---
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- govreport-summarization
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-govreport-summarization
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: govreport-summarization
      type: govreport-summarization
      config: document
      split: train
      args: document
    metrics:
    - name: Rouge1
      type: rouge
      value: 5.4727
---

<!-- 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-small-finetuned-govreport-summarization

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co./google/mt5-small) on the govreport-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9193
- Rouge1: 5.4727
- Rouge2: 1.8064
- Rougel: 4.7904
- Rougelsum: 5.1785

## 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: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 8.1803        | 1.0   | 225  | 3.4063          | 4.8262 | 1.0677 | 4.1029 | 4.6438    |
| 4.1012        | 2.0   | 450  | 3.2004          | 4.888  | 1.2529 | 4.0737 | 4.6698    |
| 3.8386        | 3.0   | 675  | 3.1341          | 5.0027 | 1.1715 | 4.1397 | 4.7616    |
| 3.6986        | 4.0   | 900  | 3.0698          | 5.3287 | 1.6223 | 4.6697 | 5.0159    |
| 3.6007        | 5.0   | 1125 | 3.0346          | 5.5318 | 1.7741 | 4.8195 | 5.2351    |
| 3.5376        | 6.0   | 1350 | 3.0039          | 4.5345 | 1.3055 | 4.0118 | 4.3259    |
| 3.4794        | 7.0   | 1575 | 2.9845          | 4.755  | 1.5096 | 4.2156 | 4.5376    |
| 3.4373        | 8.0   | 1800 | 2.9699          | 4.6843 | 1.409  | 4.0942 | 4.4492    |
| 3.4007        | 9.0   | 2025 | 2.9569          | 5.5517 | 1.8103 | 4.8226 | 5.2639    |
| 3.3788        | 10.0  | 2250 | 2.9415          | 5.4873 | 1.8689 | 4.8027 | 5.2162    |
| 3.3549        | 11.0  | 2475 | 2.9429          | 5.3814 | 1.7672 | 4.7337 | 5.1079    |
| 3.3386        | 12.0  | 2700 | 2.9338          | 5.4238 | 1.7718 | 4.7339 | 5.1216    |
| 3.3195        | 13.0  | 2925 | 2.9224          | 5.4666 | 1.8941 | 4.79   | 5.1824    |
| 3.311         | 14.0  | 3150 | 2.9223          | 5.4197 | 1.7975 | 4.7752 | 5.1176    |
| 3.3027        | 15.0  | 3375 | 2.9202          | 5.494  | 1.8446 | 4.7876 | 5.1981    |
| 3.2961        | 16.0  | 3600 | 2.9193          | 5.4727 | 1.8064 | 4.7904 | 5.1785    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1