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from models.pairwise_model import *
from features.text_utils import *
import regex as re
from models.bm25_utils import BM25Gensim
from models.qa_model import *
from tqdm.auto import tqdm
tqdm.pandas()
from datasets import load_dataset
from transformers import pipeline
class InferencePipeline():
def __init__(self, path=""):
df_wiki_windows = load_dataset("foxxy-hm/e2eqa-wiki", data_files="processed/wikipedia_20220620_cleaned_v2.csv")["train"].to_pandas()
df_wiki = load_dataset("foxxy-hm/e2eqa-wiki", data_files="wikipedia_20220620_short.csv")["train"].to_pandas()
df_wiki.title = df_wiki.title.apply(str)
entity_dict = load_dataset("foxxy-hm/e2eqa-wiki", data_files="processed/entities.json")["train"].to_dict()
new_dict = dict()
for key, val in entity_dict.items():
val = val[0].replace("wiki/", "").replace("_", " ")
entity_dict[key] = val
key = preprocess(key)
new_dict[key.lower()] = val
entity_dict.update(new_dict)
title2idx = dict([(x.strip(), y) for x, y in zip(df_wiki.title, df_wiki.index.values)])
qa_model = QAEnsembleModel("nguyenvulebinh/vi-mrc-large", ["qa_model_robust.bin"], entity_dict)
pairwise_model_stage1 = PairwiseModel("nguyenvulebinh/vi-mrc-base")
pairwise_model_stage1.load_state_dict(torch.load("pairwise_v2.bin", map_location=torch.device('cpu')))
pairwise_model_stage1.eval()
pairwise_model_stage2 = PairwiseModel("nguyenvulebinh/vi-mrc-base")
pairwise_model_stage2.load_state_dict(torch.load("pairwise_stage2_seed0.bin", map_location=torch.device('cpu')))
bm25_model_stage1 = BM25Gensim("bm25_stage1/", entity_dict, title2idx)
bm25_model_stage2_full = BM25Gensim("bm25_stage2/full_text/", entity_dict, title2idx)
bm25_model_stage2_title = BM25Gensim("bm25_stage2/title/", entity_dict, title2idx)
self.qa_model = qa_model
self.pairwise_model_stage1 = pairwise_model_stage1
self.pairwise_model_stage2 = pairwise_model_stage2
self.bm25_model_stage1 = bm25_model_stage1
self.bm25_model_stage2_full = bm25_model_stage2_full
self.bm25_model_stage2_title = bm25_model_stage2_title
def get_answer_e2e(self, question):
query = preprocess(question).lower()
top_n, bm25_scores = self.bm25_model_stage1.get_topk_stage1(query, topk=200)
titles = [preprocess(df_wiki_windows.title.values[i]) for i in top_n]
texts = [preprocess(df_wiki_windows.text.values[i]) for i in top_n]
question = preprocess(question)
ranking_preds = self.pairwise_model_stage1.stage1_ranking(question, texts)
ranking_scores = ranking_preds * bm25_scores
best_idxs = np.argsort(ranking_scores)[-10:]
ranking_scores = np.array(ranking_scores)[best_idxs]
texts = np.array(texts)[best_idxs]
best_answer = self.qa_model(question, texts, ranking_scores)
if best_answer is None:
return "Chịu"
bm25_answer = preprocess(str(best_answer).lower(), max_length=128, remove_puncts=True)
if not check_number(bm25_answer):
bm25_question = preprocess(str(question).lower(), max_length=128, remove_puncts=True)
bm25_question_answer = bm25_question + " " + bm25_answer
candidates, scores = self.bm25_model_stage2_title.get_topk_stage2(bm25_answer, raw_answer=best_answer)
titles = [df_wiki.title.values[i] for i in candidates]
texts = [df_wiki.text.values[i] for i in candidates]
ranking_preds = self.pairwise_model_stage2.stage2_ranking(question, best_answer, titles, texts)
if ranking_preds.max() >= 0.1:
final_answer = titles[ranking_preds.argmax()]
else:
candidates, scores = self.bm25_model_stage2_full.get_topk_stage2(bm25_question_answer)
titles = [df_wiki.title.values[i] for i in candidates] + titles
texts = [df_wiki.text.values[i] for i in candidates] + texts
ranking_preds = np.concatenate(
[self.pairwise_model_stage2.stage2_ranking(question, best_answer, titles, texts), ranking_preds])
final_answer = "wiki/"+titles[ranking_preds.argmax()].replace(" ","_")
else:
final_answer = bm25_answer.lower()
return final_answer
class EndpointHandler():
def __init__(self):
self.inference_pipeline = InferencePipeline()
self.pipeline = pipeline("qa-model", model=self.inference_pipeline, tokenizer=None)
def __call__(self, question):
answer = self.inference_pipeline.get_answer_e2e(question)
return answer
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