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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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- summarization |
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- generated_from_trainer |
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datasets: |
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- multi_news |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-Abstractive-Summarizer |
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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: multi_news |
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type: multi_news |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 15.0486 |
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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-Abstractive-Summarizer |
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This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8162 |
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- Rouge1: 15.0486 |
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- Rouge2: 5.1197 |
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- Rougel: 12.0288 |
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- Rougelsum: 13.4434 |
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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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| 2.7895 | 1.0 | 113 | 2.7479 | 15.6051 | 5.1347 | 12.2224 | 13.8875 | |
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| 2.6034 | 2.0 | 226 | 2.7745 | 15.2054 | 4.7334 | 11.7555 | 13.436 | |
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| 2.4515 | 3.0 | 339 | 2.7820 | 15.008 | 4.6612 | 11.5993 | 13.3788 | |
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| 2.3439 | 4.0 | 452 | 2.8056 | 15.2475 | 4.9304 | 11.8552 | 13.4615 | |
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| 2.2803 | 5.0 | 565 | 2.8162 | 15.0486 | 5.1197 | 12.0288 | 13.4434 | |
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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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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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