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twang2218
/
text2vec-base-chinese-law

Feature Extraction
Transformers
PyTorch
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use twang2218/text2vec-base-chinese-law with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use twang2218/text2vec-base-chinese-law with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="twang2218/text2vec-base-chinese-law")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("twang2218/text2vec-base-chinese-law")
    model = AutoModel.from_pretrained("twang2218/text2vec-base-chinese-law")
  • Notebooks
  • Google Colab
  • Kaggle
text2vec-base-chinese-law
410 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
twang2218's picture
twang2218
Retrain the model using positive cases and shuffled positive cases.
11f2b4a verified about 3 years ago
  • 1_Pooling
    Add model about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    28 Bytes
    initial commit about 3 years ago
  • config.json
    864 Bytes
    Retrain the model using positive cases and shuffled positive cases. about 3 years ago
  • eval_results.txt
    69 Bytes
    Retrain the model using positive cases and shuffled positive cases. about 3 years ago
  • modules.json
    230 Bytes
    Add model about 3 years ago
  • pytorch_model.bin
    409 MB
    xet
    Retrain the model using positive cases and shuffled positive cases. about 3 years ago
  • sentence_bert_config.json
    54 Bytes
    Add model about 3 years ago
  • special_tokens_map.json
    125 Bytes
    Add model about 3 years ago
  • tokenizer.json
    439 kB
    Add model about 3 years ago
  • tokenizer_config.json
    394 Bytes
    Add model about 3 years ago
  • training_progress_scores.csv
    305 Bytes
    Retrain the model using positive cases and shuffled positive cases. about 3 years ago
  • vocab.txt
    110 kB
    Add model about 3 years ago