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README.md
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---
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license: apache-2.0
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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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- wiki_lingua
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metrics:
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- rouge
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model-index:
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- name: wiki_lingua-ar-8-8-5.6e-05-mt5-small-finetuned
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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: wiki_lingua
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type: wiki_lingua
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config: ar
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split: test
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args: ar
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.5417
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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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# wiki_lingua-ar-8-8-5.6e-05-mt5-small-finetuned
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3401
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- Rouge1: 0.5417
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- Rouge2: 0.0921
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- Rougel: 0.5445
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- Rougelsum: 0.5404
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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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| 3.851 | 1.0 | 2499 | 2.5949 | 0.4653 | 0.1378 | 0.4684 | 0.4631 |
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| 3.0409 | 2.0 | 4998 | 2.4790 | 0.4825 | 0.1156 | 0.4834 | 0.4798 |
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| 2.8824 | 3.0 | 7497 | 2.4273 | 0.5264 | 0.1331 | 0.5307 | 0.522 |
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| 2.7853 | 4.0 | 9996 | 2.3945 | 0.4879 | 0.1191 | 0.4871 | 0.4811 |
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| 2.7221 | 5.0 | 12495 | 2.3678 | 0.5655 | 0.0981 | 0.5672 | 0.5595 |
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| 2.6797 | 6.0 | 14994 | 2.3511 | 0.5002 | 0.1191 | 0.5084 | 0.4968 |
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| 2.6516 | 7.0 | 17493 | 2.3409 | 0.5606 | 0.1138 | 0.5631 | 0.5578 |
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| 2.6364 | 8.0 | 19992 | 2.3401 | 0.5417 | 0.0921 | 0.5445 | 0.5404 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 1.13.0
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- Datasets 2.1.0
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- Tokenizers 0.13.2
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