---
tags:
- generated_from_trainer
model-index:
- name: results_mt5_xl-sum
  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. -->

# results_mt5_xl-sum

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8431
- Rouge1 Fmeasure: 0.6139
- Rouge2 Fmeasure: 0.1189
- Rougel Fmeasure: 0.1997
- Meteor: 0.3315
- Bertscore F1: 0.8418

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 Fmeasure | Rouge2 Fmeasure | Rougel Fmeasure | Meteor | Bertscore F1 |
|:-------------:|:------:|:----:|:---------------:|:---------------:|:---------------:|:---------------:|:------:|:------------:|
| 2.6516        | 0.8529 | 500  | 0.9710          | 0.2668          | 0.0484          | 0.1537          | 0.2745 | 0.8284       |
| 1.0475        | 1.7058 | 1000 | 0.8792          | 0.4289          | 0.0884          | 0.1737          | 0.2949 | 0.8278       |
| 0.9413        | 2.5586 | 1500 | 0.8457          | 0.4960          | 0.0865          | 0.1898          | 0.3141 | 0.8339       |
| 0.8711        | 3.4115 | 2000 | 0.8398          | 0.5400          | 0.1121          | 0.1941          | 0.3110 | 0.8397       |
| 0.8235        | 4.2644 | 2500 | 0.8345          | 0.5587          | 0.1022          | 0.2041          | 0.3160 | 0.8388       |
| 0.7797        | 5.1173 | 3000 | 0.8368          | 0.5735          | 0.1036          | 0.2044          | 0.3157 | 0.8344       |
| 0.7401        | 5.9701 | 3500 | 0.8217          | 0.5507          | 0.1133          | 0.1936          | 0.3186 | 0.8366       |
| 0.7022        | 6.8230 | 4000 | 0.8361          | 0.5808          | 0.1118          | 0.2008          | 0.3227 | 0.8406       |
| 0.6796        | 7.6759 | 4500 | 0.8344          | 0.6173          | 0.1277          | 0.1986          | 0.3260 | 0.8407       |
| 0.6523        | 8.5288 | 5000 | 0.8436          | 0.6232          | 0.1186          | 0.2024          | 0.3317 | 0.8398       |
| 0.6385        | 9.3817 | 5500 | 0.8431          | 0.6139          | 0.1189          | 0.1997          | 0.3315 | 0.8418       |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1