Instructions to use AnanthZeke/TryNER-tabert-500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnanthZeke/TryNER-tabert-500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnanthZeke/TryNER-tabert-500")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AnanthZeke/TryNER-tabert-500") model = AutoModelForTokenClassification.from_pretrained("AnanthZeke/TryNER-tabert-500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8914c6f3e2e6f029c665632234409ec6a55db0b9f91a18d0cf625cbb774cd069
- Size of remote file:
- 173 MB
- SHA256:
- 1f829bc31b8e7b15b20cd14dd5d7c41b0819720ea1763a026aaea51a274c09a6
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