0.29118 -tomorrow -0.088968 -0.047691 0.21455 -0.88194 -0.08835 -0.9363 0.43395 0.30085 -0.30457 0.15748 -unity -0.3308 -0.27777 0.24292 -0.88044 0.088659 -0.96242 0.39934 0.30319 -0.17101 0.09274 -volunteers -0.26174 -0.057436 0.089196 -0.96116 0.074692 -0.85164 0.4566 -0.0054521 -0.20545 0.028282 -Assa -0.28009 -0.15646 0.12637 -0.92162 0.1677 -0.92001 0.53836 0.060919 -0.24258 -0.024064 -created -0.080767 0.047301 0.21443 -0.88478 0.14305 -0.98661 0.42311 0.14744 -0.27115 -0.017732 -wall -0.099802 -0.014132 0.060633 -0.85707 -0.038637 -0.96233 0.37083 0.19281 -0.28115 0.16652 -coach -0.16856 -0.29836 0.23797 -0.90876 -0.11726 -0.84583 0.42414 0.1564 -0.25516 0.15827 -recovery -0.20675 -0.18212 0.19319 -0.89779 -0.064241 -0.85309 0.38424 0.17494 -0.21933 0.17171 -Switzerland -0.079992 -0.13598 0.18184 -0.92639 0.054323 -0.93894 0.37569 0.18704 -0.28502 0.12325 -enter -0.45322 -0.01919 0.18559 -0.79178 0.14156 -1.0558 0.60923 0.11858 -0.19179 -0.02854 -doubt -0.32574 0.053422 0.0062525 -0.9487 -0.018581 -0.97107 0.45578 -0.15671 -0.24103 -0.0065333 -cause -0.50551 -0.12227 0.088099 -1.0827 0.016891 -0.88276 0.37197 -0.12661 -0.062636 0.15792 -crowd -0.40782 -0.23396 0.12535 -1.0146 0.051628 -0.88484 0.3862 -0.14947 -0.070023 0.21455 -students -0.19614 -0.093446 0.18796 -1.0112 0.32153 -0.915 0.4746 0.072986 -0.18092 0.036884 -yachts -0.10043 -0.13598 0.28857 -0.93148 0.16356 -0.94541 0.56432 0.17456 -0.27281 0.16088 -mountain -0.5485 -0.088163 0.27787 -1.0609 0.43525 -0.9536 0.4235 -0.0033296 -0.087574 -0.17432 -oil -0.49401 0.027284 0.086111 -0.77788 -0.26105 -0.89366 0.44204 0.070293 -0.21487 -0.035968 -names -0.12431 -0.10413 0.14273 -0.95384 0.0011781 -0.79293 0.37917 -0.00022532 -0.25695 0.13455 -Eve 0.026754 0.033638 0.13392 -0.94275 -0.146 -0.70248 0.46788 0.14457 -0.42444 0.19181 -boats -0.0297 -0.14933 0.3111 -1.04 0.14434 -0.76292 0.42072 0.15491 -0.32255 0.20952 -Philip -0.19464 -0.10752 0.22174 -0.94747 0.077592 -0.9701 0.41533 0.13309 -0.25159 -0.020048 -While -0.32228 -0.12627 0.1897 -0.89911 0.094805 -0.92193 0.48529 0.14728 -0.31592 0.074455 -property -0.24023 -0.26967 0.17315 -0.96327 -0.46607 -0.69738 0.31227 0.11607 -0.24229 0.4851 -River. -0.2681 -0.16975 0.18522 -0.90112 -0.08442 -0.99434 0.40489 0.13814 -0.1938 0.15909 -acting -0.53013 -0.14158 -0.054806 -0.86516 -0.18841 -0.89288 0.42395 0.0077935 -0.24085 -0.023856 -attacks, -0.26184 -0.064943 0.076111 -1.135 1.1445 -1.2878 0.43577 -0.25375 -0.021592 -0.66229 -80 0.19687 -0.0052728 0.11849 -0.99997 -0.26229 -0.74572 0.24241 0.16065 -0.50429 0.38195 -them," -0.12137 -0.21374 0.13169 -1.0482 -0.045658 -0.7064 0.35385 -0.059479 -0.23442 0.25225 -verdict -0.37523 -0.14589 0.21297 -0.90034 0.0082628 -0.88882 0.44354 -0.023509 -0.11883 0.14185 -together -0.41599 -0.11771 0.11297 -0.89298 -0.35122 -0.78593 0.44979 -0.018914 -0.2029 0.32439 -apparently -0.28669 -0.14803 0.16608 -0.90586 -0.11703 -0.78704 0.45045 0.14256 -0.27331 0.16938 -aboard -0.22315 -0.15286 0.22083 -0.9105 0.11552 -0.92815 0.47306 0.053682 -0.17567 0.17077 -area, 0.060098 -0.28676 0.48758 -1.1426 -0.19647 -0.58428 0.30434 0.084059 -0.22395 0.62962 -affected -0.31576 -0.14433 0.15533 -0.90465 0.35061 -1.0104 0.42994 0.02733 -0.076688 -0.10059 -reveal -0.29761 -0.14128 0.13282 -0.90104 -0.13577 -0.93424 0.41919 0.13418 -0.17131 0.14438 -Firefighters -0.14621 -0.1608 0.39446 -1.0887 0.32588 -0.87854 0.47296 0.21558 -0.23373 0.026986 -squad -0.11952 0.19318 0.11819 -1.0138 0.35206 -0.94827 0.57597 0.016621 -0.25446 -0.094914 -swept -0.254 -0.38474 0.3558 -0.89072 -0.2463 -0.79614 0.21236 0.2282 -0.14143 0.40372 -played -0.12248 -0.10612 0.044413 -0.99036 0.062518 -0.93714 0.40971 0.03221 -0.22713 0.076656 -agreed -0.47498 -0.17392 0.11952 -0.93832 0.12854 -1.0205 0.44601 0.026194 -0.083695 -0.075291 -hope -0.20079 -0.22468 0.37713 -1.0259 -0.34884 -0.56551 0.30759 0.068423 -0.3139 0.42521 -Hicks, -0.49856 -0.19744 0.00187 -0.99613 0.52396 -1.0283 0.50948 -0.19591 0.031717 -0.14309 -ready -0.30419 -0.20335 0.17166 -0.85772 -0.46007 -0.75412 0.38355 0.1726 -0.12917 0.40266 -department -0.29198 -0.26417 0.19732 -0.83206 0.12443 -1.0298 0.50087 0.37477 -0.10588 -0.0049605 -doubles -0.18303 0.085722 0.041611 -0.91097 -0.12315 -0.87427 0.58882 0.046346 -0.31335 0.081859 -Gillespie -0.21623 0.065488 0.036796 -0.91373 0.032026 -0.98446 0.523 0.035139 -0.25906 0.014605 -scored -0.17742 0.027689 0.062962 -0.92025 0.35127 -1.0697 0.48265 0.086444 -0.25782 -0.10568 -conflict -0.10585 -0.21113 0.17809 -0.99384 0.22581 -0.88548 0.41597 0.18941 -0.15521 0.13629 -dropped -0.32043 -0.087391 0.11451 -0.94564 0.29418 -1.0107 0.48245 0.031455 -0.16764 -0.093651 -years, -0.0011375 -0.20937 0.11103 -0.99005 0.27352 -0.91087 0.54879 0.21073 -0.28111 0.15536 -Fatah -0.42844 -0.21625 0.049941 -0.90511 0.30765 -1.0389 0.42033 -0.096833 -0.06178 -0.086817 -Friedli -0.31514 -0.2202 0.13265 -0.9082 0.12679 -0.93712 0.36831 0.074354 -0.084924 0.047795 -old -0.69719 -0.18566 0.070685 -1.0067 0.10258 -0.97605 0.44238 -0.1796 -0.052982 0.089902 -Transport -0.15508 -0.11403 0.15107 -0.92124 0.092459 -0.8699 0.4467 0.1271 -0.29248 0.15401 -agency -0.30065 -0.21441 0.035038 -0.91231 0.56437 -1.0848 0.57941 0.034436 -0.11448 -0.15499 -follows -0.017571 0.10862 0.0052477 -0.90609 0.073521 -1.0277 0.5345 0.17643 -0.28208 0.019561 -streets -0.16264 -0.015076 0.17619 -0.93528 0.30503 -0.97626 0.52447 0.13421 -0.26039 0.029433 -debt -0.29361 0.01649 0.11836 -0.9269 -0.1014 -0.8797 0.37439 -0.051927 -0.20218 0.14944 -factions -0.66215 -0.3437 0.0784 -0.91268 0.086813 -0.84447 0.43038 0.10023 -0.11111 0.12388 -dozens -0.020118 -0.041503 -0.0025768 -0.944 0.048652 -0.88489 0.45687 0.10902 -0.31275 0.12495 -Hundreds -0.18794 -0.071425 0.086088 -1.0112 0.47302 -1.0115 0.48967 0.04175 -0.17609 -0.1515 -capital, -0.12553 -0.055046 0.031502 -1.019 0.64319 -1.1797 0.51824 0.0011017 -0.14031 -0.23638 -fierce -0.32886 -0.061441 -0.0085193 -0.97711 0.18292 -0.86985 0.49664 -0.058466 -0.14711 0.11091 -Sunday -0.2073 -0.0074925 0.04898 -0.92262 0.16386 -1.0042 0.49381 -0.054376 -0.23898 -0.070552 -2001 -0.34211 -0.11376 0.19554 -0.89605 0.34288 -0.93222 0.43281 0.0060886 -0.18179 -0.055397 -attempting -0.20869 -0.03348 -0.12391 -0.94975 0.37949 -1.0764 0.48798 -0.10882 -0.1925 -0.18612 -races -0.012184 0.16111 0.060192 -0.99748 0.41683 -1.0134 0.55748 0.12327 -0.26316 -0.13532 -prior -0.39419 -0.24487 0.17659 -0.9468 0.046177 -0.91649 0.39508 -0.010464 -0.17617 0.29622 -Japanese -0.24729 -0.2236 0.2223 -0.88269 0.19642 -0.9013 0.48408 0.14182 -0.19541 -0.016586 -domestic -0.090836 -0.11496 0.22062 -0.94755 -0.021954 -0.86699 0.48926 0.26807 -0.34971 0.16311 -Internet -0.44494 -0.081498 0.22393 -0.86936 0.25624 -1.0817 0.53033 0.019088 -0.15582 -0.081715 -spread -0.34286 0.0085029 0.12235 -0.96069 -0.014924 -0.85794 0.43675 -0.0030413 -0.14867 0.16879 -create -0.16881 0.0015007 0.25886 -0.89012 -0.22962 -0.83982 0.39156 0.25736 -0.3609 0.21564 -playing -0.30897 -0.044987 -0.018034 -0.89053 -0.18541 -0.96471 0.48865 -0.052599 -0.31886 -0.044458 -growing 0.019896 -0.049953 0.18254 -0.98348 -0.16335 -0.84057 0.47503 0.18023 -0.3839 0.12714 -scheduled -0.3439 -0.11101 0.15367 -0.98378 0.19435 -1.0042 0.41828 0.024073 -0.067868 -0.007766 -factory -0.45105 -0.087521 0.030059 -0.97854 -0.027822 -1.0114 0.44714 -0.057401 -0.21847 0.092436 -knowledge -0.3498 -0.13386 0.19503 -0.9762 0.07868 -0.96534 0.3975 -0.048246 -0.17176 0.031962 -save -0.38017 -0.23925 0.33928 -0.94021 -0.35619 -0.73018 0.26854 -0.049871 -0.066799 0.41109 -holiday -0.17117 0.021268 0.12256 -0.95257 0.54454 -0.99889 0.51952 0.066868 -0.25515 -0.20265 -Timor -0.086932 -0.14251 0.27041 -0.80165 0.1293 -0.96345 0.48208 0.44571 -0.29487 0.001174 -Thursday -0.16804 0.032178 0.17198 -0.89441 0.064809 -0.94998 0.5231 0.14831 -0.32066 -0.046057 -recent -0.14141 -0.21349 0.34583 -0.80142 0.047077 -0.96236 0.44747 0.59265 -0.15062 0.12291 -revealed -0.311 -0.1451 0.11148 -0.9362 0.070507 -1.0092 0.43316 0.023024 -0.12374 0.015503 -rain -0.71506 0.070684 0.009715 -0.93109 0.32285 -0.98704 0.52257 -0.17777 -0.1588 -0.21132 -Professor -0.34146 -0.047432 0.047709 -0.85468 -0.34078 -0.80689 0.38297 0.011803 -0.29741 0.29715 -"But -0.23533 -0.072159 0.11926 -0.96619 -0.25722 -0.77987 0.46747 -0.052188 -0.22124 0.32704 -statement. -0.25781 -0.23333 0.21852 -1.0081 0.55942 -1.0465 0.4925 -0.026883 -0.06044 -0.095841 -Solomon -0.1112 -0.17747 0.30167 -0.94309 0.1185 -0.95132 0.39373 0.2722 -0.16205 0.048385 -organisations -0.31664 -0.15481 0.086478 -0.91277 0.24868 -0.91472 0.51023 0.1276 -0.26093 0.067923 -runs -0.081029 0.15482 0.10812 -0.95525 0.16914 -0.87168 0.50043 0.24514 -0.40289 -0.089365 -respond -0.12951 -0.13325 0.25617 -0.99682 -0.036371 -0.84081 0.34943 0.22771 -0.22782 0.15075 -Michael -0.27256 -0.1403 0.14789 -0.84897 0.11625 -0.89961 0.44581 -0.021928 -0.093597 0.08034 -When -0.3826 -0.067863 0.1181 -0.95835 -0.24689 -0.80123 0.4205 -0.12366 -0.28273 0.26885 -40 -0.080944 -0.030479 0.48167 -1.0393 -0.23531 -0.65996 0.23723 0.1509 -0.12914 0.17171 -Hayden -0.26217 0.13508 0.056591 -1.0181 0.060048 -0.92058 0.46848 -0.06749 -0.26086 -0.062866 -attack. -0.2187 -0.054543 0.15759 -1.1832 0.91744 -1.218 0.35545 -0.205 -0.083368 -0.48719 -Earlier -0.35822 0.034622 0.045342 -0.84545 0.2501 -1.0562 0.53407 -0.14539 -0.2124 -0.041862 -Indonesia -0.13118 0.068667 0.079238 -0.90011 -0.074519 -0.92243 0.52518 0.13945 -0.25327 0.099072 -Sarah -0.29148 -0.083626 0.10898 -0.96221 0.095324 -0.90043 0.48371 0.16895 -0.13041 0.13251 -detain -0.35919 -0.070197 0.25744 -0.8393 0.011666 -0.98708 0.45426 0.19846 -0.17789 0.032611 -Neil -0.4231 0.11533 0.060575 -0.94699 -0.31259 -0.88055 0.5079 0.012363 -0.18818 0.047914 -states 0.035746 -0.23303 0.46542 -1.061 0.50206 -0.87712 0.40336 0.011969 -0.23725 0.047316 -4,000 -0.062591 0.016686 -0.018896 -0.91001 -0.16483 -0.83083 0.41536 0.25611 -0.34508 0.053079 -things -0.051032 -0.15411 0.11416 -0.91539 -0.13999 -0.74606 0.36922 0.042876 -0.36282 0.21292 -toll -0.051031 -0.074063 -0.050378 -0.81443 -0.16458 -0.86624 0.50923 0.13427 -0.31809 0.16723 -you're -0.059447 -0.12139 0.1556 -1.0472 -0.35454 -0.73766 0.27145 0.065707 -0.33578 0.33058 -felt -0.43935 -0.029357 0.056699 -1.0074 -0.012995 -0.77477 0.40277 -0.15507 -0.20878 0.065392 -deployed 0.053162 -0.1647 0.22864 -0.97735 0.35141 -0.99622 0.42883 0.28513 -0.2113 0.045679 -Hamas, -0.33651 -0.27008 0.13846 -0.95793 0.30384 -0.96349 0.42221 0.066626 -0.14882 0.029652 -gun -0.19859 -0.12881 -0.085064 -0.77327 -0.15347 -1.0126 0.51969 -0.022194 -0.37624 -0.040505 -Senior -0.22561 -0.12378 0.17376 -0.97379 0.44761 -0.99742 0.52638 -0.025954 -0.082844 -0.089523 -plan -0.40868 -0.099606 0.13781 -0.87832 0.010708 -0.91702 0.48558 -0.034796 -0.17617 0.13689 -elected -0.13135 0.04798 0.14718 -0.88991 0.044746 -0.96795 0.40639 0.12036 -0.23581 -0.0068139 -government, -0.27946 -0.2585 0.1293 -0.85924 0.25008 -1.0282 0.60002 0.2802 -0.16703 0.092715 -north. 0.11999 0.082092 0.12268 -1.0197 0.096272 -0.83958 0.56722 0.2645 -0.31074 0.18826 -tailenders -0.37816 -0.044353 -0.0033328 -0.92389 0.26644 -1.0267 0.46643 -0.02207 -0.17795 -0.20222 -B-52 -0.33645 -0.14164 -0.034415 -1.0395 0.44756 -0.93329 0.52896 -0.1543 -0.19566 -0.048659 -advice -0.019011 -0.080755 0.13782 -1.0027 -0.14265 -0.75955 0.37166 0.25948 -0.27782 0.3537 -continues -0.31877 -0.08533 0.23952 -1.0301 0.1736 -0.92963 0.4279 0.22104 -0.16045 0.032999 -Lording -0.44379 -0.31877 -0.02095 -0.89717 0.27762 -1.0258 0.49945 -0.15816 -0.072751 -0.17291 -body -0.27807 -0.043922 -0.064788 -0.85849 0.4291 -1.0043 0.5301 -0.15222 -0.14431 -0.10526 -died. -0.28004 -0.15246 -0.050408 -0.89943 0.25872 -1.1047 0.51751 0.027965 -0.16932 0.011267 -Melbourne, -0.16467 -0.0016058 0.15283 -0.917 -0.25817 -0.83193 0.39381 0.033862 -0.38796 0.1885 -activity -0.4133 -0.23608 0.042298 -0.90558 0.024862 -0.93733 0.37843 0.10265 -0.13713 0.092931 -Krishna -0.37526 -0.19429 0.26769 -0.98535 0.13161 -0.93223 0.42722 -0.041666 -0.18585 0.028535 -crossed -0.070605 -0.052971 0.25836 -1.0456 0.26535 -1.0064 0.47807 0.18456 -0.31118 -0.08613 -described -0.21603 -0.1074 0.13553 -0.93613 0.040677 -0.93754 0.39861 0.10401 -0.22784 0.060417 -suffered -0.24294 -0.18708 0.019853 -0.95508 0.11403 -0.92028 0.46586 -0.0031084 -0.14647 0.12232 -500 -0.10623 0.18396 -0.090181 -1.01 0.1596 -0.86083 0.49228 0.10059 -0.22581 -0.014477 -militants. -0.45571 -0.19868 0.017953 -0.93778 0.71889 -1.1236 0.51809 -0.12618 -0.058003 -0.21578 -rescue -0.26812 -0.02935 0.10839 -0.92493 -0.17213 -0.96675 0.37002 0.15285 -0.25694 0.13041 -walk -0.22773 0.038052 -0.11351 -0.80286 0.32745 -1.1318 0.54419 0.0089137 -0.22752 -0.081451 -That -0.1364 -0.029955 0.15664 -0.88105 -0.49632 -0.69868 0.40827 0.12486 -0.43356 0.32152 -diplomatic -0.25385 -0.12728 0.056069 -0.94035 0.0083836 -0.90342 0.41262 -0.015823 -0.21035 0.12273 -directors -0.077749 -0.14422 0.21524 -0.95906 -0.036602 -0.91827 0.31253 0.2058 -0.25 0.12023 -concern -0.30573 -0.29248 0.31705 -0.99877 0.075426 -0.84557 0.39936 0.22679 -0.052918 0.24857 -Ricky -0.53879 -0.0035531 0.062384 -1.0496 0.017301 -0.8555 0.45923 -0.17582 -0.26331 -0.021441 -attacking -0.19124 -0.011663 0.046328 -1.0701 0.61407 -1.1884 0.46141 -0.13146 -0.16939 -0.36588 -handed -0.32916 -0.28368 0.13324 -1.0037 0.43082 -1.022 0.45323 -0.087054 -0.061207 -0.075076 -edge -0.030324 0.011877 0.20697 -0.91813 0.29694 -1.065 0.38935 0.097339 -0.26136 -0.037787 -weekend. -0.24846 -0.18026 0.12221 -0.98336 0.063706 -0.77877 0.3955 -0.036036 -0.26551 0.25798 -why -0.54003 -0.17741 0.15215 -0.98643 0.38637 -1.0131 0.40792 -0.3226 -0.1438 0.0090477 -country. -0.33879 -0.12475 0.19287 -0.88116 0.034972 -0.88351 0.47031 0.21101 -0.26327 0.064972 -promised -0.33058 -0.19621 0.14678 -0.87526 0.24515 -1.0454 0.45322 0.11116 -0.14254 -0.030859 -Radio -0.40828 -0.053057 0.12387 -0.9347 0.2561 -0.9069 0.5037 -0.12057 -0.10187 -0.042171 -According -0.40444 -0.16357 0.086887 -0.89811 0.32539 -1.0539 0.52773 -0.041895 -0.20015 -0.28409 -investigating -0.17936 -0.11615 0.037612 -0.88204 0.20279 -1.0224 0.50793 0.16582 -0.24511 -0.019877 -Sydney's 0.26561 0.12441 0.31024 -1.0951 0.18804 -0.78061 0.58081 0.22754 -0.37633 0.18482 -civil -0.27172 -0.091949 0.087172 -1.0009 0.018191 -0.81623 0.45942 0.026575 -0.21189 0.17785 -Ministry -0.67236 -0.23749 0.14073 -0.8914 0.29613 -1.0714 0.49386 -0.16486 0.15936 0.069926 -Pakistan. -0.862 -0.55687 0.208 -1.0142 0.66765 -1.1684 0.38819 -0.14924 0.07302 -0.32486 -blaze -0.45721 -0.094917 0.039373 -1.0435 0.53644 -1.008 0.48946 -0.11726 -0.058373 -0.1909 -form -0.34916 -0.017953 -0.015494 -0.90177 0.14814 -1.154 0.44586 0.098764 -0.18015 -0.20346 -showed -0.29118 -0.22855 0.27023 -1.0458 0.11953 -0.83314 0.3252 -0.025419 -0.054188 0.13709 -field -0.18158 -0.17807 0.31801 -0.99627 -0.10492 -0.7835 0.45081 0.068907 -0.17304 0.29566 -period -0.24461 -0.075453 -0.0059673 -0.87738 0.42241 -1.0123 0.52005 0.092256 -0.21545 -0.044755 -action. -0.65352 -0.39539 0.19935 -0.9616 -0.052078 -0.81066 0.31832 0.10863 -0.05101 0.15773 -threatened -0.28797 -0.095794 0.1904 -0.98768 0.20751 -0.9698 0.3816 -0.079589 -0.1922 0.073733 -game -0.19809 -0.0086769 -0.066625 -0.93597 -0.36786 -0.73464 0.42883 0.018447 -0.48203 0.24025 -open -0.30496 0.025563 -0.024002 -0.92346 0.24914 -1.0943 0.49335 -0.27707 -0.17528 -0.25764 -shows -0.24444 0.016777 0.12173 -0.95246 0.04824 -0.87266 0.541 0.10345 -0.3015 -0.0094383 -hospital. -0.0014781 -0.058618 0.13286 -0.97958 0.23553 -0.93121 0.53765 0.15109 -0.27042 0.060958 -largest -0.20056 -0.048746 0.099227 -0.95552 0.37308 -1.0641 0.49084 0.073491 -0.37554 -0.077533 -responsible 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0.404 0.10336 -0.19082 0.18 -crashed -0.28983 -0.19277 0.28008 -1.0251 0.36846 -1.0111 0.34961 0.023464 -0.10933 -0.062239 -natural -0.07196 -0.27232 0.38912 -0.98727 -0.1257 -0.81443 0.29644 0.32534 -0.29026 0.20611 -begin -0.67268 -0.064473 -0.036203 -1.0396 0.39318 -1.0634 0.52304 -0.39897 -0.10272 -0.20808 -Up -0.12987 -0.31596 0.37794 -0.92846 0.34058 -0.87118 0.55491 0.18954 -0.065122 0.20761 -celebrations -0.35697 -0.24729 0.14783 -0.91635 0.17408 -0.84252 0.47401 0.19342 -0.26351 0.14232 -reject -0.36215 -0.083687 0.019168 -0.88026 -0.34821 -0.80624 0.35637 0.060272 -0.22834 0.17106 -options -0.43006 -0.32979 0.099825 -0.8432 0.28397 -0.94099 0.45111 0.18693 -0.12492 0.077929 -single -0.36768 -0.0077289 0.12405 -0.89668 -0.13589 -0.91868 0.43771 0.023178 -0.19012 0.020062 -handling -0.19383 -0.086433 0.073078 -0.84088 -0.022429 -0.96951 0.52248 0.18913 -0.26988 -0.062818 -match. -0.10931 0.18295 -0.0038214 -0.83137 -0.24983 -0.95995 0.53487 0.20493 -0.49541 -0.1036 -summit 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-0.82546 0.47717 -0.017943 -0.095205 0.12177 -prepare -0.27096 -0.23845 0.17099 -0.88899 -0.17308 -0.79854 0.33358 0.18857 -0.27852 0.22166 -champion -0.37096 -0.075525 0.033513 -0.87721 0.032021 -1.0175 0.42515 0.086005 -0.22466 0.0095515 -Pollock -0.17072 0.075854 0.034762 -0.99117 0.025959 -0.84027 0.51192 0.054958 -0.34076 0.13271 -television. -0.44044 -0.21914 0.064641 -0.95829 0.15258 -0.9742 0.43971 -0.15748 -0.021531 0.11953 -begun -0.44643 -0.063631 0.062254 -0.89464 -0.26594 -0.77289 0.43425 -0.022602 -0.29919 0.059812 -coast -0.096154 -0.098287 0.25499 -0.86603 0.41534 -0.9861 0.60581 0.24712 -0.30546 -0.11746 -leave -0.48798 -0.14912 0.15468 -1.0082 0.28873 -0.94628 0.46997 -0.14209 0.015677 0.16177 -St -0.15052 -0.038151 0.074615 -0.94029 0.62285 -0.93937 0.64598 0.084345 -0.21029 -0.10022 -Sydney. 0.26468 0.080405 0.32812 -1.0438 0.026161 -0.72776 0.57149 0.22449 -0.45732 0.20119 -losing -0.30082 -0.14351 0.13768 -0.94164 -0.073193 -0.87297 0.40935 -0.035951 -0.20497 0.083264 -work. -0.33936 -0.20084 0.24815 -1.0307 0.14337 -0.84725 0.4851 0.038437 -0.18419 0.15475 -counts -0.18804 -0.080086 0.15436 -0.93251 0.17173 -0.92948 0.56095 0.42776 -0.34996 0.025217 -26-year-old -0.23247 -0.16833 0.10058 -0.95923 0.12781 -0.89816 0.45605 0.021855 -0.15746 0.094273 -suggested -0.22162 -0.0057453 0.067216 -1.0141 0.46976 -1.0677 0.51246 -0.14477 -0.10022 -0.052623 -projects -0.38763 -0.26072 0.012426 -1.01 0.17994 -0.91495 0.52402 -0.090687 -0.25331 0.078616 -understood -0.39868 -0.13931 0.061138 -0.91247 0.04974 -0.9366 0.44174 -0.038707 -0.21788 0.022325 -various -0.42015 -0.30838 0.073304 -0.85863 0.37029 -1.0839 0.46362 0.028696 -0.00035373 -0.010771 -debate -0.12791 -0.10166 0.26514 -0.92209 0.13826 -0.92997 0.45138 0.26024 -0.26033 0.064546 -Bill -0.39838 -0.033529 0.094462 -0.87625 -0.2477 -0.78666 0.481 0.13671 -0.29902 0.21293 -happens -0.21991 -0.11012 -0.0028922 -0.9438 0.12635 -0.90128 0.55503 0.0095414 -0.22928 0.17408 -Commissioner -0.49284 -0.16213 0.12342 -0.90232 0.031972 -0.97126 0.46812 -0.01738 -0.030934 0.15521 -Deputy -0.36846 -0.16084 0.078157 -0.82351 -0.050522 -0.92733 0.5193 0.07008 -0.23301 0.11732 -civilians -0.26415 -0.15822 0.068405 -0.98221 0.50329 -1.0065 0.52038 -0.038207 -0.18997 -0.052481 -threatening -0.25182 -0.045476 0.10861 -0.89748 0.2866 -1.0112 0.42588 -0.062959 -0.30542 -0.11057 -women's -0.19161 -0.071387 0.19565 -1.0621 0.22142 -1.0313 0.47957 0.15791 -0.22421 0.099062 -containment -0.24544 -0.31776 0.35774 -1.0603 0.27667 -0.94787 0.45011 0.25615 -0.13414 0.083895 -stand -0.32437 -0.29159 0.29872 -0.96611 0.10026 -0.85177 0.31083 0.053036 -0.15696 0.15546 -MacGill -0.31908 0.030595 0.093019 -0.94111 -0.093146 -0.86372 0.49992 0.057094 -0.24437 0.11818 -putting -0.38698 -0.14535 0.03712 -0.90819 0.081707 -0.9084 0.42326 -0.057029 -0.26089 -0.021234 -determine -0.4176 -0.097098 0.14469 -0.93155 0.11262 -1.0324 0.43527 0.012073 -0.099626 0.040556 -Israel. -0.47031 -0.063841 -0.37841 -0.97351 1.1357 -1.3919 0.65755 -0.52246 0.1681 -0.27245 -forecast -0.19474 -0.079622 0.15075 -1.103 0.64718 -1.1086 0.38859 0.13428 -0.16916 -0.21521 -During -0.34549 -0.037444 0.073162 -0.8123 -0.10379 -0.97766 0.4863 0.040554 -0.3512 -0.056979 -bureau -0.18654 -0.084556 0.22349 -0.94008 0.17144 -0.88225 0.50717 0.042983 -0.26211 0.16669 -findings -0.15385 0.024256 0.097106 -0.90701 -0.0062973 -0.87097 0.45966 0.092639 -0.31016 0.048742 -fear -0.13347 -0.033548 0.051886 -1.0621 0.69087 -1.067 0.49215 -0.082951 -0.19496 -0.24658 -data -0.34699 -0.17287 -0.01897 -0.93141 0.24327 -0.91246 0.46874 -0.18289 -0.18046 0.081533 -gone -0.26956 -0.29501 0.25143 -1.0867 0.19435 -0.93538 0.33428 0.0059832 -0.084104 0.060041 -record -0.3523 0.061825 0.089374 -0.8624 0.10975 -0.93357 0.47165 0.088856 -0.19873 -0.037258 -hoping -0.18615 -0.052057 0.080318 -0.91618 0.10148 -0.88347 0.48114 -0.0095034 -0.35875 -0.1065 -Israelis. -0.23908 -0.12511 -0.23259 -1.0042 0.8512 -1.1663 0.54631 -0.34526 -0.007403 -0.12883 -Hamid -0.30894 -0.25919 0.073896 -0.86696 -0.060983 -0.83277 0.46301 0.015398 -0.12849 0.15373 -present -0.28584 -0.21305 0.1917 -0.82763 -0.2293 -0.86695 0.41269 0.28361 -0.18485 0.14715 -live -0.36521 -0.27784 0.21715 -1.0707 0.52157 -1.1198 0.27499 -0.065779 0.10911 -0.14034 -ahead. -0.18178 -0.16656 0.22029 -0.91903 0.26257 -0.96658 0.42367 0.15003 -0.19482 0.13553 -warning -0.057134 -0.10892 0.21894 -0.93471 0.075207 -0.93845 0.32891 0.098712 -0.29838 -0.10296 -trapped -0.39018 -0.17152 0.16364 -0.90232 0.39176 -1.0995 0.41738 -0.031247 -0.14006 -0.14978 -markets -0.22304 0.049703 0.023357 -0.89652 0.028235 -0.93915 0.54958 0.10373 -0.31918 -0.015176 -Sergeant -0.22138 -0.073868 0.18847 -0.89862 -0.12118 -0.88672 0.44595 0.059898 -0.20127 0.20083 -Seven -0.15834 0.012167 0.075471 -0.9344 -0.0060505 -0.87636 0.57467 -0.091161 -0.18515 0.16282 -firm -0.20924 0.13692 0.229 -1.0004 0.087825 -0.74557 0.46068 0.063433 -0.19736 -0.013484 -welcomed -0.33084 -0.13075 -0.031394 -0.933 0.12591 -0.93544 0.43404 -0.0071708 -0.19188 0.02752 -responding -0.27598 -0.17165 0.25794 -0.96352 -0.021708 -0.90139 0.36233 0.13162 -0.20719 0.013575 -law -0.23204 0.098585 0.084543 -0.87951 0.045764 -1.0408 0.39644 0.083872 -0.19391 0.06397 -deputy -0.35117 -0.094578 0.069262 -0.8022 0.21323 -1.0385 0.49627 0.14174 -0.18405 -0.036922 -unidentified -0.21788 -0.19765 0.16739 -0.96515 0.26464 -1.0681 0.45954 0.19657 -0.10865 -0.040348 -clashes -0.18525 -0.090192 0.13899 -0.92914 0.19958 -0.99479 0.48018 0.0076089 -0.22087 0.057853 -ago, -0.12196 -0.25092 0.21986 -0.78537 -0.43743 -0.83485 0.35358 0.5775 -0.36967 0.27372 -replied: -0.53654 -0.30794 0.0668 -0.95327 -0.32244 -0.86974 0.32233 0.0035099 -0.12836 0.27396 -path -0.29637 -0.0050163 -0.09227 -0.91761 0.61522 -1.2084 0.51978 -0.070516 -0.022066 -0.26732 -search -0.33437 -0.14509 0.16727 -0.8863 0.019863 -0.77068 0.47079 0.019677 -0.24613 0.18844 -hundred -0.025627 -0.11448 0.18116 -0.96779 0.2532 -0.93224 0.3929 0.12679 -0.19685 -0.13179 -state. -0.18528 -0.23642 0.26504 -1.0479 0.57506 -1.0035 0.41344 -0.17807 -0.09178 -0.023322 -efforts -0.2879 -0.13298 0.14961 -0.93882 0.13162 -0.93926 0.49649 0.17908 -0.18039 0.022261 -tree -0.13421 0.049938 0.039293 -0.85099 -0.054489 -0.95255 0.48989 0.20728 -0.25372 0.081726 -telephone -0.31696 -0.22565 0.13317 -1.0219 0.2304 -1.0449 0.39416 -0.015374 -0.058222 0.064146 -problem -0.23808 -0.055263 0.21232 -0.94406 -0.52183 -0.69284 0.40925 0.16283 -0.35028 0.39899 -approached -0.3755 -0.24085 0.045111 -0.96186 0.31842 -0.96877 0.4262 -0.091481 -0.06434 0.076188 -chairman -0.24927 -0.10477 0.17401 -0.90386 0.013637 -0.93125 0.41858 0.049651 -0.20749 0.15118 -Afroz -0.3492 -0.11356 0.046444 -0.96179 0.49956 -1.1739 0.50381 -0.029897 -0.15488 -0.24651 -Monday, -0.18335 0.075194 0.19071 -0.85914 -0.074304 -0.8325 0.45894 0.23434 -0.32902 0.11567 -advance -0.35487 -0.13718 0.083065 -1.0344 0.076132 -0.86051 0.39686 0.031053 -0.1538 0.12614 diff --git a/gensim/test/test_data/non_ascii_fasttext.vec b/gensim/test/test_data/non_ascii_fasttext.vec deleted file mode 100644 index ea179a2ff5..0000000000 --- a/gensim/test/test_data/non_ascii_fasttext.vec +++ /dev/null @@ -1,172 +0,0 @@ -171 2 -ji -1.5308 2.0551 -který -0.99211 1.4997 -jen -1.1228 1.3667 -podle -1.1469 1.4473 -zde -1.0191 1.4011 -už -0.91921 1.3531 -být -1.0086 1.4582 -více -1.1058 1.3376 -bude -1.2032 1.7383 -již -1.3136 1.4792 -než -1.0664 1.6635 -vás -1.1113 1.5703 -by -1.1698 1.966 -které -1.1295 1.6275 -co -0.93518 1.1776 -nebo -1.0791 1.5071 -ten -1.1881 1.415 -tak -1.4548 1.8457 -má -1.0658 1.5255 -při -1.3464 1.6107 -od -0.79486 1.5585 -po -1.2758 1.9186 -tipy -0.69335 1.0799 -ještě -0.87116 1.1618 -až -1.2688 1.6518 -bez -0.99627 1.423 -také -1.141 1.4808 -pouze -0.94181 1.4076 -první -1.1166 1.5035 -vaše -0.9672 1.4975 -která -1.1102 1.5806 -nás -1.1328 1.5253 -nový -0.85553 1.1462 -jsou -1.0792 1.8008 -pokud -1.0427 1.3178 -může -1.1269 1.419 -strana -0.84973 1.1957 -jeho -1.1644 1.5879 -své -1.0546 1.6185 -jiné -0.95046 1.2816 -zprávy -0.88762 1.3374 -nové -1.0588 1.619 -není -1.0321 1.5566 -tomu -1.0753 1.5211 -ona -1.21 1.6992 -ono -1.0733 1.6574 -oni -1.1153 1.643 -ony -1.0926 1.5244 -my -0.92689 1.6378 -vy -1.3708 1.8 -jí -1.205 1.6606 -mě -0.96436 1.4713 -mne -1.0956 1.6333 -jemu -1.1181 1.4661 -on -1.0062 1.4124 -těm -0.90732 1.2586 -těmu -0.90621 1.4096 -němu -1.0823 1.4396 -němuž -1.0786 1.3892 -jehož -1.1649 1.4418 -jíž -1.0574 1.6338 -jelikož -1.0449 1.3625 -jež -1.2657 1.7032 -jakož -1.3373 1.6112 -načež -1.0127 1.3696 -ze -1.1784 1.7095 -jak -1.2097 1.5224 -další -0.7288 0.96256 -ale -1.1029 1.4153 -si -1.1097 1.5884 -se -1.2981 1.7707 -ve -1.256 1.7985 -to -1.6894 2.2424 -jako -1.2333 1.5942 -za -1.0376 1.6162 -zpět -0.83657 1.354 -jejich -0.97548 1.4219 -do -0.93685 1.4001 -pro -1.4367 1.9498 -je -1.9446 2.5147 -na -1.5543 2.2901 -atd -0.98175 1.3697 -atp -0.83266 1.1085 -jakmile -1.0954 1.2764 -přičemž -1.0533 1.4279 -já -1.1496 1.4432 -nám -1.0246 1.6043 -jej -1.203 1.6252 -zda -0.93651 1.2363 -proč -0.90395 1.3144 -máte -0.99962 1.4802 -tato -1.3248 1.5575 -kam -0.63468 1.246 -tohoto -0.9737 1.3422 -kdo -0.88982 1.4152 -kteří -0.92973 1.4696 -mi -1.343 1.7217 -tyto -0.99375 1.3067 -tom -1.1636 1.608 -tomuto -1.0103 1.3488 -mít -1.1538 1.6326 -nic -0.76497 1.0685 -proto -1.1781 1.6367 -kterou -1.0561 1.563 -byla -0.9338 1.7033 -toho -1.1263 1.5702 -protože -1.1777 1.4984 -asi -1.0555 1.4401 -budeš -0.98208 1.5432 -s -1.3733 1.6447 -k -1.0223 1.6019 -o -1.4531 1.879 -i -1.0985 1.2956 -u -0.91038 1.6173 -v -1.2536 1.5998 -z -0.96962 1.7437 -dnes -0.92891 1.2478 -cz -0.84461 1.0881 -tímto -0.98475 1.3061 -ho -0.74774 1.4925 -budem -1.0178 1.4333 -byli -0.90776 1.4799 -jseš -1.0297 1.4975 -můj -0.891 1.2674 -svým -1.0586 1.5377 -ta -1.4932 2.0156 -tomto -1.1626 1.5135 -tohle -1.2215 1.6529 -tuto -1.0516 1.3583 -neg -0.94527 1.5529 -pod -1.0601 1.578 -téma -0.93273 1.3456 -mezi -0.96807 1.3465 -přes -1.1927 1.5099 -ty -1.3733 1.7374 -pak -1.0392 1.5592 -vám -0.89801 1.3586 -ani -1.2113 1.5634 -když -1.0124 1.5112 -však -0.75634 1.1299 -či -0.79489 1.2817 -jsem -1.0435 1.4903 -tento -1.0861 1.5053 -článku -0.93302 1.3758 -články -0.98897 1.4387 -aby -1.0874 1.6114 -jsme -1.0547 1.6846 -před -1.0538 1.5186 -pta -1.062 1.6063 -a -1.3116 2.0391 -aj -1.1578 1.5193 -naši -1.2075 1.3714 -napište -1.0436 1.4646 -re -1.3115 1.5453 -což -1.1731 1.3545 -tím -1.0296 1.5885 -takže -1.1014 1.3574 -svých -0.82606 1.1187 -její -1.1029 1.3696 -svými -1.1052 1.4953 -jste -1.1003 1.7465 -byl -0.89449 1.4131 -tu -1.1255 1.5505 -tedy -1.1693 1.6446 -teto -1.2134 1.546 -bylo -0.86091 1.3805 -kde -1.3468 1.7507 -ke -1.0699 1.6688 -pravé -0.9391 1.5172 -nad -1.3404 1.7661 -nejsou -0.85023 1.5033 From cebb3fc16c5d588879bc2c7b0b8f84a3deed64e4 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 28 Jun 2017 13:51:41 +0530 Subject: [PATCH 28/28] fixes utf8 bug in flake8_diff.sh script --- continuous_integration/travis/flake8_diff.sh | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/continuous_integration/travis/flake8_diff.sh b/continuous_integration/travis/flake8_diff.sh index 14b4432915..514ec3cd84 100755 --- a/continuous_integration/travis/flake8_diff.sh +++ b/continuous_integration/travis/flake8_diff.sh @@ -115,9 +115,10 @@ echo -e '\nRunning flake8 on the diff in the range' "$COMMIT_RANGE" \ echo '--------------------------------------------------------------------------------' # We ignore files from sklearn/externals. +# Excluding vec files since they contain non-utf8 content and flake8 raises exception for non-utf8 input # We need the following command to exit with 0 hence the echo in case # there is no match -MODIFIED_FILES="$(git diff --name-only $COMMIT_RANGE || echo "no_match")" +MODIFIED_FILES="$(git diff --name-only $COMMIT_RANGE -- . ':(exclude)*.vec' || echo "no_match")" check_files() { files="$1" @@ -133,6 +134,6 @@ check_files() { if [[ "$MODIFIED_FILES" == "no_match" ]]; then echo "No file has been modified" else - check_files "$(echo "$MODIFIED_FILES" )" "--ignore=E501,E731,E12,W503 --exclude=*.sh,*.md,*.yml,*.rst,*.ipynb,*.txt,*.csv,*.vec,Dockerfile*" + check_files "$(echo "$MODIFIED_FILES" )" "--ignore=E501,E731,E12,W503 --exclude=*.sh,*.md,*.yml,*.rst,*.ipynb,*.txt,*.csv,Dockerfile*" fi echo -e "No problem detected by flake8\n"