---
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-qlora-finetune-tweetsumm-1724817707
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: Andyrasika/TweetSumm-tuned
      type: Andyrasika/TweetSumm-tuned
    metrics:
    - type: rouge
      value: 0.4708
      name: Rouge1
    - type: f1
      value: 0.8942
      name: F1
    - type: precision
      value: 0.8941
      name: Precision
    - type: recall
      value: 0.8945
      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-qlora-finetune-tweetsumm-1724817707

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: 1.7934
- Rouge1: 0.4708
- Rouge2: 0.2246
- Rougel: 0.3984
- Rougelsum: 0.4357
- Gen Len: 49.4091
- F1: 0.8942
- Precision: 0.8941
- Recall: 0.8945

## 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
- lr_scheduler_warmup_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.062         | 1.0   | 110  | 1.8472          | 0.4633 | 0.2177 | 0.3919 | 0.428     | 49.7273 | 0.8911 | 0.8897    | 0.8927 |
| 1.7853        | 2.0   | 220  | 1.8120          | 0.4633 | 0.2203 | 0.3941 | 0.4285    | 49.4273 | 0.8953 | 0.8945    | 0.8963 |
| 1.5952        | 3.0   | 330  | 1.7934          | 0.4708 | 0.2246 | 0.3984 | 0.4357    | 49.4091 | 0.8942 | 0.8941    | 0.8945 |


### Framework versions

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