Instructions to use Addy1008/adarsh-mBART-codemixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Addy1008/adarsh-mBART-codemixed with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Addy1008/adarsh-mBART-codemixed") model = AutoModelForSeq2SeqLM.from_pretrained("Addy1008/adarsh-mBART-codemixed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
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Checkpoint available in this repository is obtained after fine-tuning facebook/mbart-large-cc25 on
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mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
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Checkpoint available in this repository is obtained after fine-tuning facebook/mbart-large-cc25 on 10000 samples from manually generated corpus. This checkpoint gives decent results for Hindi-english translation.
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