Instructions to use Ro551/mbart-large-50-GEC-spanish-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ro551/mbart-large-50-GEC-spanish-merged with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ro551/mbart-large-50-GEC-spanish-merged") model = AutoModelForSeq2SeqLM.from_pretrained("Ro551/mbart-large-50-GEC-spanish-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
base_model: facebook/mbart-large-50
tags:
- generated_from_trainer
model-index:
- name: mbart-large-50-GEC-spanish-merged
results: []
mbart-large-50-GEC-spanish-merged
This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0397
- Gleu: 0.8549
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: 9.175332375538101e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.05586177114362126
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Gleu |
|---|---|---|---|---|
| 0.4394 | 1.0 | 2804 | 0.0457 | 0.8332 |
| 0.1933 | 2.0 | 5608 | 0.0397 | 0.8549 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2