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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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- NLP |
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- text-to-text |
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- Summarization |
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metrics: |
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- rouge |
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model-index: |
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- name: MTSUFall2024SoftwareEngineering |
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results: [] |
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datasets: |
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- MTSUFall2024SoftwareEngineering/UnitedStatesSenateBillsAndSummaries |
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language: |
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- en |
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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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# MTSUFall2024SoftwareEngineering |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9830 |
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- Rouge1: 0.2539 |
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- Rouge2: 0.201 |
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- Rougel: 0.2469 |
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- Rougelsum: 0.2469 |
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- Gen Len: 18.9996 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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Used for Middle Tennessee State University's Software Engineering Fall 2024 class. |
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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: 2e-05 |
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- train_batch_size: 14 |
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- eval_batch_size: 14 |
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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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- mixed_precision_training: Native AMP |
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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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| 2.7481 | 1.0 | 749 | 2.1062 | 0.2552 | 0.1993 | 0.2475 | 0.2475 | 18.9996 | |
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| 2.3354 | 2.0 | 1498 | 2.0224 | 0.2531 | 0.2 | 0.246 | 0.246 | 18.9996 | |
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| 2.2351 | 3.0 | 2247 | 1.9929 | 0.2542 | 0.2011 | 0.2471 | 0.247 | 18.9996 | |
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| 2.193 | 4.0 | 2996 | 1.9830 | 0.2539 | 0.201 | 0.2469 | 0.2469 | 18.9996 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
