Transformers
TensorBoard
Safetensors
mbart
text2text-generation
simplification
Generated from Trainer
Instructions to use JoseLuis95/mbart-neutralization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoseLuis95/mbart-neutralization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JoseLuis95/mbart-neutralization") model = AutoModelForSeq2SeqLM.from_pretrained("JoseLuis95/mbart-neutralization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,7 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 16 |
|
| 17 |
# mbart-neutralization
|
| 18 |
|
| 19 |
-
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on
|
|
|
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
- Loss: 0.0181
|
| 22 |
- Bleu: 98.7341
|
|
|
|
| 16 |
|
| 17 |
# mbart-neutralization
|
| 18 |
|
| 19 |
+
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on [Spanish Gender Neutralization
|
| 20 |
+
dataset](https://huggingface.co/datasets/hackathon-pln-es/neutral-es).
|
| 21 |
It achieves the following results on the evaluation set:
|
| 22 |
- Loss: 0.0181
|
| 23 |
- Bleu: 98.7341
|