---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_law_model
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/klenamdroefenu-kwame-nkrumah-university-of-science-and-t/huggingface/runs/r1syibf4)
# my_awesome_law_model

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2170
- Rouge1: 0.1575
- Rouge2: 0.0733
- Rougel: 0.135
- Rougelsum: 0.135
- Gen Len: 19.0

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 389  | 2.2997          | 0.1558 | 0.0713 | 0.1334 | 0.1333    | 19.0    |
| 2.6025        | 2.0   | 778  | 2.2468          | 0.1571 | 0.0729 | 0.1344 | 0.1343    | 19.0    |
| 2.3823        | 3.0   | 1167 | 2.2222          | 0.157  | 0.0731 | 0.1346 | 0.1345    | 19.0    |
| 2.3471        | 4.0   | 1556 | 2.2170          | 0.1575 | 0.0733 | 0.135  | 0.135     | 19.0    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1