Instructions to use chris1tava/position_names with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chris1tava/position_names with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chris1tava/position_names")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chris1tava/position_names") model = AutoModelForTokenClassification.from_pretrained("chris1tava/position_names", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from chris1tava/position_names: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/chris1tava/position_names/resolve/main/training_args.bin
- Command line
-
hf download hf://chris1tava/position_names/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/chris1tava/position_names/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- aa317bd3cd6a14dcece30c4d7af712e0d6dda8d3be293bbf3f16b2936852fab9
- Size of remote file:
- 4.66 kB
- SHA256:
- 35aa93156d0b250b648ac05c2d17004453f89bf2e43a34ecd80d8a09e424c8cb
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