Instructions to use AnanthZeke/tabert-4k-naamapadam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnanthZeke/tabert-4k-naamapadam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnanthZeke/tabert-4k-naamapadam")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AnanthZeke/tabert-4k-naamapadam") model = AutoModelForTokenClassification.from_pretrained("AnanthZeke/tabert-4k-naamapadam") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 306892d86f60940fcc6d02e3cfa7575cf47d31513abeddafb243ef1acd0418b6
- Size of remote file:
- 184 MB
- SHA256:
- 96477272e4849956beff5f12209f75b8d6d8c25cfa50284181dcec85564c4348
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