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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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Only for the researching usage. |
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## The converting process below. |
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```python |
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# Setting the env |
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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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# Loading the dataset |
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import json |
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import gzip |
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from sentence_transformers.util import http_get |
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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='utf8') 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['paragraphs'][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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len(passages) |
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# Loading the model |
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from sentence_transformers import SentenceTransformer |
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bi_encoder=SentenceTransformer(os.getenv('MODEL_NAME')) |
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bi_encoder.max_seq_length=256 |
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bi_encoder.to('cuda') |
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bi_encoder |
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# normalizing the embeddings |
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from sentence_transformers.util import normalize_embeddings |
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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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# save to the csv file |
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import pandas as pd |
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embeddings_data=pd.DataFrame(corpus_embeddings.cpu()) |
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embeddings_data.to_csv('simple_english_wikipedia.csv', index=False) |
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``` |