Instructions to use terzimert/model_token_classification-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use terzimert/model_token_classification-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="terzimert/model_token_classification-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("terzimert/model_token_classification-v2") model = AutoModelForTokenClassification.from_pretrained("terzimert/model_token_classification-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from terzimert/model_token_classification-v2: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/terzimert/model_token_classification-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://terzimert/model_token_classification-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/terzimert/model_token_classification-v2/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 5605c173cbf9318e6215fc441b52a766afeb644ff694fcdd0c5bca96e9c3631e
- Size of remote file:
- 709 MB
- SHA256:
- 4d76c20f2c3c26242caf8eef60bc004f59d2b1afb944f5136154504047555b33
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