Instructions to use smitmenon/e2m_denoise_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smitmenon/e2m_denoise_finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("smitmenon/e2m_denoise_finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("smitmenon/e2m_denoise_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fine-tuned with FLORES dataset for English-to-Malayalam translation version mbart_mldenoised_v2
Browse files- README.md +5 -5
- generation_config.json +1 -1
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [smitmenon/e2m_endenoise_project](https://huggingface.co/smitmenon/e2m_endenoise_project) on the flores dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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- Transformers 4.
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- Pytorch 2.5.1+cu121
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- Datasets 3.1.0
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- Tokenizers 0.
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This model is a fine-tuned version of [smitmenon/e2m_endenoise_project](https://huggingface.co/smitmenon/e2m_endenoise_project) on the flores dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6116
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.6398 | 1.0 | 125 | 0.6094 |
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| 0.4651 | 2.0 | 250 | 0.6116 |
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### Framework versions
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- Transformers 4.47.0
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- Pytorch 2.5.1+cu121
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- Datasets 3.1.0
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- Tokenizers 0.21.0
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generation_config.json
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"max_length": 200,
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"num_beams": 5,
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"pad_token_id": 1,
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"transformers_version": "4.
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}
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"max_length": 200,
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"num_beams": 5,
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"pad_token_id": 1,
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"transformers_version": "4.47.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2444578688
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