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--- |
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base_model: facebook/mbart-large-50-many-to-many-mmt |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: summarizer-tamil-mbart |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/codebot/bart_tam_sum/runs/r6mx7h63) |
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# summarizer-tamil-mbart |
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This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4252 |
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- Rouge1: 9.3056 |
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- Rouge2: 2.0 |
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- Rougel: 9.2889 |
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- Rougelsum: 9.2222 |
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- Gen Len: 39.2233 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 5 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:------:|:----:|:-------:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 4.5487 | 0.2963 | 200 | 39.11 | 4.1027 | 2.1667 | 0.3333 | 2.1111 | 2.1667 | |
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| 4.0997 | 0.5926 | 400 | 41.7633 | 3.9744 | 2.2222 | 0.3333 | 2.2593 | 2.2222 | |
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| 4.0165 | 0.8889 | 600 | 52.0967 | 3.9417 | 2.6667 | 0.6667 | 2.6667 | 2.7778 | |
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| 3.7801 | 1.1852 | 800 | 45.3967 | 3.9424 | 2.1444 | 0.0 | 2.2222 | 2.1333 | |
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| 3.7308 | 1.4815 | 1000 | 41.3833 | 3.9573 | 2.7333 | 0.2222 | 2.6063 | 2.7905 | |
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| 3.7946 | 1.7778 | 1200 | 35.37 | 3.8979 | 1.0571 | 0.2222 | 0.9619 | 1.0571 | |
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| 3.6338 | 2.0741 | 1400 | 30.9567 | 3.9569 | 1.6611 | 0.3333 | 1.6333 | 1.6833 | |
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| 3.2282 | 2.3704 | 1600 | 42.4933 | 3.0726 | 4.0698 | 0.3889 | 3.9754 | 3.9825 | |
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| 3.1351 | 2.6667 | 1800 | 38.48 | 3.0771 | 2.8333 | 0.0 | 2.8095 | 2.8333 | |
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| 3.1739 | 2.9630 | 2000 | 40.04 | 3.0871 | 2.4921 | 0.0 | 2.496 | 2.4762 | |
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| 2.8247 | 3.2593 | 2200 | 39.95 | 3.0882 | 3.4706 | 0.2222 | 3.4421 | 3.4357 | |
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| 2.7748 | 3.5556 | 2400 | 38.29 | 3.0735 | 3.0 | 0.0 | 3.0 | 3.0 | |
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| 2.5244 | 3.8519 | 2600 | 2.4450 | 7.3889 | 1.2222 | 7.4667 | 7.5 | 38.1767 | |
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| 2.5382 | 4.1481 | 2800 | 2.4365 | 8.1111 | 1.9744 | 8.2111 | 8.1667 | 39.3333 | |
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| 2.4642 | 4.4444 | 3000 | 2.4334 | 8.3889 | 2.1905 | 8.5389 | 8.4444 | 37.7767 | |
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| 2.4641 | 4.7407 | 3200 | 2.4252 | 9.3056 | 2.0 | 9.2889 | 9.2222 | 39.2233 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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