transformers tensorboard safetensors mbart text2text-generation generated_from_trainer en es dataset:Helsinki-NLP/opus-100 base_model:facebook/mbart-large-50 base_model:finetune:facebook/mbart-large-50 license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/mbart-large-50
datasets:
- Helsinki-NLP/opus-100
language:
- en
- es
license: mit
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: mbart-large-50-finetuned-es-en-2
  results: []
---

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# mbart-large-50-finetuned-es-en-2

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the [Helsinki-NLP/opus-100](https://huggingface.co/datasets/Helsinki-NLP/opus-100).

## Model description

mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper.

mBART-50 is a multilingual Sequence-to-Sequence model. It was created to show that multilingual translation models can be created through multilingual fine-tuning. Instead of fine-tuning on one direction, a pre-trained model is fine-tuned many directions simultaneously. mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages. The pre-training objective is explained below. Multilingual Denoising Pretraining: The model incorporates N languages by concatenating data: D = {D1, ..., DN } where each Di is a collection of monolingual documents in language i. The source documents are noised using two schemes, first randomly shuffling the original sentences' order, and second a novel in-filling scheme, where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text. 35% of each instance's words are masked by random sampling a span length according to a Poisson distribution (λ = 3.5). The decoder input is the original text with one position offset. A language id symbol LID is used as the initial token to predict the sentence.

## Intended uses & limitations

More information needed

## Training and evaluation data

10,000 random samples from the dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

### BLEU SCORE: 25.6431

### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2