---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
datasets:
- Andyrasika/TweetSumm-tuned
metrics:
- rouge
- f1
- precision
- recall
model-index:
- name: t5-base-Full-TweetSumm-1724683206
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4709
    - name: F1
      type: f1
      value: 0.8952
    - name: Precision
      type: precision
      value: 0.8934
    - name: Recall
      type: recall
      value: 0.8971
---

<!-- 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-Full-TweetSumm-1724683206

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: 1.8697
- Rouge1: 0.4709
- Rouge2: 0.2223
- Rougel: 0.3999
- Rougelsum: 0.4391
- Gen Len: 41.8455
- F1: 0.8952
- Precision: 0.8934
- Recall: 0.8971

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.2928        | 1.0   | 220  | 1.8094          | 0.466  | 0.2146 | 0.3912 | 0.4301    | 41.9182 | 0.891  | 0.8891    | 0.8931 |
| 1.2939        | 2.0   | 440  | 1.7929          | 0.4605 | 0.2125 | 0.3897 | 0.4259    | 42.0    | 0.8928 | 0.8914    | 0.8944 |
| 0.7227        | 3.0   | 660  | 1.8697          | 0.4709 | 0.2223 | 0.3999 | 0.4391    | 41.8455 | 0.8952 | 0.8934    | 0.8971 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1