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--- |
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# Dataset Card for `hlm-paraphrase-multilingual-mpnet-base-v2` |
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### Dataset Summary |
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Chromadb vectorstore for 红楼梦, created with |
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``` |
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import os |
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from langchain.document_loaders import TextLoader |
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from langchain.embeddings import SentenceTransformerEmbeddings |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import Chroma |
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model_name = 'paraphrase-multilingual-mpnet-base-v2' |
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embedding = SentenceTransformerEmbeddings(model_name=model_name) |
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url = 'https://raw.githubusercontent.com/ffreemt/multilingual-dokugpt/master/docs/hlm.txt' |
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os.system(f'wget -c {url}') |
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doc = TextLoader('hlm.txt').load() |
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text_splitter = RecursiveCharacterTextSplitter( |
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separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""], |
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chunk_size=620, |
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chunk_overlap=60, |
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length_function=len |
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) |
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doc_chunks = text_splitter.split_documents(doc) |
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client_settings = Settings(chroma_db_impl="duckdb+parquet", anonymized_telemetry=False, persist_directory='db') |
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# takes 8-20 minutes on CPU |
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vectorstore = Chroma.from_documents( |
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documents=doc_chunks, |
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embedding=embedding, |
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persist_directory='db', |
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client_settings=client_settings, |
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) |
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vectorstore.persist() |
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``` |
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### How to use |
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Download the `hlm` directory to a local directory, e.g., `db`, for example |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download( |
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repo_id="mikeee/chroma-paraphrase-multilingual-mpnet-base-v2", |
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repo_type="dataset", |
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allow_patterns="hlm/*", |
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local_dir="db", |
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resume_download=True, |
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) |
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``` |
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Load the vectorestore: |
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```python |
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from langchain.embeddings import SentenceTransformerEmbeddings |
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from langchain.vectorstores import Chroma |
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from chromadb.config import Settings |
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model_name = 'paraphrase-multilingual-mpnet-base-v2' |
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embedding = SentenceTransformerEmbeddings(model_name=model_name) |
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client_settings = Settings( |
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chroma_db_impl="duckdb+parquet", |
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anonymized_telemetry=False, |
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persist_directory='db/hlm' |
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) |
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db = Chroma( |
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# persist_directory='docs', |
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embedding_function=embedding, |
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client_settings=client_settings, |
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) |
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res = db.search("红楼梦主线", search_type="similarity", k=2) |
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print(res) |
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# [Document(page_content='通灵宝玉正面图式\u3000通灵宝玉反面图式\n\n\n\n玉宝灵通\u3000\u3000\u3000\u3000\u3000三二一\n\n仙莫\u3000\u3000\u3000\u3000\u3000\u3000知疗除\n\n寿失\u3000\u3000\u3000\u3000\u3000\u3000祸冤邪\n\n恒莫\u3000\u3000\u3000\u3000\u3000\u3000福疾崇\n\n昌忘\n\n\n\n宝钗看毕，【甲戌双行。。。 |
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``` |
