SenaSoft
/

chdv-summarization

@@ -1,199 +1,89 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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]
-#### 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]
-### Results
-[More Information Needed]
-#### Summary
-## 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]
-#### Hardware
-[More Information Needed]
-#### 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]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+base_model: LeoCordoba/beto2beto-mlsum
+library_name: peft
+tags:
+- summarization
+- spanish
+- lora
+- transformers
+- seq2seq
+- beto
 ---
+# SenaSoft/chdv-summarization
+Modelo de resumen automático de textos en español fine-tuneado a partir de **LeoCordoba/beto2beto-mlsum**, utilizando **LoRA** con la librería **PEFT**.
+## 🧠 Descripción del modelo
+Este modelo genera resúmenes en español a partir de textos largos, usando una arquitectura **Encoder-Decoder basada en BETO** (BERT español adaptado a tareas de resumen).
+Fue ajustado con un adaptador **LoRA (Low-Rank Adaptation)** para reducir el costo computacional y acelerar el entrenamiento sin sacrificar desempeño.
+- **Autores:** Christopher Aponte y David Navarro
+- **Lenguaje:** Español
+- **Tarea:** Resumen de texto (summarization)
+- **Modelo base:** [LeoCordoba/beto2beto-mlsum](https://huggingface.co/LeoCordoba/beto2beto-mlsum)
+- **Framework:** 🤗 Transformers + PEFT
+- **Dataset:** [csebuetnlp/xlsum (configuración: spanish)](https://huggingface.co/datasets/csebuetnlp/xlsum)
+## 🚀 Ejemplo de uso
+```python
+from transformers import pipeline
+# Carga del pipeline de resumen
+resumidor = pipeline("summarization", model="SenaSoft/chdv-summarization")
+texto = """
+El aprendizaje automático es una rama fundamental de la inteligencia artificial que se centra en el desarrollo de algoritmos y modelos capaces de aprender a partir de datos. En lugar de seguir instrucciones programadas de forma explícita, estas máquinas identifican patrones, relaciones y tendencias dentro de grandes volúmenes de información, lo que les permite mejorar su rendimiento y tomar decisiones cada vez más precisas con el tiempo.
+Esta disciplina se aplica en una amplia variedad de campos. En la visión por computadora, por ejemplo, permite que los sistemas reconozcan rostros, objetos y escenas dentro de imágenes o videos. En el procesamiento de lenguaje natural, posibilita que las máquinas comprendan y generen texto o voz de manera coherente, facilitando herramientas como traductores automáticos, chatbots o asistentes virtuales. También se utiliza en la predicción de comportamientos, donde modelos entrenados con datos históricos pueden anticipar compras de usuarios, detectar fraudes financieros o incluso prever fallas en sistemas industriales.
+El impacto del aprendizaje automático es tan amplio que se ha convertido en una de las tecnologías más influyentes del siglo XXI, impulsando la automatización, la personalización de servicios y el análisis inteligente de datos en prácticamente todos los sectores.
+"""
+# Generar resumen
+resumen = resumidor(texto, max_length=100, min_length=30, do_sample=False)
+print(resumen[0]["summary_text"])
+```
+Tiempo de respuesta aproximado: **1.1 segundos** en GPU T4.
+## ⚙️ Detalles de entrenamiento
+- **Modelo base:** LeoCordoba/beto2beto-mlsum
+- **Adaptación:** LoRA (`r=8`, `alpha=16`, `dropout=0.3`, módulos: query, key, value)
+- **Dataset:** 10 000 ejemplos del split `train` de XLSum (spanish)
+- **Épocas:** 3
+- **Batch size:** 16
+- **Learning rate:** 5e-5
+- **Framework:** Transformers + PEFT
+- **Entrenamiento:** Seq2SeqTrainer
+- **Duración total:** 6 h 34 min
+### 📊 Resultados del entrenamiento
+| Step | Training Loss | Validation Loss |
+|------|----------------|-----------------|
+| 100  | 5.77 | 1.17 |
+| 500  | 0.98 | 0.85 |
+| 1000 | 0.92 | 0.83 |
+| 1500 | 0.93 | 0.83 |
+| 1800 | 0.93 | 0.82 |
+> El modelo converge de manera estable, con un **Validation Loss final ≈ 0.82**, mostrando buena generalización y sin signos de sobreajuste.
+## 🧩 Limitaciones
+- Puede generar resúmenes demasiado breves en textos con múltiples párrafos.
+- No está diseñado para otros idiomas distintos al español.
+- No debe usarse para generar conclusiones analíticas o críticas.
+## 📘 Licencia
+El modelo base y este fine-tune se comparten bajo la misma licencia abierta de Hugging Face.
+---
+**Autores:** Christopher Aponte y David Navarro
+**Repositorio:** [SenaSoft/chdv-summarization](https://huggingface.co/SenaSoft/chdv-summarization)