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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: google/mt5-base |
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metrics: |
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- bleu |
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- rouge |
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model-index: |
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- name: mt5-base-ICFOSS-malayalam_Hindi_Translator |
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results: [] |
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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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# mt5-base-ICFOSS-malayalam_Hindi_Translator |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2179 |
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- Bleu: 6.2035 |
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- Rouge: {'rouge1': 0.2667970960136926, 'rouge2': 0.14574925525428614, 'rougeL': 0.26511828595423204, 'rougeLsum': 0.26501665904942706} |
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- Chrf: {'score': 23.454551827072866, 'char_order': 6, 'word_order': 0, 'beta': 2} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 0.0002 |
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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: cosine |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Chrf | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:| |
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| 2.5515 | 1.0 | 4315 | 1.2874 | 5.8306 | {'rouge1': 0.2660910934739513, 'rouge2': 0.14404792849379128, 'rougeL': 0.26384549634107013, 'rougeLsum': 0.2637751499455684} | {'score': 22.571342084258088, 'char_order': 6, 'word_order': 0, 'beta': 2} | |
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| 1.9143 | 2.0 | 8630 | 1.2319 | 6.1128 | {'rouge1': 0.263256301663898, 'rouge2': 0.14256738224583015, 'rougeL': 0.261282034035635, 'rougeLsum': 0.2613517649673947} | {'score': 23.235214776547263, 'char_order': 6, 'word_order': 0, 'beta': 2} | |
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| 1.8644 | 3.0 | 12945 | 1.2192 | 6.2145 | {'rouge1': 0.2670714744552978, 'rouge2': 0.14606073298261613, 'rougeL': 0.2652594809906982, 'rougeLsum': 0.26489596193447795} | {'score': 23.438449086905997, 'char_order': 6, 'word_order': 0, 'beta': 2} | |
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| 1.8539 | 4.0 | 17260 | 1.2179 | 6.2043 | {'rouge1': 0.26678061058524805, 'rouge2': 0.14565482302690236, 'rougeL': 0.26489350144733725, 'rougeLsum': 0.26477198178581135} | {'score': 23.464895899326955, 'char_order': 6, 'word_order': 0, 'beta': 2} | |
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| 1.8525 | 5.0 | 21575 | 1.2179 | 6.2035 | {'rouge1': 0.2667970960136926, 'rouge2': 0.14574925525428614, 'rougeL': 0.26511828595423204, 'rougeLsum': 0.26501665904942706} | {'score': 23.454551827072866, 'char_order': 6, 'word_order': 0, 'beta': 2} | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
