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")# 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
- Xet hash:
- b1f9fee9a1d47d071f1464e30018ce5432dca1838601abc7a457c120c7f57ceb
- Size of remote file:
- 261 MB
- SHA256:
- 1938b801b1c8e46b96fd20b7ed31561caf1c883675ec51fba7100e7888ae646b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.