safetensors t5 generated_from_trainer dataset:Andyrasika/TweetSumm-tuned base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 model-index region:us
---
base_model: google-t5/t5-small
datasets:
- Andyrasika/TweetSumm-tuned
license: apache-2.0
metrics:
- rouge
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: t5-small-Full-TweetSumm-1724699443
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4576
      name: Rouge1
    - type: f1
      value: 0.8917
      name: F1
    - type: precision
      value: 0.8901
      name: Precision
    - type: recall
      value: 0.8936
      name: Recall
---

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# t5-small-Full-TweetSumm-1724699443

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: 1.9954
- Rouge1: 0.4576
- Rouge2: 0.2129
- Rougel: 0.3814
- Rougelsum: 0.4246
- Gen Len: 49.4636
- F1: 0.8917
- Precision: 0.8901
- Recall: 0.8936

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.3321        | 1.0   | 110  | 2.0722          | 0.462  | 0.2119 | 0.3832 | 0.429     | 49.4818 | 0.8916 | 0.8905    | 0.893  |
| 2.0488        | 2.0   | 220  | 2.0052          | 0.453  | 0.2025 | 0.3721 | 0.4167    | 49.5727 | 0.8912 | 0.8889    | 0.8938 |
| 1.7205        | 3.0   | 330  | 1.9954          | 0.4576 | 0.2129 | 0.3814 | 0.4246    | 49.4636 | 0.8917 | 0.8901    | 0.8936 |


### Framework versions

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