Instructions to use benjamin/wtp-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benjamin/wtp-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benjamin/wtp-bert-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("benjamin/wtp-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from benjamin/wtp-bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 10.1 MB
-
https://huggingface.co/benjamin/wtp-bert-tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://benjamin/wtp-bert-tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/benjamin/wtp-bert-tiny/resolve/main/pytorch_model.bin
10.1 MB
- Xet hash:
- 63eff4b9f8694c558a221a4a63d4c35775bb3729e2f16f2d5361a5ee0c0631d2
- Size of remote file:
- 10.1 MB
- SHA256:
- 225789d7d2df2e7700b10a3325c71b2dd99df7676bbf9900268de28c5e75fc11
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