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:
- 29f6d3d6c64624c5b8207abab5787d9482ca427a7b9db6e2293aff4ebd3c2606
- Size of remote file:
- 3.71 kB
- SHA256:
- b81268ee660f23dda8f06befbf46a33d4129be7165de924153fa169ad6649278
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