---
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
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# 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