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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- scitldr |
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metrics: |
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- rouge |
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model-index: |
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- name: paper-summary |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: scitldr |
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type: scitldr |
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config: Abstract |
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split: train |
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args: Abstract |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3484 |
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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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# paper-summary |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scitldr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8631 |
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- Rouge1: 0.3484 |
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- Rouge2: 0.1596 |
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- Rougel: 0.2971 |
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- Rougelsum: 0.3047 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 8 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 3.0545 | 1.0 | 63 | 2.9939 | 0.3387 | 0.1538 | 0.2887 | 0.2957 | |
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| 2.7871 | 2.0 | 126 | 2.9360 | 0.3448 | 0.1577 | 0.2947 | 0.3019 | |
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| 2.7188 | 3.0 | 189 | 2.8977 | 0.3477 | 0.1585 | 0.2967 | 0.3035 | |
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| 2.6493 | 4.0 | 252 | 2.8837 | 0.3488 | 0.1597 | 0.2973 | 0.3046 | |
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| 2.6207 | 5.0 | 315 | 2.8690 | 0.3472 | 0.1566 | 0.2958 | 0.3033 | |
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| 2.5893 | 6.0 | 378 | 2.8668 | 0.3493 | 0.1592 | 0.2972 | 0.305 | |
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| 2.5494 | 7.0 | 441 | 2.8657 | 0.3486 | 0.1595 | 0.2976 | 0.3053 | |
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| 2.5554 | 8.0 | 504 | 2.8631 | 0.3484 | 0.1596 | 0.2971 | 0.3047 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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