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--- |
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base_model: sanikaska/rut5_gazeta_title_generation |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gazeta |
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metrics: |
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- rouge |
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model-index: |
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- name: rut5_gazeta_title_generation |
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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: gazeta |
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type: gazeta |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.0594 |
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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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# rut5_gazeta_title_generation |
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This model is a fine-tuned version of [sanikaska/rut5_gazeta_title_generation](https://huggingface.co/sanikaska/rut5_gazeta_title_generation) on the gazeta dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5882 |
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- Rouge1: 0.0594 |
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- Rouge2: 0.0105 |
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- Rougel: 0.0592 |
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- Rougelsum: 0.0592 |
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- Gen Len: 9.6443 |
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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: 4 |
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- eval_batch_size: 4 |
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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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- 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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| 2.756 | 1.0 | 2500 | 2.4833 | 0.0516 | 0.0087 | 0.0514 | 0.0513 | 10.4538 | |
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| 2.4648 | 2.0 | 5000 | 2.4972 | 0.0583 | 0.0108 | 0.0582 | 0.0581 | 9.9832 | |
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| 2.306 | 3.0 | 7500 | 2.5375 | 0.0594 | 0.0104 | 0.0592 | 0.0592 | 9.4259 | |
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| 2.1811 | 4.0 | 10000 | 2.5882 | 0.0594 | 0.0105 | 0.0592 | 0.0592 | 9.6443 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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