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 MB
-
https://huggingface.co/benjamin/wtp-bert-tiny/resolve/4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/pytorch_model.bin
- Command line
-
hf download hf://benjamin/wtp-bert-tiny@4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/benjamin/wtp-bert-tiny/resolve/4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/pytorch_model.bin
10 MB
- Xet hash:
- e11aba42c6897908571a27a0f7979983637e20c7c411db61ba613e6c8317785f
- Size of remote file:
- 10 MB
- SHA256:
- 9de6e7997c90a745133f19b6d11688b6d4f3d3321ba7b3e9fc32a52e2c289a4a
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