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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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- xsum |
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metrics: |
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- rouge |
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base_model: t5-small |
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pipeline_tag: summarization |
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model-index: |
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- name: t5-small-finetuned-xsum |
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results: |
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- task: |
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type: text2text-generation |
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name: Sequence-to-sequence Language Modeling |
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dataset: |
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name: xsum |
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type: xsum |
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config: default |
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split: train[:10%] |
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args: default |
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metrics: |
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- type: rouge |
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value: 27.0616 |
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name: Rouge1 |
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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-xsum |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5622 |
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- Rouge1: 27.0616 |
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- Rouge2: 6.8574 |
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- Rougel: 21.1087 |
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- Rougelsum: 21.1175 |
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- Gen Len: 18.8246 |
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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: 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: 5 |
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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 | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
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| 2.8879 | 1.0 | 1148 | 2.6353 | 25.4786 | 5.8199 | 19.7404 | 19.7497 | 18.8089 | |
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| 2.8178 | 2.0 | 2296 | 2.5951 | 26.2963 | 6.4255 | 20.5395 | 20.5304 | 18.8084 | |
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| 2.7831 | 3.0 | 3444 | 2.5741 | 26.7181 | 6.7174 | 20.8888 | 20.8914 | 18.806 | |
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| 2.7572 | 4.0 | 4592 | 2.5647 | 27.0071 | 6.8335 | 21.108 | 21.1149 | 18.8202 | |
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| 2.7476 | 5.0 | 5740 | 2.5622 | 27.0616 | 6.8574 | 21.1087 | 21.1175 | 18.8246 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
