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--- |
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license: apache-2.0 |
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base_model: google/mt5-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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- mlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-finetuned-mlsum |
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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: mlsum |
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type: mlsum |
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config: fr |
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split: validation |
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args: fr |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 23.8523 |
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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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# mt5-small-finetuned-mlsum |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1938 |
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- Rouge1: 23.8523 |
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- Rouge2: 11.7959 |
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- Rougel: 21.1838 |
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- Rougelsum: 21.2463 |
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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: 5.6e-05 |
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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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| 5.6087 | 1.0 | 1005 | 2.4269 | 29.6042 | 15.5378 | 25.5964 | 25.6503 | |
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| 3.4099 | 2.0 | 2010 | 2.2734 | 23.8963 | 12.2351 | 21.4806 | 21.4861 | |
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| 3.169 | 3.0 | 3015 | 2.2310 | 26.7408 | 13.7129 | 23.7543 | 23.8443 | |
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| 3.0327 | 4.0 | 4020 | 2.2084 | 23.2971 | 11.5675 | 20.911 | 21.0564 | |
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| 2.9777 | 5.0 | 5025 | 2.1938 | 23.8523 | 11.7959 | 21.1838 | 21.2463 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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