Add ST annotation to evaluators (UKPLab#2586)

sentence_transformers/evaluation/BinaryClassificationEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator
 import logging
@@ -111,7 +112,7 @@ def from_input_examples(cls, examples: List[InputExample], **kwargs):
             scores.append(example.label)
         return cls(sentences1, sentences2, scores, **kwargs)
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/EmbeddingSimilarityEvaluator.py

@@ -1,4 +1,6 @@
 from contextlib import nullcontext
+
+from sentence_transformers import SentenceTransformer
 from . import SentenceEvaluator, SimilarityFunction
 import logging
 import os
@@ -101,7 +103,7 @@ def from_input_examples(cls, examples: List[InputExample], **kwargs):
             scores.append(example.label)
         return cls(sentences1, sentences2, scores, **kwargs)
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/InformationRetrievalEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator
 import torch
@@ -94,7 +95,9 @@ def __init__(
             for k in map_at_k:
                 self.csv_headers.append("{}-MAP@{}".format(score_name, k))
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1, *args, **kwargs) -> float:
+    def __call__(
+        self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1, *args, **kwargs
+    ) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"
@@ -147,7 +150,9 @@ def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int =
         else:
             return scores[self.main_score_function]["map@k"][max(self.map_at_k)]
 
-    def compute_metrices(self, model, corpus_model=None, corpus_embeddings: Tensor = None) -> Dict[str, float]:
+    def compute_metrices(
+        self, model: SentenceTransformer, corpus_model=None, corpus_embeddings: Tensor = None
+    ) -> Dict[str, float]:
         if corpus_model is None:
             corpus_model = model
 

sentence_transformers/evaluation/LabelAccuracyEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from . import SentenceEvaluator
 import torch
 from torch.utils.data import DataLoader
@@ -37,7 +38,7 @@ def __init__(self, dataloader: DataLoader, name: str = "", softmax_model=None, w
         self.csv_file = "accuracy_evaluation" + name + "_results.csv"
         self.csv_headers = ["epoch", "steps", "accuracy"]
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         model.eval()
         total = 0
         correct = 0

sentence_transformers/evaluation/MSEEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from sentence_transformers.evaluation import SentenceEvaluator
 import logging
@@ -57,7 +58,7 @@ def __init__(
         self.csv_headers = ["epoch", "steps", "MSE"]
         self.write_csv = write_csv
 
-    def __call__(self, model, output_path, epoch=-1, steps=-1):
+    def __call__(self, model: SentenceTransformer, output_path, epoch=-1, steps=-1):
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/MSEEvaluatorFromDataFrame.py

@@ -77,7 +77,7 @@ def __init__(
             all_src_embeddings = teacher_model.encode(all_source_sentences, batch_size=self.batch_size)
         self.teacher_embeddings = {sent: emb for sent, emb in zip(all_source_sentences, all_src_embeddings)}
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1):
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1):
         model.eval()
 
         mse_scores = []

sentence_transformers/evaluation/ParaphraseMiningEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator
 import logging
@@ -99,7 +100,7 @@ def __init__(
         self.csv_headers = ["epoch", "steps", "precision", "recall", "f1", "threshold", "average_precision"]
         self.write_csv = write_csv
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/RerankingEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator
 import logging
@@ -82,7 +83,7 @@ def __init__(
         ]
         self.write_csv = write_csv
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/SentenceEvaluator.py

@@ -1,11 +1,14 @@
+from sentence_transformers import SentenceTransformer
+
+
 class SentenceEvaluator:
     """
     Base class for all evaluators
 
     Extend this class and implement __call__ for custom evaluators.
     """
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         """
         This is called during training to evaluate the model.
         It returns a score for the evaluation with a higher score indicating a better result.

sentence_transformers/evaluation/SequentialEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from . import SentenceEvaluator
 from typing import Iterable
 
@@ -14,7 +15,7 @@ def __init__(self, evaluators: Iterable[SentenceEvaluator], main_score_function=
         self.evaluators = evaluators
         self.main_score_function = main_score_function
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         scores = []
         for evaluator in self.evaluators:
             scores.append(evaluator(model, output_path, epoch, steps))

sentence_transformers/evaluation/TranslationEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator
 import logging
@@ -70,7 +71,7 @@ def __init__(
         self.csv_headers = ["epoch", "steps", "src2trg", "trg2src"]
         self.write_csv = write_csv
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"

sentence_transformers/evaluation/TripletEvaluator.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer
 from contextlib import nullcontext
 from . import SentenceEvaluator, SimilarityFunction
 import logging
@@ -75,7 +76,7 @@ def from_input_examples(cls, examples: List[InputExample], **kwargs):
             negatives.append(example.texts[2])
         return cls(anchors, positives, negatives, **kwargs)
 
-    def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
+    def __call__(self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
         if epoch != -1:
             if steps == -1:
                 out_txt = f" after epoch {epoch}"