Instructions to use AlanLiJHU/MLMA_Lab5_Task2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlanLiJHU/MLMA_Lab5_Task2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AlanLiJHU/MLMA_Lab5_Task2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AlanLiJHU/MLMA_Lab5_Task2") model = AutoModelForTokenClassification.from_pretrained("AlanLiJHU/MLMA_Lab5_Task2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AlanLiJHU/MLMA_Lab5_Task2: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/AlanLiJHU/MLMA_Lab5_Task2/resolve/main/training_args.bin
- Command line
-
hf download hf://AlanLiJHU/MLMA_Lab5_Task2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AlanLiJHU/MLMA_Lab5_Task2/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- c76a01113aa217d34ea9fb28407b0cd3d773eb1a27c4872e1f9d38093875254f
- Size of remote file:
- 4.86 kB
- SHA256:
- 5034a1abc208d7fad6584303d55db6ec0dd61dcf33f9dafee944d92d313cd2a9
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