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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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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-cnn-news |
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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: cnn_dailymail |
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type: cnn_dailymail |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 24.7231 |
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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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# t5-small-finetuned-cnn-news |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8412 |
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- Rouge1: 24.7231 |
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- Rouge2: 12.292 |
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- Rougel: 20.5347 |
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- Rougelsum: 23.4668 |
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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: 0.00056 |
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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: 5 |
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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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| 2.0318 | 1.0 | 718 | 1.8028 | 24.5415 | 12.0907 | 20.5343 | 23.3386 | |
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| 1.8307 | 2.0 | 1436 | 1.8028 | 24.0965 | 11.6367 | 20.2078 | 22.8138 | |
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| 1.6881 | 3.0 | 2154 | 1.8136 | 25.0822 | 12.6509 | 20.9523 | 23.8303 | |
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| 1.5778 | 4.0 | 2872 | 1.8269 | 24.4271 | 11.8443 | 20.2281 | 23.0941 | |
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| 1.501 | 5.0 | 3590 | 1.8412 | 24.7231 | 12.292 | 20.5347 | 23.4668 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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