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--- |
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license: mit |
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language: |
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- en |
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--- |
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Only for the reaseaching usage. |
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The original data from http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz. |
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We use `nq_distilbert-base-v1` model encode all the data to the PyTorch Tensors. And `normalize` the embeddings by using `sentence_transformers.util.normalize_embeddings`. |
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## How to use |
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See notebook [Wikipedia Q&A Retrieval-Semantic Search](https://www.kaggle.com/code/aisuko/wikipedia-q-a-retrieval-semantic-search) |
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## Installing the package |
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```python |
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!pip install sentence-transformers==2.3.1 |
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``` |
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## The converting process |
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```python |
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# the whole process takes 1287.0s GPU P100 |
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import os |
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import json |
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import gzip |
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from sentence_tranformers.util import http_get |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import normalize_embeddings |
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os.environ['DATASET_NAME']='simplewiki-2020-11-01.jsonl.gz' |
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os.environ['DATASET_URL']='http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz' |
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os.environ['MODEL_NAME']='multi-qa-MiniLM-L6-cos-v1' |
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os.environ['CROSS_CODE_NAME']='cross-encoder/ms-marco-MiniLM-L-6-v2' |
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http_get(os.getenv('DATASET_URL'), os.getenv('DATASET_NAME')) |
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passages=[] |
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with gzip.open(os.getenv('DATASET_NAME'), 'rt', encoding='utf-8') as fIn: |
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for line in fIn: |
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data=json.loads(line.strip()) |
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# add all paragraphs |
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# passages.extend(data['paragraphs']) |
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# only add the first paragraph |
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# passages.append(data['paragraph'][0]) |
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for paragraph in data['paragraphs']: |
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# We encode the passages as [title, text] |
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passages.append([data['title'], paragraph]) |
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print('Passages:', len(passages)) |
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bi_encoder=SentenceTransformer('nq-distilbert-base-v1') |
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bi_encoder.max_seq_length=256 |
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bi_encoder.to('cuda') |
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corpus_embeddings=bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True).to('cuda') |
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corpus_embeddings=normalize_embeddings(corpus_embeddings) |
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len(corpus_embeddings) |
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import pandas as pd |
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embedding_data=pd.DataFrame(corpus_embeddings.cpu()) |
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embedding_data.to_csv('simple_english_wikipedia_2020_11_01.csv', index=False) |
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``` |
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