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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/nllb-200-distilled-600M |
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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: nllb-200-distilled-600M-finetuned_ramayana_sns_prose_lexrank |
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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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# nllb-200-distilled-600M-finetuned_ramayana_sns_prose_lexrank |
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This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.8356 |
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- Rouge1: 15.0234 |
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- Rouge2: 1.2752 |
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- Rougel: 12.4341 |
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- Rougelsum: 13.036 |
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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: 5.6e-06 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| |
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| 4.9309 | 1.0 | 86 | 4.4877 | 17.9643 | 1.8368 | 13.627 | 16.743 | |
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| 4.5531 | 2.0 | 172 | 4.2961 | 7.7741 | 0.7819 | 6.5664 | 7.0319 | |
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| 4.4082 | 3.0 | 258 | 4.1867 | 15.3171 | 1.2616 | 12.5029 | 13.4603 | |
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| 4.3142 | 4.0 | 344 | 4.1128 | 14.7478 | 1.299 | 12.4545 | 12.8832 | |
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| 4.22 | 5.0 | 430 | 4.0577 | 14.6397 | 1.1974 | 12.3644 | 12.5395 | |
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| 4.1743 | 6.0 | 516 | 4.0173 | 14.7595 | 1.3556 | 12.4877 | 12.6788 | |
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| 4.1279 | 7.0 | 602 | 3.9858 | 14.3561 | 1.3361 | 12.071 | 12.5251 | |
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| 4.0927 | 8.0 | 688 | 3.9564 | 15.0213 | 1.3697 | 12.7109 | 13.1084 | |
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| 4.0625 | 9.0 | 774 | 3.9320 | 15.2813 | 1.3317 | 12.635 | 13.4154 | |
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| 4.0361 | 10.0 | 860 | 3.9113 | 15.0786 | 1.2544 | 12.6139 | 13.0141 | |
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| 3.9913 | 11.0 | 946 | 3.8951 | 15.0242 | 1.2899 | 12.7049 | 13.1678 | |
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| 3.9949 | 12.0 | 1032 | 3.8822 | 15.1567 | 1.3332 | 12.7349 | 13.1691 | |
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| 3.9643 | 13.0 | 1118 | 3.8724 | 15.0434 | 1.2552 | 12.6509 | 13.1845 | |
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| 3.96 | 14.0 | 1204 | 3.8608 | 14.5834 | 1.2768 | 12.1898 | 12.5734 | |
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| 3.9524 | 15.0 | 1290 | 3.8533 | 14.6872 | 1.2161 | 12.2549 | 12.7557 | |
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| 3.9345 | 16.0 | 1376 | 3.8443 | 15.0962 | 1.3235 | 12.5689 | 13.1217 | |
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| 3.9359 | 17.0 | 1462 | 3.8407 | 14.7724 | 1.2323 | 12.4059 | 12.6775 | |
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| 3.9213 | 18.0 | 1548 | 3.8367 | 14.6599 | 1.285 | 12.2237 | 12.7231 | |
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| 3.9213 | 19.0 | 1634 | 3.8356 | 15.0234 | 1.2752 | 12.4341 | 13.036 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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