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--- |
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license: apache-2.0 |
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base_model: tsmatz/mt5_summarize_japanese |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5_summarize_japanese-6051-japanese |
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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: xlsum |
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type: xlsum |
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config: japanese |
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split: validation |
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args: japanese |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3394 |
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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_summarize_japanese-6051-japanese |
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This model is a fine-tuned version of [tsmatz/mt5_summarize_japanese](https://huggingface.co/tsmatz/mt5_summarize_japanese) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1045 |
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- Rouge1: 0.3394 |
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- Rouge2: 0.0501 |
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- Rougel: 0.3328 |
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- Rougelsum: 0.3321 |
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- Gen Len: 31.8808 |
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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: 5e-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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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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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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| 1.3889 | 4.5 | 500 | 1.1112 | 0.3381 | 0.05 | 0.3306 | 0.3302 | 31.3127 | |
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| 1.3351 | 8.99 | 1000 | 1.1045 | 0.3394 | 0.0501 | 0.3328 | 0.3321 | 31.8808 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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