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import argparse
import json
import torch
from datasets import load_dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import DPRQuestionEncoder
from common import embed_questions, clean_question, articles_to_paragraphs, kilt_wikipedia_columns
from common import kilt_wikipedia_paragraph_columns as columns
def generate_dpr_training_file(args):
n_negatives = 7
min_chars_per_passage = 200
def query_index(question, topk=(n_negatives * args.n_positives) * 2):
question_embedding = embed_questions(question_model, question_tokenizer, [question])
scores, wiki_passages = kilt_wikipedia_paragraphs.get_nearest_examples("embeddings", question_embedding, k=topk)
retrieved_examples = []
r = list(zip(wiki_passages[k] for k in columns))
for i in range(topk):
retrieved_examples.append({k: v for k, v in zip(columns, [r[j][0][i] for j in range(len(columns))])})
return retrieved_examples
def find_positive_and_hard_negative_ctxs(dataset_index: int, n_positive=1, device="cuda:0"):
positive_context_list = []
hard_negative_context_list = []
example = dataset[dataset_index]
question = clean_question(example['title'])
passages = query_index(question)
passages = [dict([(k, p[k]) for k in columns]) for p in passages]
q_passage_pairs = [[question, f"{p['title']} {p['text']}" if args.use_title else p["text"]] for p in passages]
features = ce_tokenizer(q_passage_pairs, padding="max_length", max_length=256, truncation=True,
return_tensors="pt")
with torch.no_grad():
passage_scores = ce_model(features["input_ids"].to(device),
features["attention_mask"].to(device)).logits
for p_idx, p in enumerate(passages):
p["score"] = passage_scores[p_idx].item()
# order by scores
def score_passage(item):
return item["score"]
# pick the most relevant as the positive answer
best_passage_list = sorted(passages, key=score_passage, reverse=True)
for idx, item in enumerate(best_passage_list):
if idx < n_positive:
positive_context_list.append({"title": item["title"], "text": item["text"]})
else:
break
# least relevant as hard_negative
worst_passage_list = sorted(passages, key=score_passage, reverse=False)
for idx, hard_negative in enumerate(worst_passage_list):
if idx < n_negatives * n_positive:
hard_negative_context_list.append({"title": hard_negative["title"], "text": hard_negative["text"]})
else:
break
assert len(positive_context_list) * n_negatives == len(hard_negative_context_list)
return positive_context_list, hard_negative_context_list
device = ("cuda" if torch.cuda.is_available() else "cpu")
question_model = DPRQuestionEncoder.from_pretrained(args.question_encoder_name).to(device)
question_tokenizer = AutoTokenizer.from_pretrained(args.question_encoder_name)
_ = question_model.eval()
ce_model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-4-v2').to(device)
ce_tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L-4-v2')
_ = ce_model.eval()
kilt_wikipedia = load_dataset("kilt_wikipedia", split="full")
kilt_wikipedia_paragraphs = kilt_wikipedia.map(articles_to_paragraphs, batched=True,
remove_columns=kilt_wikipedia_columns,
batch_size=512,
cache_file_name=f"../data/wiki_kilt_paragraphs_full.arrow",
desc="Expanding wiki articles into paragraphs")
# use paragraphs that are not simple fragments or very short sentences
# Wikipedia Faiss index needs to fit into a 16 Gb GPU
kilt_wikipedia_paragraphs = kilt_wikipedia_paragraphs.filter(
lambda x: (x["end_character"] - x["start_character"]) > min_chars_per_passage)
kilt_wikipedia_paragraphs.load_faiss_index("embeddings", args.index_file_name, device=0)
eli5_train_set = load_dataset("vblagoje/lfqa", split="train")
eli5_validation_set = load_dataset("vblagoje/lfqa", split="validation")
eli5_test_set = load_dataset("vblagoje/lfqa", split="test")
for dataset_name, dataset in zip(["train", "validation", "test"], [eli5_train_set,
eli5_validation_set,
eli5_test_set]):
progress_bar = tqdm(range(len(dataset)), desc=f"Creating DPR formatted {dataset_name} file")
with open('eli5-dpr-' + dataset_name + '.jsonl', 'w') as fp:
for idx, example in enumerate(dataset):
negative_start_idx = 0
positive_context, hard_negative_ctxs = find_positive_and_hard_negative_ctxs(idx, args.n_positives,
device)
for pc in positive_context:
hnc = hard_negative_ctxs[negative_start_idx:negative_start_idx + n_negatives]
json.dump({"id": example["q_id"],
"question": clean_question(example["title"]),
"positive_ctxs": [pc],
"hard_negative_ctxs": hnc}, fp)
fp.write("\n")
negative_start_idx += n_negatives
progress_bar.update(1)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Creates DPR training file")
parser.add_argument(
"--use_title",
action="store_true",
help="If true, use title in addition to passage text for passage embedding",
)
parser.add_argument(
"--n_positives",
default=3,
help="Number of positive samples per question",
)
parser.add_argument(
"--question_encoder_name",
default="vblagoje/dpr-question_encoder-single-lfqa-base",
help="Question encoder to use",
)
parser.add_argument(
"--index_file_name",
default="../data/kilt_dpr_wikipedia_first.faiss",
help="Faiss index with passage embeddings",
)
main_args, _ = parser.parse_known_args()
generate_dpr_training_file(main_args)
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