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--- |
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license: apache-2.0 |
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base_model: google/long-t5-tglobal-large |
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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-Large-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-Large-NSPCC |
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This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3710 |
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- Rouge1: 0.4978 |
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- Rouge2: 0.2091 |
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- Rougel: 0.2874 |
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- Rougelsum: 0.2871 |
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- Gen Len: 251.3511 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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: 8 |
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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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| 6.0401 | 0.9960 | 188 | 2.7089 | 0.2766 | 0.0617 | 0.1655 | 0.1657 | 151.7021 | |
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| 2.4805 | 1.9974 | 377 | 1.8809 | 0.382 | 0.1178 | 0.2092 | 0.2092 | 211.1809 | |
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| 1.8093 | 2.9987 | 566 | 1.5769 | 0.4356 | 0.1527 | 0.2409 | 0.2409 | 246.1277 | |
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| 1.4653 | 4.0 | 755 | 1.4359 | 0.4661 | 0.1722 | 0.26 | 0.2603 | 245.0851 | |
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| 1.2626 | 4.9960 | 943 | 1.3908 | 0.4829 | 0.1931 | 0.2717 | 0.2717 | 239.8617 | |
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| 1.117 | 5.9974 | 1132 | 1.3724 | 0.4864 | 0.1988 | 0.2804 | 0.2804 | 244.4255 | |
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| 1.0404 | 6.9987 | 1321 | 1.3714 | 0.4914 | 0.2007 | 0.2826 | 0.2821 | 248.6915 | |
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| 1.0065 | 7.9682 | 1504 | 1.3710 | 0.4978 | 0.2091 | 0.2874 | 0.2871 | 251.3511 | |
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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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