Instructions to use beyhan/checkpoint-3750 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beyhan/checkpoint-3750 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="beyhan/checkpoint-3750")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("beyhan/checkpoint-3750") model = AutoModelForTokenClassification.from_pretrained("beyhan/checkpoint-3750", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from beyhan/checkpoint-3750: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/beyhan/checkpoint-3750/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://beyhan/checkpoint-3750/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/beyhan/checkpoint-3750/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- bc8eeae5b353d478113cc73d864847c6c5e8403e6105703fc64de85c14dc30f8
- Size of remote file:
- 443 MB
- SHA256:
- cdfeccc0625a4366339bd24a9f1b7c830b97176686d14e135c5e65e401c33d58
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