Transformers
Safetensors
t5
text2text-generation
simplification
Generated from Trainer
text-generation-inference
Instructions to use sanar085/flant5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanar085/flant5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sanar085/flant5") model = AutoModelForSeq2SeqLM.from_pretrained("sanar085/flant5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -24,15 +24,15 @@ It achieves the following results on the evaluation set:
|
|
| 24 |
|
| 25 |
## Model description
|
| 26 |
|
| 27 |
-
|
| 28 |
|
| 29 |
## Intended uses & limitations
|
| 30 |
|
| 31 |
-
|
| 32 |
|
| 33 |
## Training and evaluation data
|
| 34 |
|
| 35 |
-
|
| 36 |
|
| 37 |
## Training procedure
|
| 38 |
|
|
|
|
| 24 |
|
| 25 |
## Model description
|
| 26 |
|
| 27 |
+
El modelo FLAN T5 Base es una variante del modelo de lenguaje T5 (Text-To-Text Transfer Transformer) desarrollado por Google. T5 es una arquitectura de modelo de lenguaje basada en transformers que ha demostrado un rendimiento sobresaliente en una amplia gama de tareas de procesamiento de lenguaje natural (NLP).
|
| 28 |
|
| 29 |
## Intended uses & limitations
|
| 30 |
|
| 31 |
+
En este caso el modelo es un traductor automático que simplifica el francés.
|
| 32 |
|
| 33 |
## Training and evaluation data
|
| 34 |
|
| 35 |
+
Se utilizan 266 filas de la tabla como entrenamiento y 67 como test.
|
| 36 |
|
| 37 |
## Training procedure
|
| 38 |
|