Instructions to use CATIE-AQ/NERmembert-large-4entities with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CATIE-AQ/NERmembert-large-4entities with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CATIE-AQ/NERmembert-large-4entities")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/NERmembert-large-4entities") model = AutoModelForTokenClassification.from_pretrained("CATIE-AQ/NERmembert-large-4entities", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CATIE-AQ/NERmembert-large-4entities: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/CATIE-AQ/NERmembert-large-4entities/resolve/main/training_args.bin
- Command line
-
hf download hf://CATIE-AQ/NERmembert-large-4entities/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CATIE-AQ/NERmembert-large-4entities/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 2087302e1ef36fcee820d07cde565edff9cb072d6173b7f4be6a91e8adf8b27f
- Size of remote file:
- 4.73 kB
- SHA256:
- 079ce4815691a78a77ea58eca46c1e395f7201bf77af65709a1a4cf098c58ce1
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