Instructions to use l3cube-pune/tamil-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/tamil-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/tamil-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/tamil-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/tamil-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42b2912e944501028ad53b61e4503e131f734e89f032ce5850f96c9666b797b8
- Size of remote file:
- 951 MB
- SHA256:
- 571ec300921a08e65f0433f49b9f5f47bcd831002385ffaf69f8d89f35c5dfec
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