Instructions to use emilys/hmBERT-CoNLL-cp3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilys/hmBERT-CoNLL-cp3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="emilys/hmBERT-CoNLL-cp3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("emilys/hmBERT-CoNLL-cp3") model = AutoModelForTokenClassification.from_pretrained("emilys/hmBERT-CoNLL-cp3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from emilys/hmBERT-CoNLL-cp3: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/emilys/hmBERT-CoNLL-cp3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://emilys/hmBERT-CoNLL-cp3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/emilys/hmBERT-CoNLL-cp3/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 4272e20dab6deb361369c56c4ce0e0f2f70a630f472f768d830b10989b0ca648
- Size of remote file:
- 440 MB
- SHA256:
- cbc85b942cf2e60e0ae0f8107cad752a6a8ca930e2f6ccc198843274db4c5969
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