Instructions to use LightFury9/nlp-mini-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LightFury9/nlp-mini-project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LightFury9/nlp-mini-project")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LightFury9/nlp-mini-project") model = AutoModelForTokenClassification.from_pretrained("LightFury9/nlp-mini-project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LightFury9/nlp-mini-project: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/LightFury9/nlp-mini-project/resolve/main/training_args.bin
- Command line
-
hf download hf://LightFury9/nlp-mini-project/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/LightFury9/nlp-mini-project/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 2d6e5c15c521a82685f1898cc88ff3ebd4bcbade7a55e63dca6d75686b8affa8
- Size of remote file:
- 4.54 kB
- SHA256:
- bcb19ae30c418df4cb67f3ee63ee680e79e66ea9abd190c6903b39c75d3f3bd6
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