File size: 5,049 Bytes
acf107e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
from typing import List, Tuple
import numpy as np
import pandas as pd
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, RobertaForSequenceClassification, RobertaTokenizer, ElectraModel, ElectraForCausalLM, GPT2Tokenizer, GPT2Model, GPT2LMHeadModel
import torch
import os
import json
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import gzip
from transformers import Text2TextGenerationPipeline
class TestEnsembleQAPipeline(Text2TextGenerationPipeline):
def __init__(self, model=None, tokenizer=None, framework="pt", **kwargs): # Add **kwargs to accept additional unused arguments
super().__init__(model=model, tokenizer=tokenizer, framework=framework)
self.quiz_bowl_model = QuizBowlModel() # Initializes your QuizBowl model
def _forward(self, model_inputs, **generate_kwargs):
questions = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in model_inputs["input_ids"]]
results = self.quiz_bowl_model.guess_and_buzz(questions)
return results
def postprocess(self, model_outputs):
results = {}
for output in model_outputs:
guess_text = output['guess']
confidence = output['confidence']
results = {'guess': guess_text, 'confidence': confidence}
return results
class QuizBowlModel:
def __init__(self):
self.load_models()
def load_models(self):
"""Load all models"""
# model_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'models', 't5-model-params')
# self.load_seq2seq_model(model_dir)
self.load_flan_models('google/flan-t5-large', 'google/flan-t5-small')
def load_seq2seq_model(self, model_dir):
"""Load saved models"""
self.test_tokenizer = AutoTokenizer.from_pretrained(model_dir)
self.test_model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
self.test_model.eval()
def load_flan_models(self, large_model_id, small_model_id):
"""Load hugging face models."""
self.tokenizer_flan_t5 = AutoTokenizer.from_pretrained(large_model_id)
self.model_flan_t5 = AutoModelForSeq2SeqLM.from_pretrained(large_model_id)
self.tokenizer_t5 = AutoTokenizer.from_pretrained(small_model_id)
self.model_t5 = AutoModelForSeq2SeqLM.from_pretrained(small_model_id)
def guess_and_buzz(self, question_texts):
"""Generate answers from all models for given questions"""
total_answers = self.generate_answers(question_texts)
Display the model's guesses before voting
print("Answers Before Voting Mechanism:")
for question, model_answers in zip(question_texts, total_answers):
print(f"{question}\nModel Guesses: {model_answers}\n\n")
return self.ensemble_tfidf_voting(total_answers)
def generate_answers(self, question_texts):
"""Generate answers from each model."""
# Tokenize and generate answers using each model
return [(self.decode_answer(self.model_flan_t5, self.tokenizer_flan_t5, question),
self.decode_answer(self.model_t5, self.tokenizer_t5, question))
for question in question_texts]
def decode_answer(self, model, tokenizer, input_text):
input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model.generate(**input_ids, max_new_tokens=5, output_scores=True, return_dict_in_generate=True)
decoded_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
if outputs.scores:
log_probs = [torch.nn.functional.log_softmax(score, dim=-1) for score in outputs.scores]
scores = []
for i in range(len(outputs.sequences[0]) - 1):
selected_log_prob = log_probs[i][0, outputs.sequences[0][i + 1]].item()
scores.append(selected_log_prob)
confidence_score = np.exp(np.mean(scores))
else:
confidence_score = None
return decoded_text, confidence_score
def ensemble_tfidf_voting(self, all_answers):
"""Find answer with highest confidence"""
for answers in all_answers:
highest_confidence_answer = max(answers, key=lambda x: x[1])
yield {'guess': highest_confidence_answer[0], 'confidence': highest_confidence_answer[1]}
# for answers in all_answers:
# texts = [answer[0] for answer in answers]
# vectorizer = TfidfVectorizer()
# tfidf_matrix = vectorizer.fit_transform(texts)
# cosine_scores = cosine_similarity(tfidf_matrix)
# most_similar_index = np.argmax(np.mean(cosine_scores, axis=0))
# yield {'guess': answers[most_similar_index][0], 'confidence': answers[most_similar_index][1]}
|