| --- |
| license: apache-2.0 |
| base_model: google/long-t5-tglobal-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: LongT5-Base-NSPCC |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # LongT5-Base-NSPCC |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1612 |
| - Rouge1: 0.4604 |
| - Rouge2: 0.1738 |
| - Rougel: 0.2641 |
| - Rougelsum: 0.2648 |
| - Gen Len: 242.1064 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0003 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 6 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:| |
| | 13.1772 | 1.0 | 12 | 2.8918 | 0.2996 | 0.0788 | 0.1529 | 0.1528 | 359.617 | |
| | 2.3541 | 2.0 | 24 | 1.2767 | 0.3935 | 0.1207 | 0.1972 | 0.1965 | 340.8298 | |
| | 1.5574 | 3.0 | 36 | 1.1901 | 0.4486 | 0.1662 | 0.2444 | 0.2444 | 278.3511 | |
| | 1.439 | 4.0 | 48 | 1.1712 | 0.46 | 0.1746 | 0.2628 | 0.2636 | 254.266 | |
| | 1.4027 | 5.0 | 60 | 1.1603 | 0.4625 | 0.174 | 0.2619 | 0.2622 | 246.0851 | |
| | 1.3858 | 6.0 | 72 | 1.1612 | 0.4604 | 0.1738 | 0.2641 | 0.2648 | 242.1064 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.39.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
