Instructions to use AnanthZeke/tabert-500-naamapadam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnanthZeke/tabert-500-naamapadam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnanthZeke/tabert-500-naamapadam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AnanthZeke/tabert-500-naamapadam") model = AutoModelForTokenClassification.from_pretrained("AnanthZeke/tabert-500-naamapadam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AnanthZeke/tabert-500-naamapadam: direct link, hf CLI and curl.
- Browser
- Download file 173 MB
-
https://huggingface.co/AnanthZeke/tabert-500-naamapadam/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AnanthZeke/tabert-500-naamapadam/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AnanthZeke/tabert-500-naamapadam/resolve/main/pytorch_model.bin
173 MB
- Xet hash:
- 63e51dc73bc2ff52dd5ddfd255027655ec38a063ba71eca9a6bbcd6b5d917860
- Size of remote file:
- 173 MB
- SHA256:
- 5c9220372ec399d95e354c83483960d358abccaac63485aa8b018d7ec204bfd1
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