---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: scientific_papers
      type: scientific_papers
      config: pubmed
      split: validation
      args: pubmed
    metrics:
    - name: Rouge1
      type: rouge
      value: 7.0145
---

<!-- 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-amazon-en-es

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5188
- Rouge1: 7.0145
- Rouge2: 1.8214
- Rougel: 5.8355
- Rougelsum: 6.2468

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 17.5935       | 1.0   | 50   | 8.3763          | 1.0909 | 0.7547 | 1.0909 | 1.0909    |
| 10.9464       | 2.0   | 100  | 4.7599          | 3.5415 | 0.0    | 2.927  | 2.547     |
| 6.5498        | 3.0   | 150  | 3.6772          | 6.7136 | 1.8214 | 5.2193 | 5.925     |
| 6.0161        | 4.0   | 200  | 3.5188          | 7.0145 | 1.8214 | 5.8355 | 6.2468    |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.1+cpu
- Datasets 2.17.0
- Tokenizers 0.15.2