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--- |
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license: apache-2.0 |
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base_model: t5-base |
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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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- wer |
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model-index: |
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- name: t-5-base-abs2abs |
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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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# t-5-base-abs2abs |
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3203 |
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- Rouge1: 0.6446 |
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- Rouge2: 0.3626 |
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- Rougel: 0.5773 |
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- Rougelsum: 0.5771 |
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- Wer: 0.5292 |
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- Bleurt: -0.1862 |
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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: 2e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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: 2 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:-------:| |
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| No log | 0.14 | 250 | 1.4708 | 0.6226 | 0.3343 | 0.5514 | 0.5512 | 0.559 | -0.1681 | |
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| 1.9361 | 0.27 | 500 | 1.4181 | 0.6277 | 0.3422 | 0.5591 | 0.5588 | 0.5498 | -0.1527 | |
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| 1.9361 | 0.41 | 750 | 1.3918 | 0.6326 | 0.3467 | 0.5633 | 0.5632 | 0.5453 | -0.1653 | |
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| 1.5072 | 0.55 | 1000 | 1.3740 | 0.6352 | 0.3508 | 0.5664 | 0.5662 | 0.541 | -0.1653 | |
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| 1.5072 | 0.68 | 1250 | 1.3602 | 0.6369 | 0.3528 | 0.5687 | 0.5685 | 0.539 | -0.4817 | |
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| 1.4761 | 0.82 | 1500 | 1.3504 | 0.6388 | 0.3557 | 0.5711 | 0.571 | 0.5361 | -0.1653 | |
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| 1.4761 | 0.96 | 1750 | 1.3424 | 0.6399 | 0.3573 | 0.5728 | 0.5725 | 0.5341 | -0.1653 | |
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| 1.4475 | 1.09 | 2000 | 1.3368 | 0.6413 | 0.3586 | 0.5737 | 0.5735 | 0.5329 | -0.4817 | |
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| 1.4475 | 1.23 | 2250 | 1.3324 | 0.6422 | 0.36 | 0.5748 | 0.5746 | 0.5316 | -0.4726 | |
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| 1.4375 | 1.36 | 2500 | 1.3280 | 0.6435 | 0.3608 | 0.5757 | 0.5754 | 0.5309 | -0.3069 | |
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| 1.4375 | 1.5 | 2750 | 1.3246 | 0.644 | 0.3618 | 0.5765 | 0.5763 | 0.5304 | -0.1862 | |
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| 1.4053 | 1.64 | 3000 | 1.3222 | 0.6443 | 0.3622 | 0.5769 | 0.5767 | 0.5296 | -0.1862 | |
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| 1.4053 | 1.77 | 3250 | 1.3208 | 0.6446 | 0.3625 | 0.5771 | 0.5769 | 0.5293 | -0.1862 | |
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| 1.3911 | 1.91 | 3500 | 1.3203 | 0.6446 | 0.3626 | 0.5773 | 0.5771 | 0.5292 | -0.1862 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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