Instructions to use abishekcodes/distil-new-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abishekcodes/distil-new-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abishekcodes/distil-new-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("abishekcodes/distil-new-ner") model = AutoModelForTokenClassification.from_pretrained("abishekcodes/distil-new-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a07c10a23fef6f8173dfe34ac1c149f34b0edf41c0cb768dc9f981e552eca40e
- Size of remote file:
- 5.84 kB
- SHA256:
- c10204a2ca89b107fed6fdf501ea7951e703c3b7e1462b78770d091a90a2ed4a
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