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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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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: TGL-3 |
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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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# TGL-3 |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an abstract-summary dataset, |
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23000 pieces of data for training. The data was acquired by openreview.net. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4435 |
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- Rouge1: 36.4998 |
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- Rouge2: 17.8322 |
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- Rougel: 31.8632 |
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- Rougelsum: 31.8341 |
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## Model description |
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Here is the paper https://arxiv.org/abs/1910.10683 |
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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: 8 |
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- eval_batch_size: 8 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 2.9096 | 1.0 | 1240 | 2.5721 | 36.234 | 17.8214 | 31.5514 | 31.5549 | |
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| 2.7259 | 2.0 | 2480 | 2.5258 | 36.2572 | 17.9912 | 31.6249 | 31.6441 | |
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| 2.6434 | 3.0 | 3720 | 2.4957 | 36.4623 | 17.9657 | 31.7693 | 31.7542 | |
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| 2.5896 | 4.0 | 4960 | 2.4663 | 36.3692 | 17.8372 | 31.5909 | 31.6089 | |
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| 2.5491 | 5.0 | 6200 | 2.4511 | 36.4775 | 17.8094 | 31.8102 | 31.8003 | |
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| 2.5183 | 6.0 | 7440 | 2.4440 | 36.5892 | 17.906 | 31.9058 | 31.8985 | |
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| 2.4997 | 7.0 | 8680 | 2.4438 | 36.3747 | 17.8309 | 31.7314 | 31.7178 | |
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| 2.4863 | 8.0 | 9920 | 2.4435 | 36.4998 | 17.8322 | 31.8632 | 31.8341 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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