Instructions to use CouchCat/ma_ner_v6_distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CouchCat/ma_ner_v6_distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CouchCat/ma_ner_v6_distil")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v6_distil") model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v6_distil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CouchCat/ma_ner_v6_distil: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/CouchCat/ma_ner_v6_distil/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CouchCat/ma_ner_v6_distil/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CouchCat/ma_ner_v6_distil/resolve/main/pytorch_model.bin
261 MB
- Xet hash:
- b1f9fee9a1d47d071f1464e30018ce5432dca1838601abc7a457c120c7f57ceb
- Size of remote file:
- 261 MB
- SHA256:
- 1938b801b1c8e46b96fd20b7ed31561caf1c883675ec51fba7100e7888ae646b
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