Instructions to use Maoger/TAC_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maoger/TAC_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Maoger/TAC_dataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maoger/TAC_dataset") model = AutoModelForTokenClassification.from_pretrained("Maoger/TAC_dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Maoger/TAC_dataset: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Maoger/TAC_dataset/resolve/main/training_args.bin
- Command line
-
hf download hf://Maoger/TAC_dataset/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Maoger/TAC_dataset/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 9fd2b6c76c0f07d8cc4c2a52bdedfd71457149b99792932124d55301acfc6b62
- Size of remote file:
- 5.3 kB
- SHA256:
- 6c99d97f3b5929dbbe590e37fcc89ea58be9b9e5f8192c6963636619214ca13e
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