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--- |
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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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datasets: |
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- eur-lex-sum |
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metrics: |
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- rouge |
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model-index: |
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- name: T5_small_eurlexsum |
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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: eur-lex-sum |
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type: eur-lex-sum |
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config: french |
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split: test |
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args: french |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2 |
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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_eurlexsum |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eur-lex-sum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1159 |
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- Rouge1: 0.2 |
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- Rouge2: 0.1394 |
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- Rougel: 0.1833 |
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- Rougelsum: 0.1829 |
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- Gen Len: 19.0 |
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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: 4 |
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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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| No log | 1.0 | 71 | 1.4740 | 0.1718 | 0.0935 | 0.1476 | 0.1476 | 19.0 | |
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| No log | 2.0 | 142 | 1.2138 | 0.1915 | 0.1207 | 0.1719 | 0.1719 | 19.0 | |
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| No log | 3.0 | 213 | 1.1368 | 0.1953 | 0.1306 | 0.1759 | 0.1759 | 19.0 | |
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| No log | 4.0 | 284 | 1.1159 | 0.2 | 0.1394 | 0.1833 | 0.1829 | 19.0 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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