transformers tensorboard safetensors mt5 text2text-generation summarization generated_from_trainer en es dataset:csebuetnlp/xlsum base_model:google/mt5-small base_model:finetune:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-small
datasets:
- csebuetnlp/xlsum
language:
- en
- es
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
widget:
- text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey
    building, and the tallest structure in Paris. Its base is square, measuring 125
    metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed
    the Washington Monument to become the tallest man-made structure in the world,
    a title it held for 41 years until the Chrysler Building in New York City was
    finished in 1930. It was the first structure to reach a height of 300 metres.
    Due to the addition of a broadcasting aerial at the top of the tower in 1957,
    it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters,
    the Eiffel Tower is the second tallest free-standing structure in France after
    the Millau Viaduct.
  example_title: English Summary
- text: La torre tiene 324 metros (1.063 pies) de altura, aproximadamente la misma
    altura que un edificio de 81 pisos, y la estructura más alta de París. Su base
    es cuadrada, mide 125 metros (410 pies) de lado. Durante su construcción, la Torre
    Eiffel superó al Monumento a Washington para convertirse en la estructura artificial
    más alta del mundo, un título que mantuvo durante 41 años hasta que se terminó
    el Edificio Chrysler en la ciudad de Nueva York en 1930. Fue la primera estructura
    en alcanzar una altura de 300 metros. Debido a la adición de una antena de transmisión
    en la parte superior de la torre en 1957, ahora es más alta que el Edificio Chrysler
    en 5,2 metros (17 pies). Excluyendo los transmisores, la Torre Eiffel es la segunda
    estructura independiente más alta de Francia después del viaducto de Millau.
  example_title: Spanish Summary
model-index:
- name: mt5-small-finetuned-xlsum-en-es
  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-small-finetuned-xlsum-en-es

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the csebuetnlp/xlsum dataset.

Reduced versions of the English/Spanish subsets were used, focusing on shorter targets.

It achieves the following results on the evaluation set:
- Loss: 2.9483
- Rouge1: 19.42
- Rouge2: 4.44
- Rougel: 16.7
- Rougelsum: 16.7
- Mean Len: 16.3231

## Model description

More information needed

## Intended uses & limitations

Model may produce false information when summarizing.

This is very much an initial draft, and is not expected for use in production, use at your own risk.

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

Lead-3 Baseline:
- Rouge1: 12.22
- Rouge2: 2.01
- RougeL: 9.02
- RougeLsum: 10.33

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Mean Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 6.7763        | 1.0   | 1237 | 3.1120          | 13.57  | 2.76   | 11.59  | 11.59     | 12.6116  |
| 4.1022        | 2.0   | 2474 | 2.9718          | 19.35  | 4.32   | 16.63  | 16.64     | 16.3084  |
| 3.9219        | 3.0   | 3711 | 2.9483          | 19.42  | 4.44   | 16.7   | 16.7      | 16.3231  |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@inproceedings{hasan-etal-2021-xl,
    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Islam, Md. Saiful  and
      Mubasshir, Kazi  and
      Li, Yuan-Fang  and
      Kang, Yong-Bin  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.413",
    pages = "4693--4703",
}
```