---
license: apache-2.0
base_model: lunarlist/mt5-summarize
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-summarize-full
  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. -->

# mt5-summarize-full

This model is a fine-tuned version of [lunarlist/mt5-summarize](https://huggingface.co/lunarlist/mt5-summarize) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8640
- Rouge1: 0.3352
- Rouge2: 0.1212
- Rougel: 0.2748
- Rougelsum: 0.4747

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.0732        | 1.0667 | 100  | 3.1187          | 0.3331 | 0.1146 | 0.2648 | 0.5137    |
| 3.6546        | 2.1333 | 200  | 2.9872          | 0.3410 | 0.1256 | 0.2894 | 0.4943    |
| 3.3308        | 3.2    | 300  | 2.9373          | 0.3430 | 0.1278 | 0.2881 | 0.4743    |
| 3.276         | 4.2667 | 400  | 2.8782          | 0.3355 | 0.1163 | 0.2793 | 0.4801    |
| 3.1345        | 5.3333 | 500  | 2.9083          | 0.3354 | 0.1216 | 0.2835 | 0.4758    |
| 3.0736        | 6.4    | 600  | 2.8588          | 0.3531 | 0.1353 | 0.2900 | 0.4991    |
| 3.0168        | 7.4667 | 700  | 2.8592          | 0.3436 | 0.1229 | 0.2893 | 0.4863    |
| 2.969         | 8.5333 | 800  | 2.8739          | 0.3528 | 0.1297 | 0.2863 | 0.4968    |
| 2.9677        | 9.6    | 900  | 2.8640          | 0.3352 | 0.1212 | 0.2748 | 0.4747    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1