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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: 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: cnn_news_summary_model_trained_on_reduced_data |
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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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# cnn_news_summary_model_trained_on_reduced_data |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an cnn_daily_mail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6040 |
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- Rouge1: 0.2183 |
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- Rouge2: 0.0946 |
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- Rougel: 0.1843 |
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- Rougelsum: 0.1842 |
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- Generated Length: 19.0 |
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## Model Description |
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The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): |
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> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. |
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T5-Small is the checkpoint with 60 million parameters. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 3 |
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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 | Generated Length | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:| |
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| No log | 1.0 | 431 | 1.6239 | 0.2171 | 0.0934 | 0.1827 | 0.1827 | 19.0 | |
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| 1.9203 | 2.0 | 862 | 1.6075 | 0.2166 | 0.0937 | 0.1828 | 0.1827 | 19.0 | |
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| 1.822 | 3.0 | 1293 | 1.6040 | 0.2183 | 0.0946 | 0.1843 | 0.1842 | 19.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Tokenizers 0.19.1 |
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