MTSUFall2024SoftwareEngineering
This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7579
- Rouge1: 0.268
- Rouge2: 0.2083
- Rougel: 0.258
- Rougelsum: 0.2582
- Gen Len: 18.9805
Model description
This model is a fine-tuned Google T5-Small model that is fine-tuned to summarize United States Senate and House Bills.
Intended uses & limitations
Summarize United States Federal Legislation.
Training and evaluation data
Trained on ~51.9k bills and summaries.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.1182 | 1.0 | 3708 | 1.8807 | 0.2643 | 0.2029 | 0.2533 | 0.2534 | 18.9817 |
| 1.999 | 2.0 | 7416 | 1.8013 | 0.2663 | 0.2053 | 0.2558 | 0.2559 | 18.9833 |
| 1.9739 | 3.0 | 11124 | 1.7681 | 0.267 | 0.2066 | 0.2568 | 0.2569 | 18.9816 |
| 1.9448 | 4.0 | 14832 | 1.7579 | 0.268 | 0.2083 | 0.258 | 0.2582 | 18.9805 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Model tree for cheaptrix/MTSUFall2024SoftwareEngineering
Base model
google-t5/t5-small
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cheaptrix/MTSUFall2024SoftwareEngineering") model = AutoModelForSeq2SeqLM.from_pretrained("cheaptrix/MTSUFall2024SoftwareEngineering")