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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/m2m100_418M |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- meteor |
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model-index: |
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- name: M2M101 |
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results: [] |
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pipeline_tag: translation |
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datasets: |
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- sarch7040/Deshika |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# praTran |
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This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on Deshika dataset which is a prallel corpus of Prakrit with their corresponding English Translations |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3269 |
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- Bleu: 8.4241 |
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- Meteor: 0.3851 |
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- Gen Len: 30.7356 |
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## Model Description |
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praTran is a finetuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) which was trained on a downstream task. |
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## Intended uses |
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This model is intended to use for academic purposes. |
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## Limitation |
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The models translation is not that good at this current stage to the language being extremely low resource. Impro |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:| |
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| No log | 1.0 | 74 | 4.9214 | 2.8147 | 0.2385 | 31.7864 | |
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| No log | 2.0 | 148 | 3.1788 | 5.0267 | 0.3144 | 29.7695 | |
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| No log | 3.0 | 222 | 2.0374 | 6.4844 | 0.3399 | 30.2237 | |
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| No log | 4.0 | 296 | 1.4798 | 7.4708 | 0.3768 | 31.3932 | |
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| No log | 5.0 | 370 | 1.3269 | 8.4241 | 0.3851 | 30.7356 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
