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")# pip install -U transformers accelerate # 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
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## TamilBERT
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TamilBERT is a Tamil BERT model trained on publicly available Tamil monolingual datasets.
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Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>]
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```
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@article{joshi2022l3cubehind,
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title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
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## TamilBERT
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TamilBERT is a Tamil BERT model trained on publicly available Tamil monolingual datasets.
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Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] .
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Citing:
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```
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@article{joshi2022l3cubehind,
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title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
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