| --- |
| library_name: peft |
| license: apache-2.0 |
| base_model: google-t5/t5-base |
| tags: |
| - base_model:adapter:google-t5/t5-base |
| - lora |
| - transformers |
| datasets: |
| - Andyrasika/TweetSumm-tuned |
| metrics: |
| - rouge |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: t5-base-lora-finetune-tweetsumm-1760075742 |
| results: |
| - task: |
| type: summarization |
| name: Summarization |
| dataset: |
| name: Andyrasika/TweetSumm-tuned |
| type: Andyrasika/TweetSumm-tuned |
| metrics: |
| - type: rouge |
| value: 0.4407 |
| name: Rouge1 |
| - type: f1 |
| value: 0.8888 |
| name: F1 |
| - type: precision |
| value: 0.8854 |
| name: Precision |
| - type: recall |
| value: 0.8924 |
| name: Recall |
| --- |
| |
| <!-- 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. --> |
|
|
| # t5-base-lora-finetune-tweetsumm-1760075742 |
|
|
| This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the Andyrasika/TweetSumm-tuned dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.8115 |
| - Rouge1: 0.4407 |
| - Rouge2: 0.2025 |
| - Rougel: 0.368 |
| - Rougelsum: 0.4049 |
| - Gen Len: 47.8545 |
| - F1: 0.8888 |
| - Precision: 0.8854 |
| - Recall: 0.8924 |
|
|
| ## 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: 0.001 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:| |
| | 2.1023 | 1.0 | 110 | 1.8780 | 0.424 | 0.1972 | 0.3628 | 0.3926 | 48.7 | 0.8843 | 0.8815 | 0.8873 | |
| | 1.7351 | 2.0 | 220 | 1.8109 | 0.4494 | 0.2109 | 0.3799 | 0.4135 | 49.1909 | 0.8907 | 0.8879 | 0.8937 | |
| | 1.5234 | 3.0 | 330 | 1.8115 | 0.4407 | 0.2025 | 0.368 | 0.4049 | 47.8545 | 0.8888 | 0.8854 | 0.8924 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.17.1 |
| - Transformers 4.56.2 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
