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:

• cat
• ls
• cd

Usage

Below is an example of how to use es2bash-mt5 with the Hugging Face Transformers library:

from transformers import pipeline

translator = pipeline('translation', model='dev2bit/es2bash-mt5')

request = "listar los archivos en el directorio actual"
translated = translator(request, max_length=512)
print(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.

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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:

• cat
• ls
• cd

Uso

A continuación, se muestra un ejemplo de cómo usar es2bash-mt5 con la biblioteca Hugging Face Transformers:

from transformers import pipeline

translator = pipeline('translation', model='dev2bit/es2bash-mt5')

request = "listar los archivos en el directorio actual"
translated = translator(request, max_length=512)
print(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.

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This model is a fine-tuned version of google/mt5-small on the es2bash dataset. It achieves the following results on the evaluation set:

• Loss: 0.0919

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