Instructions to use l3cube-pune/hing-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-roberta") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-roberta") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -19,6 +19,17 @@ HingRoBERTa is a Hindi-English code-mixed RoBERTa model trained on roman text. I
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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398)
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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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More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398)
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Other models from HingBERT family: <br>
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<a href="https://huggingface.co/l3cube-pune/hing-bert"> HingBERT </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-mbert"> HingMBERT </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-mbert-mixed"> HingBERT-Mixed </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-roberta"> HingRoBERTa </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-robera-mixed"> HingRoBERTa-Mixed </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-gpt"> HingGPT </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-gpt-devanagari"> HingGPT-Devanagari </a> <br>
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<a href="https://huggingface.co/l3cube-pune/hing-bert-lid"> HingBERT-LID </a> <br>
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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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