Instructions to use ar5entum/bart_dev_rom_tl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ar5entum/bart_dev_rom_tl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ar5entum/bart_dev_rom_tl") model = AutoModelForSeq2SeqLM.from_pretrained("ar5entum/bart_dev_rom_tl") - Notebooks
- Google Colab
- Kaggle
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README.md
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# bart_dev_rom_tl
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This model is a fine-tuned version of [ar5entum/
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It achieves the following results on the evaluation set:
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- Loss: 0.8156
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- Bleu: 40.6409
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# bart_dev_rom_tl
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This model is a fine-tuned version of [ar5entum/bart_hin_eng_mt](https://huggingface.co/ar5entum/bart_hin_eng_mt) on [ar5entum/hindi-english-roman-devnagiri-transliteration-corpus](https://huggingface.co/datasets/ar5entum/hindi-english-roman-devnagiri-transliteration-corpus/) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8156
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- Bleu: 40.6409
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