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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: long-t5-tglobal-base |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/theubaada/huggingface/runs/2p17lh0w) |
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# long-t5-tglobal-base |
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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: 1.9401 |
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- Rouge1: 0.1934 |
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- Rouge2: 0.0269 |
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- Rougel: 0.1151 |
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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: 4e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- total_eval_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: linear |
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- num_epochs: 13 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:| |
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| 1.5731 | 0.9996 | 600 | 1.9730 | 0.1342 | 0.0151 | 0.0912 | |
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| 1.3694 | 1.9996 | 1200 | 1.9623 | 0.1371 | 0.0175 | 0.0909 | |
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| 1.9561 | 2.9992 | 1800 | 1.9565 | 0.1423 | 0.0178 | 0.0928 | |
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| 1.0882 | 3.9996 | 2400 | 1.9548 | 0.1417 | 0.0186 | 0.0900 | |
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| 1.4872 | 4.9992 | 3000 | 1.9412 | 0.1581 | 0.0212 | 0.1006 | |
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| 1.4126 | 5.9988 | 3600 | 1.9486 | 0.1589 | 0.0188 | 0.0986 | |
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| 1.1634 | 7.0 | 4201 | 1.9464 | 0.1756 | 0.0229 | 0.1046 | |
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| 0.9541 | 7.9996 | 4801 | 1.9401 | 0.1791 | 0.0243 | 0.1078 | |
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| 0.9153 | 8.9975 | 5400 | 1.9401 | 0.1934 | 0.0269 | 0.1151 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.2.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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