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--- |
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license: mit |
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base_model: facebook/mbart-large-50 |
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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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- lr-sum |
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metrics: |
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- rouge |
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model-index: |
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- name: mbart-large-50-finetuned-lrsum-fr |
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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: lr-sum |
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type: lr-sum |
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config: fra |
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split: validation |
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args: fra |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2579 |
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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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# mbart-large-50-finetuned-lrsum-fr |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the lr-sum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0341 |
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- Rouge1: 0.2579 |
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- Rouge2: 0.1232 |
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- Rougel: 0.2142 |
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- Rougelsum: 0.2153 |
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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: 8 |
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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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| 1.8023 | 1.0 | 141 | 1.2331 | 0.2511 | 0.115 | 0.205 | 0.2088 | |
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| 8.3626 | 2.0 | 282 | 1.3380 | 0.2601 | 0.1213 | 0.2106 | 0.2155 | |
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| 0.848 | 3.0 | 423 | 1.5333 | 0.2431 | 0.1109 | 0.2008 | 0.2022 | |
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| 0.4302 | 4.0 | 564 | 1.4443 | 0.2487 | 0.1153 | 0.204 | 0.2063 | |
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| 0.2181 | 5.0 | 705 | 1.6967 | 0.2445 | 0.1081 | 0.1977 | 0.2001 | |
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| 0.1131 | 6.0 | 846 | 1.8275 | 0.2704 | 0.1358 | 0.2249 | 0.2265 | |
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| 0.052 | 7.0 | 987 | 1.9579 | 0.2549 | 0.1161 | 0.2085 | 0.2099 | |
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| 0.0245 | 8.0 | 1128 | 2.0341 | 0.2579 | 0.1232 | 0.2142 | 0.2153 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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