Instructions to use l3cube-pune/hing-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -21,8 +21,7 @@ More details on the dataset, models, and baseline results can be found in our [p
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```
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@inproceedings{nayak-joshi-2022-l3cube,
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title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models",
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author = "Nayak, Ravindra and
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Joshi, Raviraj",
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booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference",
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month = jun,
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year = "2022",
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```
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@inproceedings{nayak-joshi-2022-l3cube,
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title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models",
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author = "Nayak, Ravindra and Joshi, Raviraj",
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booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference",
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month = jun,
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year = "2022",
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