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eval_generators.py
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eval_generators.py
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from openai import OpenAI
from together import Together
import torch
from transformers import AutoModelForSeq2SeqLM, MT5Tokenizer, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
from utils import *
from length_mapping import *
from datasets import load_dataset
import evaluate
import re
import ast
import os
import pandas as pd
from tqdm import tqdm
class GPT():
def __init__(self, task):
self.client = OpenAI(
organization='org-OWe4x5PWbYh2yljPFA9odbCD',
)
self.task = task
def forward(self, x, k, cot=False):
if self.task == "translation-en-ur" or self.task == "translation-ur-en":
messages = create_messages_translation(self.task, k, cot)
elif self.task == "transliteration":
messages = create_messages_transliteration(k, cot)
elif self.task == "summarization":
messages = create_messages_summarization(k, cot)
elif self.task == "paraphrase":
messages = create_messages_paraphrase(k, cot)
elif self.task == "question-answering":
messages = create_messages_question_answering(k, cot)
x = f"Context:\n{x[0]}\n\nQuestion:\n{x[1]}"
x = str(x)
messages.append({"role": "user", "content": x})
completion = self.client.chat.completions.create(
model="gpt-4-turbo",
messages=messages,
response_format={"type": "json_object"}
)
return completion.choices[0].message.content
class LLaMA():
def __init__(self, task):
self.client = Together()
self.task = task
def forward(self, x):
if self.task == "translation-en-ur" or self.task == "translation-ur-en":
messages = create_messages_translation(self.task, 0)
elif self.task == "transliteration":
messages = create_messages_transliteration(0)
elif self.task == "summarization":
messages = create_messages_summarization(0)
elif self.task == "paraphrase":
messages = create_messages_paraphrase(0)
elif self.task == "question-answering":
messages = create_messages_question_answering(0)
x = f"Context:\n{x[0]}\n\nQuestion:\n{x[1]}"
x = str(x)
messages.append({"role": "user", "content": x})
response = self.client.chat.completions.create(
model="meta-llama/Llama-3-8b-chat-hf",
messages=messages,
temperature=0.6,
top_p=0.9,
)
return response.choices[0].message.content
class LLaMAFT():
def __init__(self, task, device):
self.task = task
self.device = device
self.bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
self.tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
self.model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", quantization_config=self.bnb_config)
self.model = PeftModel.from_pretrained(self.model, f"ULRs/llama-3-8b-{task}-ur").to(self.device)
def format_input(self, x):
if self.task == "question-answering":
input = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{LLAMA_FT_PROMPTS[self.task]}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nContext:\n{x[0]}\n\nQuestion:\n{x[1]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
return input
input = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{LLAMA_FT_PROMPTS[self.task]}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{x}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
return input
def forward(self, x):
x = self.format_input(x)
input_ids = self.tokenizer(x, return_tensors="pt").to(self.device)
outputs = self.model.generate(
**input_ids,
max_new_tokens=LENGTH_MAPPING_LLAMA[self.task],
temperature=0.6,
top_p=0.9,
pad_token_id=self.tokenizer.eos_token_id
)
output = self.tokenizer.decode(outputs[0])
return output.split("assistant<|end_header_id|>\n\n")[-1].replace("<|eot_id|>", "")
class MT5():
def __init__(self, task, device):
self.device = device
self.task = task
if self.task == "translation-en-ur" or self.task == "translation-ur-en":
self.model = AutoModelForSeq2SeqLM.from_pretrained(f"ULRs/mt5-large-{self.task}").to(self.device)
self.tokenizer = MT5Tokenizer.from_pretrained(f"ULRs/mt5-large-{self.task}")
else:
self.model = AutoModelForSeq2SeqLM.from_pretrained(f"ULRs/mt5-large-{self.task}-ur").to(self.device)
self.tokenizer = MT5Tokenizer.from_pretrained(f"ULRs/mt5-large-{self.task}-ur")
def forward(self, x):
if self.task == "translation-en-ur" or self.task == "translation-ur-en":
x = f"Translate: {x}"
elif self.task == "transliteration":
x = f"Transliterate: {x}"
elif self.task == "summarization":
x = f"Summarize: {x}"
elif self.task == "paraphrase":
x = f"Paraphrase: {x}"
elif self.task == "question-answering":
x = f"context: {x[0]} question: {x[1]}"
inputs = self.tokenizer.batch_encode_plus([x], truncation=True, padding="max_length", max_length=LENGTH_MAPPING_MT5[self.task]["input"], return_tensors="pt")
outputs = self.model.generate(
input_ids=inputs['input_ids'].to(self.device),
attention_mask=inputs['attention_mask'].to(self.device),
max_length=LENGTH_MAPPING_MT5[self.task]["output"],
)
decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return decoded_output
class Metric():
def __init__(self):
self.bleu = evaluate.load("sacrebleu")
self.f1_score = evaluate.load("f1")
self.squad_metric = evaluate.load("squad")
self.rouge = evaluate.load("rouge")
def calculate_bleu_score(self, predictions, references):
return {"sacrebleu": self.bleu.compute(predictions=predictions, references=references)["score"]}
def calculate_rouge_score(self, predictions, references):
return self.rouge.compute(predictions=predictions, references=references, tokenizer=lambda x: x.split())
def calculate_squad_metric(self, predictions, references):
return self.squad_metric.compute(predictions=predictions, references=references)
def calculate_f1_score(self, predictions, references):
return self.f1_score.compute(predictions=predictions, references=references, average="macro")
class Evaluation():
def __init__(self, task, k_shot, cot, to_eval, device):
self.task = task
if not os.path.exists(f"predictions/{self.task}"):
os.mkdir(f"predictions/{self.task}")
self.k_shot = k_shot
self.cot = cot
self.to_eval = to_eval
self.dataset = load_dataset("csv", data_files={"test": f"datasets/{self.task.replace('-en-ur', '').replace('-ur-en', '')}.csv"})
if self.to_eval == "gpt":
self.gpt = GPT(self.task)
if self.to_eval == "mt5":
self.mt5 = MT5(self.task, device)
if self.to_eval == "llama":
self.llama = LLaMA(self.task)
if self.to_eval == "llama-ft":
self.llama_ft = LLaMAFT(self.task, device)
self.metric = Metric()
def extract_output(self, output, key):
try:
output = ast.literal_eval(output)[key]
except:
try:
temp = "{" + re.search(rf"['\"]{key}['\"]\s*:\s*['\"].*?['\"]}}", output).group(0)
output = ast.literal_eval(temp)[key]
except:
output = re.sub(r"[{}\[\]'\"':]", "", output.replace(key, "")).lstrip().rstrip()
return output
def eval_gpt(self, input, key, i):
for k in [0, 3, 6]:
if k not in self.k_shot:
continue
if os.path.exists(f"predictions/{self.task}/gpt-{k}-shot.csv"):
df = pd.read_csv(f"predictions/{self.task}/gpt-{k}-shot.csv")
else:
df = pd.DataFrame(columns=["prediction"])
if len(df) > i and type(df.iloc[i]["prediction"]) == str:
continue
output = self.gpt.forward(input, k)
prediction = self.extract_output(output, key)
df.loc[i] = [prediction]
df.to_csv(f"predictions/{self.task}/gpt-{k}-shot.csv", index=False, encoding="utf-8")
if self.cot:
if os.path.exists(f"predictions/{self.task}/gpt-cot.csv"):
df = pd.read_csv(f"predictions/{self.task}/gpt-cot.csv")
else:
df = pd.DataFrame(columns=["prediction"])
if len(df) > i and type(df.iloc[i]["prediction"]) == str:
pass
else:
output = self.gpt.forward(input, k, cot=True)
prediction = self.extract_output(output, key)
df.loc[i] = [prediction]
df.to_csv(f"predictions/{self.task}/gpt-cot.csv", index=False, encoding="utf-8")
def eval_llama(self, input, key, i):
if os.path.exists(f"predictions/{self.task}/llama.csv"):
df = pd.read_csv(f"predictions/{self.task}/llama.csv")
else:
df = pd.DataFrame(columns=["prediction"])
if len(df) > i and type(df.iloc[i]["prediction"]) == str:
pass
else:
output = self.llama.forward(input)
prediction = self.extract_output(output, key)
df.loc[i] = [prediction]
df.to_csv(f"predictions/{self.task}/llama.csv", index=False, encoding="utf-8")
def eval_llama_ft(self, input, key, i):
if os.path.exists(f"predictions/{self.task}/llama-ft.csv"):
df = pd.read_csv(f"predictions/{self.task}/llama-ft.csv")
else:
df = pd.DataFrame(columns=["prediction"])
if len(df) > i and type(df.iloc[i]["prediction"]) == str:
pass
else:
output = self.llama_ft.forward(input)
prediction = self.extract_output(output, key)
df.loc[i] = [prediction]
df.to_csv(f"predictions/{self.task}/llama-ft.csv", index=False, encoding="utf-8")
def eval_mt5(self, input, i):
if os.path.exists(f"predictions/{self.task}/mt5.csv"):
df = pd.read_csv(f"predictions/{self.task}/mt5.csv")
else:
df = pd.DataFrame(columns=["prediction"])
if len(df) > i:
pass
else:
prediction = self.mt5.forward(input)
df.loc[i] = [prediction]
df.to_csv(f"predictions/{self.task}/mt5.csv", index=False, encoding="utf-8")
def eval(self):
for i in tqdm(range(len(self.dataset["test"]))):
key = ""
if self.task == "translation-en-ur":
input = self.dataset["test"][i]["english"]
key = "translation"
elif self.task == "translation-ur-en":
input = self.dataset["test"][i]["urdu"]
key = "translation"
elif self.task == "transliteration":
input = self.dataset["test"][i]["urdu"]
key = "transliteration"
elif self.task == "summarization":
input = self.dataset["test"][i]["text"]
key = "summary"
elif self.task == "paraphrase":
input = self.dataset["test"][i]["text"]
key = "paraphrase"
elif self.task == "question-answering":
input = [self.dataset["test"][i]["context"], self.dataset["test"][i]["question"]]
key = "answer"
if self.to_eval == "gpt":
self.eval_gpt(input, key, i)
if self.to_eval == "llama":
self.eval_llama(input, key, i)
if self.to_eval == "llama-ft":
self.eval_llama_ft(input, key, i)
if self.to_eval == "mt5":
self.eval_mt5(input, i)
def calcuate_metric(self):
if self.task == "question-answering":
squad = {}
true = self.dataset["test"]["answer"]
y_true = []
for i in true:
y_true.append(ast.literal_eval(i))
for model in ["gpt-0-shot", "gpt-3-shot", "gpt-6-shot", "mt5", "llama", "llama-ft"]:
if os.path.exists(f"predictions/{self.task}/{model}.csv"):
df = pd.read_csv(f"predictions/{self.task}/{model}.csv")
y = df["prediction"].tolist()
predictions = []
references = []
for i, y_ in enumerate(y):
predictions.append({"id": str(i), "prediction_text": y_})
temp = []
for ref_ in y_true[i]:
temp.append({"text": ref_, "answer_start": 0})
references.append({"id": str(i), "answers": temp})
squad[model] = self.metric.calculate_squad_metric(predictions, references)
return squad
elif self.task == "summarization":
rouge = {}
references = self.dataset["test"]["summary"]
for model in ["gpt-0-shot", "gpt-3-shot", "gpt-6-shot", "mt5", "llama", "llama-ft"]:
if os.path.exists(f"predictions/{self.task}/{model}.csv"):
df = pd.read_csv(f"predictions/{self.task}/{model}.csv")
predictions = df["prediction"].tolist()
rouge[model] = self.metric.calculate_rouge_score(predictions, references)
return rouge
else:
bleu = {}
if self.task == "transliteration" or self.task == "translation-ur-en":
key = "english"
elif self.task == "paraphrase":
key = "paraphrase"
elif self.task == "translation-en-ur":
key = "urdu"
references = self.dataset["test"][key]
for model in ["gpt-0-shot", "gpt-3-shot", "gpt-6-shot", "mt5", "llama", "llama-ft"]:
if os.path.exists(f"predictions/{self.task}/{model}.csv"):
df = pd.read_csv(f"predictions/{self.task}/{model}.csv")
predictions = df["prediction"].tolist()
bleu[model] = self.metric.calculate_bleu_score(predictions, references)
return bleu
ev = Evaluation("translation-en-ur", [], False, "llama", "mps")
# ev.eval()
print(ev.calcuate_metric())