es2bash-mt5: Spanish to Bash Model
Developed by dev2bit, es2bash-mt5 is a language transformer model that is capable of predicting the optimal Bash command given a natural language request in Spanish. This model represents a major advancement in human-computer interaction, providing a natural language interface for Unix operating system commands.
About the Model
es2bash-mt5 is a fine-tuning model based on mt5-small. It has been trained on the dev2bit/es2bash dataset, which specializes in translating natural language in Spanish into Bash commands.
This model is optimized for processing requests related to the commands:
Usage
Below is an example of how to use es2bash-mt5 with the Hugging Face Transformers library:
1from transformers import pipeline
2
3translator = pipeline('translation', model='dev2bit/es2bash-mt5')
4
5request = "listar los archivos en el directorio actual"
6translated = translator(request, max_length=512)
7print(translated[0]['translation_text'])
This will print the Bash command corresponding to the given Spanish request.
Contributions
We appreciate your contributions! You can help improve es2bash-mt5 in various ways, including:
- Testing the model and reporting any issues or suggestions in the Issues section.
- Improving the documentation.
- Providing usage examples.
es2bash-mt5: Modelo de español a Bash
Desarrollado por dev2bit, es2bash-mt5 es un modelo transformador de lenguaje que tiene la capacidad de predecir el comando Bash óptimo dada una solicitud en lenguaje natural en español. Este modelo representa un gran avance en la interacción humano-computadora, proporcionando una interfaz de lenguaje natural para los comandos del sistema operativo Unix.
Sobre el modelo
es2bash-mt5 es un modelo de ajuste fino basado en mt5-small. Ha sido entrenado en el conjunto de datos dev2bit/es2bash, especializado en la traducción de lenguaje natural en español a comandos Bash.
Este modelo está optimizado para procesar solicitudes relacionadas con los comandos:
Uso
A continuación, se muestra un ejemplo de cómo usar es2bash-mt5 con la biblioteca Hugging Face Transformers:
1from transformers import pipeline
2
3translator = pipeline('translation', model='dev2bit/es2bash-mt5')
4
5request = "listar los archivos en el directorio actual"
6translated = translator(request, max_length=512)
7print(translated[0]['translation_text'])
Esto imprimirá el comando Bash correspondiente a la solicitud dada en español.
Contribuciones
Agradecemos sus contribuciones! Puede ayudar a mejorar es2bash-mt5 de varias formas, incluyendo:
- Probar el modelo y reportar cualquier problema o sugerencia en la sección de Issues.
- Mejorando la documentación.
- Proporcionando ejemplos de uso.
This model is a fine-tuned version of
google/mt5-small on the es2bash dataset.
It achieves the following results on the evaluation set:
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 28
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|
| 21.394 | 1.0 | 672 | 1.7470 |
| 2.5294 | 2.0 | 1344 | 0.6350 |
| 0.5873 | 3.0 | 2016 | 0.2996 |
| 0.3802 | 4.0 | 2688 | 0.2142 |
| 0.2951 | 5.0 | 3360 | 0.1806 |
| 0.225 | 6.0 | 4032 | 0.1565 |
| 0.2065 | 7.0 | 4704 | 0.1461 |
| 0.1944 | 8.0 | 5376 | 0.1343 |
| 0.174 | 9.0 | 6048 | 0.1281 |
| 0.1647 | 10.0 | 6720 | 0.1207 |
| 0.1566 | 11.0 | 7392 | 0.1140 |
| 0.1498 | 12.0 | 8064 | 0.1106 |
| 0.1382 | 13.0 | 8736 | 0.1076 |
| 0.1393 | 14.0 | 9408 | 0.1042 |
| 0.1351 | 15.0 | 10080 | 0.1019 |
| 0.13 | 16.0 | 10752 | 0.0998 |
| 0.1292 | 17.0 | 11424 | 0.0983 |
| 0.1265 | 18.0 | 12096 | 0.0973 |
| 0.1255 | 19.0 | 12768 | 0.0969 |
| 0.1216 | 20.0 | 13440 | 0.0956 |
| 0.1216 | 21.0 | 14112 | 0.0946 |
| 0.123 | 22.0 | 14784 | 0.0938 |
| 0.113 | 23.0 | 15456 | 0.0931 |
| 0.1185 | 24.0 | 16128 | 0.0929 |
| 0.1125 | 25.0 | 16800 | 0.0927 |
| 0.1213 | 26.0 | 17472 | 0.0925 |
| 0.1153 | 27.0 | 18144 | 0.0921 |
| 0.1134 | 28.0 | 18816 | 0.0919 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
