---
base_model: google-t5/t5-small
datasets:
- Andyrasika/TweetSumm-tuned
library_name: peft
license: apache-2.0
metrics:
- rouge
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: t5-small-QLoRA-TweetSumm-1724713795
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4298
      name: Rouge1
    - type: f1
      value: 0.887
      name: F1
    - type: precision
      value: 0.8838
      name: Precision
    - type: recall
      value: 0.8904
      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-small-QLoRA-TweetSumm-1724713795

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the Andyrasika/TweetSumm-tuned dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0940
- Rouge1: 0.4298
- Rouge2: 0.1915
- Rougel: 0.3559
- Rougelsum: 0.3956
- Gen Len: 47.8091
- F1: 0.887
- Precision: 0.8838
- Recall: 0.8904

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.3641        | 1.0   | 110  | 2.2019          | 0.4172 | 0.1774 | 0.3518 | 0.386     | 47.7636 | 0.8828 | 0.8806    | 0.8852 |
| 2.2228        | 2.0   | 220  | 2.1040          | 0.419  | 0.1789 | 0.3477 | 0.3827    | 48.1182 | 0.8846 | 0.882     | 0.8875 |
| 2.0174        | 3.0   | 330  | 2.0940          | 0.4298 | 0.1915 | 0.3559 | 0.3956    | 47.8091 | 0.887  | 0.8838    | 0.8904 |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1