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--- |
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license: apache-2.0 |
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base_model: google/long-t5-tglobal-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: LongT5-Base-NSPCC |
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results: [] |
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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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# LongT5-Base-NSPCC |
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This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1612 |
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- Rouge1: 0.4604 |
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- Rouge2: 0.1738 |
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- Rougel: 0.2641 |
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- Rougelsum: 0.2648 |
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- Gen Len: 242.1064 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 6 |
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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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| 13.1772 | 1.0 | 12 | 2.8918 | 0.2996 | 0.0788 | 0.1529 | 0.1528 | 359.617 | |
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| 2.3541 | 2.0 | 24 | 1.2767 | 0.3935 | 0.1207 | 0.1972 | 0.1965 | 340.8298 | |
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| 1.5574 | 3.0 | 36 | 1.1901 | 0.4486 | 0.1662 | 0.2444 | 0.2444 | 278.3511 | |
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| 1.439 | 4.0 | 48 | 1.1712 | 0.46 | 0.1746 | 0.2628 | 0.2636 | 254.266 | |
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| 1.4027 | 5.0 | 60 | 1.1603 | 0.4625 | 0.174 | 0.2619 | 0.2622 | 246.0851 | |
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| 1.3858 | 6.0 | 72 | 1.1612 | 0.4604 | 0.1738 | 0.2641 | 0.2648 | 242.1064 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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