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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: 0.2967 |
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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: 3.1243 |
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- Rouge1: 0.2967 |
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- Rouge2: 0.1277 |
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- Rougel: 0.2457 |
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- Rougelsum: 0.2602 |
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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-05 |
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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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| 2.9845 | 1.0 | 63 | 3.2358 | 0.288 | 0.1219 | 0.238 | 0.2522 | |
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| 2.7469 | 2.0 | 126 | 3.1815 | 0.2922 | 0.1251 | 0.2415 | 0.2562 | |
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| 2.7032 | 3.0 | 189 | 3.1564 | 0.2991 | 0.1303 | 0.2474 | 0.2619 | |
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| 2.6479 | 4.0 | 252 | 3.1354 | 0.2973 | 0.1282 | 0.2457 | 0.2608 | |
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| 2.6303 | 5.0 | 315 | 3.1297 | 0.2964 | 0.1272 | 0.2451 | 0.2598 | |
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| 2.6115 | 6.0 | 378 | 3.1243 | 0.2967 | 0.1277 | 0.2457 | 0.2602 | |
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| 2.5704 | 7.0 | 441 | 3.1264 | 0.2967 | 0.127 | 0.2453 | 0.26 | |
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| 2.6028 | 8.0 | 504 | 3.1252 | 0.2971 | 0.1275 | 0.2456 | 0.2604 | |
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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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