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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/flan-t5-small |
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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: flan-t5-small-compression |
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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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# flan-t5-small-compression |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5181 |
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- Rouge1: 0.8820 |
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- Rouge2: 0.7104 |
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- Rougel: 0.8485 |
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- Rougelsum: 0.8488 |
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- Comp Ratio Mean: 0.6611 |
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- Comp Ratio P90: 0.7674 |
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- Pct Violations: 0.0 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adafactor and the args are: |
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No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Comp Ratio Mean | Comp Ratio P90 | Pct Violations | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:---------------:|:--------------:|:--------------:| |
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| 1.2576 | 1.0 | 1594 | 0.6457 | 0.8528 | 0.6587 | 0.8197 | 0.8199 | 0.6626 | 0.7736 | 0.0 | |
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| 0.7688 | 2.0 | 3188 | 0.5727 | 0.8689 | 0.6851 | 0.8345 | 0.8349 | 0.6647 | 0.7694 | 0.0 | |
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| 0.6591 | 3.0 | 4782 | 0.5405 | 0.8750 | 0.6963 | 0.8413 | 0.8417 | 0.6684 | 0.7692 | 0.0 | |
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| 0.5957 | 4.0 | 6376 | 0.5333 | 0.8771 | 0.7002 | 0.8438 | 0.8440 | 0.6600 | 0.7660 | 0.0 | |
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| 0.548 | 5.0 | 7970 | 0.5212 | 0.8792 | 0.7059 | 0.8467 | 0.8470 | 0.6617 | 0.7648 | 0.0004 | |
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| 0.5139 | 6.0 | 9564 | 0.5196 | 0.8799 | 0.7064 | 0.8472 | 0.8473 | 0.6597 | 0.7636 | 0.0 | |
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| 0.4862 | 7.0 | 11158 | 0.5144 | 0.8805 | 0.7076 | 0.8473 | 0.8474 | 0.6656 | 0.7705 | 0.0004 | |
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| 0.466 | 8.0 | 12752 | 0.5157 | 0.8819 | 0.7098 | 0.8489 | 0.8492 | 0.6622 | 0.7674 | 0.0 | |
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| 0.4499 | 9.0 | 14346 | 0.5156 | 0.8816 | 0.7096 | 0.8486 | 0.8489 | 0.6604 | 0.7660 | 0.0 | |
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| 0.4393 | 10.0 | 15940 | 0.5181 | 0.8820 | 0.7104 | 0.8485 | 0.8488 | 0.6611 | 0.7674 | 0.0 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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