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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---
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license: cc-by-4.0
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language: hi, en
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datasets:
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- L3Cube-HingCorpus
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---
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## HingBERT
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HingBERT is a Hindi-English code-mixed BERT model trained on roman text. It is a base BERT model fine-tuned on L3Cube-HingCorpus.
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<br>
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[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
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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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