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")# 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 training_args.bin from AnanthZeke/tabert-500-naamapadam: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/AnanthZeke/tabert-500-naamapadam/resolve/main/training_args.bin
- Command line
-
hf download hf://AnanthZeke/tabert-500-naamapadam/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AnanthZeke/tabert-500-naamapadam/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 9cc337e611063b4d5c0d5bbda23c6a255fc17780befd2155c9a788d3fe17e0e2
- Size of remote file:
- 3.96 kB
- SHA256:
- 7e021a4cb1f2bcb70f6b823b4345b914f83a2c34c42047bfdd51f7356617545c
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