---
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-LoRA-TweetSumm-1724701402
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4387
      name: Rouge1
    - type: f1
      value: 0.8896
      name: F1
    - type: precision
      value: 0.8881
      name: Precision
    - type: recall
      value: 0.8913
      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-LoRA-TweetSumm-1724701402

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: 2.0811
- Rouge1: 0.4387
- Rouge2: 0.196
- Rougel: 0.3605
- Rougelsum: 0.4055
- Gen Len: 49.5909
- F1: 0.8896
- Precision: 0.8881
- Recall: 0.8913

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.3972        | 1.0   | 110  | 2.1384          | 0.4219 | 0.1801 | 0.3545 | 0.3925    | 49.9818 | 0.8833 | 0.8806    | 0.8861 |
| 2.2593        | 2.0   | 220  | 2.0982          | 0.4125 | 0.1843 | 0.3448 | 0.3837    | 49.9091 | 0.8853 | 0.8822    | 0.8886 |
| 1.9318        | 3.0   | 330  | 2.0811          | 0.4387 | 0.196  | 0.3605 | 0.4055    | 49.5909 | 0.8896 | 0.8881    | 0.8913 |


### Framework versions

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