---
base_model: google-t5/t5-base
datasets:
- Andyrasika/TweetSumm-tuned
library_name: peft
license: apache-2.0
metrics:
- rouge
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: t5-base-LoRA-TweetSumm-1724689228
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4651
      name: Rouge1
    - type: f1
      value: 0.8924
      name: F1
    - type: precision
      value: 0.8906
      name: Precision
    - type: recall
      value: 0.8943
      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-TweetSumm-1724689228

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: 1.7954
- Rouge1: 0.4651
- Rouge2: 0.218
- Rougel: 0.3904
- Rougelsum: 0.4291
- Gen Len: 41.8818
- F1: 0.8924
- Precision: 0.8906
- Recall: 0.8943

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.3566        | 1.0   | 440  | 1.8523          | 0.4801 | 0.2302 | 0.4078 | 0.4472    | 41.6727 | 0.8942 | 0.8938    | 0.8947 |
| 1.2968        | 2.0   | 880  | 1.7823          | 0.447  | 0.2102 | 0.3795 | 0.4136    | 41.9091 | 0.8929 | 0.8925    | 0.8935 |
| 1.7438        | 3.0   | 1320 | 1.7954          | 0.4651 | 0.218  | 0.3904 | 0.4291    | 41.8818 | 0.8924 | 0.8906    | 0.8943 |


### Framework versions

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