Instructions to use hiepnh/ta-p-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiepnh/ta-p-biencoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hiepnh/ta-p-biencoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hiepnh/ta-p-biencoder") model = AutoModel.from_pretrained("hiepnh/ta-p-biencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/test from hiepnh/ta-p-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 3 Bytes
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https://huggingface.co/hiepnh/ta-p-biencoder/resolve/main/1_Pooling/test
- Command line
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hf download hf://hiepnh/ta-p-biencoder/1_Pooling/test
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curl -L -o test https://huggingface.co/hiepnh/ta-p-biencoder/resolve/main/1_Pooling/test
3 Bytes
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