---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned_samsum_summarization_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: validation
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 38.4852
---

<!-- 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_samsum_summarization_model

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0164
- Rouge1: 38.4852
- Rouge2: 16.4292
- Rougel: 32.9585
- Rougelsum: 36.0185

## 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: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 4.9849        | 1.0   | 1050 | 2.2071          | 34.8128 | 14.0544 | 29.8982 | 32.2776   |
| 2.7097        | 2.0   | 2100 | 2.1157          | 37.7348 | 15.9587 | 32.2724 | 35.2982   |
| 2.5305        | 3.0   | 3150 | 2.0553          | 38.4581 | 16.4518 | 32.7643 | 35.936    |
| 2.451         | 4.0   | 4200 | 2.0253          | 38.3972 | 16.3508 | 32.7684 | 35.9072   |
| 2.4132        | 5.0   | 5250 | 2.0164          | 38.4852 | 16.4292 | 32.9585 | 36.0185   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0