Update README.md

README.md

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 license: gpl
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-#Nombre del Modelo: [Nombre del Modelo]
+
+# Nombre del modelo: BAAO
 
 Una breve descripción del modelo y su propósito principal.
 
-#Descripción
+# Descripciòn
+
+Este modelo utiliza la arquitectura T5 de Transformers para tareas de procesamiento de lenguaje natural, 
+centradas en la generación de texto. Se implemento y entreno usando PyTorch Lightning, 
+lo que facilita la escalabilidad y eficiencia en el entrenamiento.
 
-Este modelo utiliza la arquitectura T5 de Transformers para tareas de procesamiento de lenguaje natural, posiblemente centradas en la generación de texto o traducción. Está implementado y entrenado usando PyTorch Lightning, lo que facilita la escalabilidad y eficiencia en el entrenamiento.
 
-#Características del Modelo
+# Caracterìsticas del modelo
 
 Tipo de Modelo: T5 para generación condicional de texto.
 
@@ -18,219 +22,14 @@ Entrenado con: PyTorch Lightning.
 
 Bibliotecas Clave: Transformers, SentencePiece, PyTorch Lightning.
 
-#Datos Utilizados
+# Datos utilizados
 
 Preparación de Datos: Uso de T5Dataset para la manipulación y preparación de datos, adaptado para trabajar con el modelo T5.
-División de Datos: Dividido en conjuntos de entrenamiento y prueba para una evaluación efectiva.
-Arquitectura del Modelo
-Base: T5ForConditionalGeneration de la biblioteca Transformers.
-Personalización: Implementación dentro de una clase T5Model que extiende pl.LightningModule, lo que indica una integración con PyTorch Lightning para gestionar el entrenamiento y la validación.
-Uso del Modelo
-Instrucciones específicas no disponibles en el notebook proporcionado. Se recomienda proporcionar ejemplos de cómo cargar y utilizar el modelo para hacer predicciones.
-Requisitos y Dependencias
-Bibliotecas Requeridas: transformers, sentencepiece, pytorch_lightning.
-Notas Adicionales
-Información sobre métricas de evaluación y resultados del modelo no disponible en el notebook proporcionado. Se sugiere realizar pruebas adicionales para determinar el rendimiento del modelo.
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+División de Datos: Dividido en conjuntos de entrenamiento y prueba para una evaluación efectiva.
 
+# Arquitectura del modelo
 
+Base: T5ForConditionalGeneration de la biblioteca Transformers.
+Personalización: Implementación dentro de una clase T5Model que extiende pl.LightningModule, 
+lo que indica una integración con PyTorch Lightning para gestionar el entrenamiento y la validación.