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--- |
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license: apache-2.0 |
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base_model: google/mt5-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- thaisum |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5_thaisum_model |
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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: thaisum |
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type: thaisum |
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config: thaisum |
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split: validation |
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args: thaisum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2017 |
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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_thaisum_model |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the thaisum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3039 |
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- Rouge1: 0.2017 |
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- Rouge2: 0.0806 |
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- Rougel: 0.2016 |
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- Rougelsum: 0.2017 |
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- Gen Len: 18.9995 |
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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: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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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### 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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| 2.0742 | 1.0 | 5000 | 0.3272 | 0.1713 | 0.055 | 0.1703 | 0.1716 | 18.9945 | |
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| 1.7874 | 2.0 | 10000 | 0.3073 | 0.194 | 0.0742 | 0.1942 | 0.194 | 18.997 | |
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| 1.6341 | 3.0 | 15000 | 0.3035 | 0.2002 | 0.0804 | 0.1999 | 0.2002 | 19.0 | |
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| 1.4501 | 4.0 | 20000 | 0.3039 | 0.2017 | 0.0806 | 0.2016 | 0.2017 | 18.9995 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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