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nllb-200-distilled-600M-finetuned_srimadbhagavatam_sns
This model is a fine-tuned version of facebook/nllb-200-distilled-600M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9632
- Rouge1: 39.9844
- Rouge2: 15.8187
- Rougel: 24.7601
- Rougelsum: 37.8611
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 4.2029 | 1.0 | 193 | 3.5530 | 17.4525 | 1.8199 | 14.417 | 15.7939 |
| 3.6789 | 2.0 | 386 | 3.2385 | 18.4399 | 2.3063 | 14.4777 | 16.8663 |
| 3.4121 | 3.0 | 579 | 2.9913 | 18.6292 | 2.1671 | 14.0775 | 17.4039 |
| 3.1958 | 4.0 | 772 | 2.7935 | 20.9044 | 3.0869 | 15.7866 | 19.4597 |
| 3.0238 | 5.0 | 965 | 2.6154 | 22.9863 | 3.1733 | 15.4087 | 21.6705 |
| 2.8546 | 6.0 | 1158 | 2.4343 | 24.7063 | 4.0564 | 16.1424 | 23.2821 |
| 2.7 | 7.0 | 1351 | 2.2810 | 26.2011 | 4.6714 | 16.7887 | 24.6723 |
| 2.5532 | 8.0 | 1544 | 2.1071 | 30.7319 | 6.3718 | 17.4858 | 28.8254 |
| 2.42 | 9.0 | 1737 | 1.9742 | 28.5217 | 5.2919 | 16.9577 | 26.5686 |
| 2.2991 | 10.0 | 1930 | 1.8234 | 29.8937 | 6.3088 | 17.2141 | 28.0302 |
| 2.1851 | 11.0 | 2123 | 1.7177 | 29.8642 | 6.9874 | 18.2935 | 28.0493 |
| 2.0829 | 12.0 | 2316 | 1.5891 | 30.7551 | 6.7111 | 18.1772 | 28.8555 |
| 1.9954 | 13.0 | 2509 | 1.4965 | 32.6313 | 8.0662 | 18.4981 | 30.8014 |
| 1.9055 | 14.0 | 2702 | 1.3996 | 33.0299 | 9.6554 | 19.2763 | 31.2127 |
| 1.8372 | 15.0 | 2895 | 1.3271 | 35.4767 | 10.7234 | 20.2759 | 33.1856 |
| 1.7635 | 16.0 | 3088 | 1.2533 | 35.5164 | 11.5198 | 21.3301 | 33.2617 |
| 1.7052 | 17.0 | 3281 | 1.1865 | 37.5692 | 13.6047 | 22.9496 | 35.2626 |
| 1.6495 | 18.0 | 3474 | 1.1414 | 37.7493 | 13.6471 | 22.6947 | 35.6368 |
| 1.6009 | 19.0 | 3667 | 1.0859 | 40.251 | 15.2568 | 24.4602 | 37.955 |
| 1.5589 | 20.0 | 3860 | 1.0536 | 37.8875 | 14.5794 | 23.4696 | 35.8989 |
| 1.5209 | 21.0 | 4053 | 1.0268 | 38.4126 | 14.9535 | 24.3597 | 36.435 |
| 1.4963 | 22.0 | 4246 | 0.9982 | 40.9518 | 16.6418 | 25.284 | 38.5787 |
| 1.4651 | 23.0 | 4439 | 0.9771 | 39.4774 | 16.4189 | 24.7979 | 37.3614 |
| 1.451 | 24.0 | 4632 | 0.9662 | 40.4131 | 16.5895 | 25.0073 | 38.3018 |
| 1.4351 | 25.0 | 4825 | 0.9632 | 39.9844 | 15.8187 | 24.7601 | 37.8611 |
Framework versions
- Transformers 4.28.0
- Pytorch 1.12.1
- Datasets 2.14.4
- Tokenizers 0.13.3
