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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import csv
import json
import pandas as pd
from tqdm import tqdm
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
def parse_qa_csv_file(location):
with open(location) as file:
reader = csv.reader(file, delimiter='\t')
for row in reader:
question = row[0]
answers = eval(row[1])
yield question, answers
def parse_qa_json_file(location):
with open(location) as file:
for line in file:
qa = json.loads(line)
question = qa['question']
answers = qa['answer']
yield question, answers
def encode_query(text, tokenizer, model, device='cpu'):
input_ids = tokenizer(text, return_tensors='pt')
input_ids.to(device)
embeddings = model(input_ids["input_ids"]).pooler_output.detach().cpu().numpy()
return embeddings.flatten()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--encoder', type=str, help='encoder name or path',
default='facebook/dpr-question_encoder-multiset-base', required=False)
parser.add_argument('--input', type=str, help='qas file, json file by default', required=True)
parser.add_argument('--format', type=str, help='qas file format', default='json', required=False)
parser.add_argument('--output', type=str, help='path to store query embeddings', required=True)
parser.add_argument('--device', type=str,
help='device cpu or cuda [cuda:0, cuda:1...]', default='cpu', required=False)
args = parser.parse_args()
tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(args.encoder)
model = DPRQuestionEncoder.from_pretrained(args.encoder)
model.to(args.device)
embeddings = {'id': [], 'text': [], 'embedding': []}
qa_parser = None
if args.format == 'csv':
qa_parser = parse_qa_csv_file
elif args.format == 'json':
qa_parser = parse_qa_json_file
if qa_parser is None:
print(f'No QA parser defined for file format: {args.format}, or format not match')
for qid, (question, answers) in enumerate(tqdm(list(qa_parser(args.input)))):
embeddings['id'].append(qid)
embeddings['text'].append(question)
embeddings['embedding'].append(encode_query(question, tokenizer, model, args.device))
embeddings = pd.DataFrame(embeddings)
embeddings.to_pickle(args.output)
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