---
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-ia3-finetune-tweetsumm-1724827331
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4407
      name: Rouge1
    - type: f1
      value: 0.8906
      name: F1
    - type: precision
      value: 0.8894
      name: Precision
    - type: recall
      value: 0.8921
      name: Recall
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-base-ia3-finetune-tweetsumm-1724827331

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: 1.8276
- Rouge1: 0.4407
- Rouge2: 0.1997
- Rougel: 0.3672
- Rougelsum: 0.4075
- Gen Len: 49.5727
- F1: 0.8906
- Precision: 0.8894
- Recall: 0.8921

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.2511        | 1.0   | 879  | 1.9364          | 0.4398 | 0.1855 | 0.3668 | 0.411     | 49.5182 | 0.8883 | 0.8875    | 0.8892 |
| 1.4557        | 2.0   | 1758 | 1.8611          | 0.4491 | 0.2031 | 0.3721 | 0.4148    | 49.6091 | 0.8901 | 0.8889    | 0.8915 |
| 1.8149        | 3.0   | 2637 | 1.8386          | 0.4436 | 0.2001 | 0.3707 | 0.4092    | 49.5636 | 0.8905 | 0.889     | 0.8923 |
| 2.7192        | 4.0   | 3516 | 1.8271          | 0.4366 | 0.1966 | 0.3643 | 0.4041    | 49.6091 | 0.8897 | 0.8878    | 0.8917 |
| 1.7838        | 5.0   | 4395 | 1.8276          | 0.4407 | 0.1997 | 0.3672 | 0.4075    | 49.5727 | 0.8906 | 0.8894    | 0.8921 |


### Framework versions

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