commit c39dc4195c88d84f9d850e9d0602bb7bd34407b9
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Mon Apr 13 11:31:29 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: lmolino/extractor_abreviaciones
    Source: Original Platform

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diff --git a/README.md b/README.md
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+---
+language: es
+license: apache-2.0
+tags:
+- medical
+- clinical-nlp
+- abbreviation-detection
+- spanish
+- cardiology
+- llama
+metrics:
+- precision
+- recall
+- f1
+---
+
+# LLaMA 3.2-1B Medical Abbreviation Detector (Spanish)
+
+## Descripción del Modelo
+
+Este modelo es una versión especializada de **LLaMA 3.2-1B Instruct** ajustada mediante fine-tuning para la **detección automática de abreviaciones médicas en textos clínicos en español**. Forma parte del sistema SimpliMed desarrollado para la simplificación de informes de alta hospitalaria en el ámbito de la cardiología.
+
+El modelo ha sido entrenado específicamente para identificar:
+- **Abreviaturas**: formas truncadas (ej: "a.c." = antes de comida)
+- **Siglas**: iniciales de palabras (ej: "EPOC" = enfermedad pulmonar obstructiva crónica)
+- **Acrnimos**: siglas lexicalizadas (ej: "DIU" = dispositivo intrauterino)
+- **Símbolos médicos**: notación clínica (ej: "mmHg", "mg", "cm")
+
+### Formato de Salida
+
+```json
+{
+  "formas_abreviadas": ["HTA", "IAM", "FEVI", "EPOC"],
+  "simbolos_medicos": ["mmHg", "mg/dl", "cm", "ml"]
+}
+```
+
+## Rendimiento
+
+El modelo supera significativamente a los métodos basados en expresiones regulares:
+
+| Sistema | Precisión | Recall | F1 Score |
+|---------|-----------|--------|----------|
+| **SLM (este modelo)** | **0.8902** | **0.9283** | **0.9024** |
+| Expresiones regulares | 0.6271 | 0.7675 | 0.6704 |
+
+## Entrenamiento
+
+### Corpus de Entrenamiento
+- **Fuente**: Informes de alta hospitalaria del Hospital Universitario de Jaén (especialidad de cardiología)
+- **Anotación**: Automática con GPT-4o
+- **Diccionario de referencia**: 7,054 abreviaciones médicas del diccionario SEDOM (Sociedad Española de Documentación Médica)
+- **Tamaño del corpus**: 70% del total de informes disponibles
+
+### Hiperparámetros
+
+```python
+{
+  "per_device_train_batch_size": 2,
+  "per_device_eval_batch_size": 1,
+  "num_train_epochs": 3,
+  "eval_steps": 10,
+  "save_steps": 500,
+  "gradient_accumulation_steps": 4,
+  "learning_rate": 1e-4,
+  "seed": 42,
+  "max_seq_length": 2048
+}
+```
+
+### Frameworks
+- PyTorch
+- Hugging Face Transformers
+- vLLM (para inferencia eficiente)
+
+## Uso
+
+```python
+import json
+import re
+from openai import OpenAI
+
+class ExtractorAbreviaciones:
+    def __init__(self, ruta_modelo, ruta_prompt, base_url="http://localhost:8000/v1"):
+        self.model_path = ruta_modelo
+        self.prompt = self._cargar_prompt(ruta_prompt)
+        self.client = OpenAI(base_url=base_url, api_key="token-abc123")
+    
+    def _cargar_prompt(self, ruta_prompt):
+        with open(ruta_prompt, "r", encoding="utf-8") as file:
+            return file.read()
+    
+    def _extraer_listas(self, texto):
+        """Fallback para extraer listas si el JSON no parsea correctamente"""
+        patrones = {
+            "formas_abreviadas": re.compile(r'"formas_abreviadas": \[([^\]]+)\]'),
+            "simbolos_medicos": re.compile(r'"simbolos_medicos": \[([^\]]+)\]')
+        }
+        datos = {"formas_abreviadas": [], "simbolos_medicos": []}
+        
+        for clave, patron in patrones.items():
+            coincidencia = patron.search(texto)
+            if coincidencia:
+                elementos = [x.strip().strip('"') for x in coincidencia.group(1).split(',')]
+                datos[clave] = elementos
+        
+        return datos
+    
+    def extraer_abreviaciones(self, texto):
+        """Extrae todas las abreviaciones (combinando ambas categorías)"""
+        datos = self.extraer_abreviaciones_simbolos(texto)
+        return list(set(datos["formas_abreviadas"]) | set(datos["simbolos_medicos"]))
+    
+    def extraer_abreviaciones_simbolos(self, text, temperature=0.0, max_new_tokens=128):
+        """
+        Extrae abreviaciones y símbolos médicos de un texto.
+        
+        Args:
+            text (str): El texto clínico a analizar
+            temperature (float): Temperatura para la generación (0.0 = determinista)
+            max_new_tokens (int): Número máximo de tokens a generar
+            
+        Returns:
+            dict: {"formas_abreviadas": [...], "simbolos_medicos": [...]}
+        """
+        try:
+            messages = [
+                {"role": "system", "content": self.prompt},
+                {"role": "user", "content": text}
+            ]
+            
+            response = self.client.chat.completions.create(
+                model=self.model_path,
+                messages=messages,
+                max_tokens=max_new_tokens,
+                temperature=temperature
+            )
+            
+            resp = response.choices.message.content
+            
+            # Intentar parsear como JSON
+            try:
+                return json.loads(resp)
+            except json.JSONDecodeError:
+                # Buscar inicio del JSON
+                inicio_json = resp.find("{")
+                if inicio_json != -1:
+                    json_str = resp[inicio_json:]
+                    try:
+                        return json.loads(json_str)
+                    except json.JSONDecodeError:
+                        return self._extraer_listas(resp)
+                else:
+                    return self._extraer_listas(resp)
+                    
+        except Exception as e:
+            print(f"Error en la solicitud: {str(e)}")
+            return {"formas_abreviadas": [], "simbolos_medicos": []}
+
+# Ejemplo de uso
+extractor = ExtractorAbreviaciones(
+    ruta_modelo="lmolino/extractor_abreviaciones",
+    ruta_prompt="prompt_abreviaciones.txt"
+)
+
+texto = "Paciente con HTA e IAM previo. TA: 140/90 mmHg."
+resultado = extractor.extraer_abreviaciones_simbolos(texto)
+print(resultado)
+# {"formas_abreviadas": ["HTA", "IAM", "TA"], "simbolos_medicos": ["mmHg"]}
+
+```
+
+## Limitaciones y Consideraciones
+
+1. **Dominio específico**: El modelo ha sido entrenado principalmente con informes de cardiología, por lo que su rendimiento puede variar en otras especialidades médicas, aunque su diseño es generalista.
+
+2. **Sensibilidad al formato**: El modelo puede tener dificultades con textos completamente en mayúsculas, donde pierde pistas tipográficas distintivas.
+
+3. **Desambiguación contextual**: Para abreviaciones con múltiples significados (ej: "HTP" = hipertensin portal vs. hipertensión pulmonar), se requiere un módulo adicional de desambiguación basado en contexto clínico.
+
+4. **Dependencia del diccionario SEDOM**: Las abreviaciones detectadas se validan contra el diccionario de 7,054 entradas del SEDOM.
+
+## Casos de Uso
+
+- Preprocesamiento de informes médicos
+- Sistemas de simplificación de textos clínicos
+- Normalización de terminología médica
+- Asistencia en la comunicación médico-paciente
+- Análisis de calidad de documentación clínica
+
+## Licencia
+
+Apache 2.0
\ No newline at end of file
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+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}
diff --git a/training_args.bin b/training_args.bin
new file mode 100644
index 0000000..3a7fa83
--- /dev/null
+++ b/training_args.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0899a11c76db89c507ebff6a605a2245740a2b5fccf471fd4a078f7ca805c848
+size 5496