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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 |