Instructions to use hsc748NLP/TfhBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsc748NLP/TfhBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hsc748NLP/TfhBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hsc748NLP/TfhBERT") model = AutoModelForMaskedLM.from_pretrained("hsc748NLP/TfhBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsc748NLP/TfhBERT: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/hsc748NLP/TfhBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsc748NLP/TfhBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsc748NLP/TfhBERT/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- b29bfc36c7cdc91a0f18a00ce85804b730151d6bb3270aa6f2dccbea3fb53106
- Size of remote file:
- 712 MB
- SHA256:
- a19a530ef67668916518542acfeba9e463209016e3c2d9ac53d8003c82cb743a
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