Instructions to use AnanthZeke/tabert-1k-naamapadam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnanthZeke/tabert-1k-naamapadam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnanthZeke/tabert-1k-naamapadam")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AnanthZeke/tabert-1k-naamapadam") model = AutoModelForTokenClassification.from_pretrained("AnanthZeke/tabert-1k-naamapadam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AnanthZeke/tabert-1k-naamapadam: direct link, hf CLI and curl.
- Browser
- Download file 175 MB
-
https://huggingface.co/AnanthZeke/tabert-1k-naamapadam/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AnanthZeke/tabert-1k-naamapadam/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AnanthZeke/tabert-1k-naamapadam/resolve/main/pytorch_model.bin
175 MB
- Xet hash:
- 48c3d7248f7c701dd84b6d04b4ed8ffc41149a5a1ce8022c3c147052f9fb67bf
- Size of remote file:
- 175 MB
- SHA256:
- 8606044546017a19c7585090c49d7fddb8809666c20d25ade6a7b46b4c517f71
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