Instructions to use KanWasTaken/ATE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KanWasTaken/ATE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KanWasTaken/ATE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KanWasTaken/ATE") model = AutoModelForTokenClassification.from_pretrained("KanWasTaken/ATE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KanWasTaken/ATE: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/KanWasTaken/ATE/resolve/main/training_args.bin
- Command line
-
hf download hf://KanWasTaken/ATE/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KanWasTaken/ATE/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 2ba84364c819b68b81db44be99337cd32bd6c97281344291284cbd9272ccbb90
- Size of remote file:
- 5.3 kB
- SHA256:
- a4f7f6dd7323bf57845ca2768b4c50af426835d660ca1f73cdb19039a91f808d
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