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Model: somosnlp/Sam_Diagnostic Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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language:
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- es
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- en
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widget:
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- text: |
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<bos><start_of_turn>system
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You are a helpful AI assistant.
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Responde en formato json.
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Eres un agente experto en medicina.
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Lista de codigos linguisticos disponibles: ["es", "es"]<end_of_turn>
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<start_of_turn>user
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Mujer de 83 años, completa y bien desarrollada, con varios años de pérdida de memoria.<end_of_turn>
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<start_of_turn>model
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---
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<div style="display: flex; justify-content: center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/zYaRCrzeECYIYCHCb-2fc.png" style="width: 50%; max-height: 550px;">
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</div>
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## Explorando las Fronteras de la Inteligencia Artificial en el Ámbito Médico con Gemma-2B-IT
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Un nuevo modelo de inteligencia artificial, Gemma-2B-IT, se destaca por su capacidad excepcional. Este modelo, perteneciente a la avanzada familia de IA, ha sido meticulosamente entrenado con un conjunto de datos enriquecido y depurado.
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El proceso de desarrollo de Gemma-2B-IT comenzó con una rigurosa selección de transcripciones médicas en inglés extraidas de Kaggle. Antes de su entrenamiento, me enfrente al desafío de los datos faltantes, una barrera común en la inteligencia artificial. A través de una limpieza de datos meticulosa, se preparó el terreno para un modelo más robusto de su tipo y "confiable".
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La barrera del idioma se superó utilizando tecnologías de IA como chatgpt para traducir el conjunto de datos al español, asegurando así su aplicabilidad en una variedad más amplia de contextos médicos. Este paso crucial no solo aumentó la versatilidad del modelo sino que también demostró la potencialidad de la IA para superar obstáculos lingüísticos en el campo médico.
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Posteriormente, transforme las transcripciones en un formato ChatML, adaptando el dataset para entrenar eficientemente a Gemma-2B-IT. Esto permitió al modelo entender y procesar información médica.
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La evaluación de Gemma-2B-IT: logró un 80% de precisión al interactuar con datos nuevos y un 95% de precisión al trabajar con las transcripciones utilizadas durante su entrenamiento (se modificaron parametros como temperature). Estos resultados no solo demuestran la capacidad de Gemma-2B-IT para aprender y adaptarse, ofreciendo respuestas precisas y relevantes en casi tiempo real.
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como respuesta del modelo seria:
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- **Descripción**: Un resumen detallado del caso del paciente, proporcionado en un lenguaje claro y conciso. Esto permite a los médicos obtener una comprensión rápida y completa de la situación sin necesidad de revisar extensos historiales médicos.
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- **Especialidad Médica**: Basándose en el análisis de los síntomas y la historia clínica, Gemma-2B-IT identifica la especialidad médica más relevante para el caso. Esto asegura que los pacientes sean derivados al especialista adecuado sin demoras innecesarias.
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- **Diagnóstico Principal**: Con un enfoque en la precisión diagnóstica, el modelo proporciona el diagnóstico principal, permitiendo a los médicos centrarse en las intervenciones y tratamientos más pertinentes.
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La promesa de Gemma-2B-IT en el campo de la inteligencia artificial médica es enorme, abriendo puertas a diagnósticos más rápidos, una mayor personalización y una asistencia médica más accesible. A medida que avanzamos hacia un futuro donde la tecnología y la medicina se entrelazan cada vez más, modelos como Gemma-2B-IT marcarán la diferencia.
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## Colab.
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```
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https://colab.research.google.com/drive/1gzTk-mdKefjZwzeiaHfkQ5Qnq3G10WCA?usp=sharing
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```
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## progreso del entrenamiento.
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<div style="display: flex; justify-content: center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/IQR4Ht9ewoPgVcYjU0IuV.png" style="width: 100%; max-height: 550px;">
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</div>
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## Space.
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```
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https://huggingface.co/spaces/somosnlp/Sam_Diagnostic
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```
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## conjunto de datos formato gemma.
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```
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https://huggingface.co/datasets/somosnlp/medical_en_es_formato_chatML_Gemma
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```
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## Limpieza de datos
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```
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https://huggingface.co/datasets/somosnlp/medical_bilingual_en_es
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```
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## Modelo inicial fine-tune.
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```
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1. https://huggingface.co/google/gemma-2b-it
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2. NickyNicky/gemma-2b-it_oasst2_all_chatML_Unsloth_V1
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```
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## Nota.
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```
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La AI no te quitara el trabajo...
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El trabajo te lo quitara otra persona que tenga conocimiento fundamental y avanzado de la AI.
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```
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## Hecho.
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```
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https://huggingface.co/NickyNicky
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```
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<!-- Codigo de entrenamiento: https://colab.research.google.com/drive/1UmG6X_vRqMCIWqoPrdMdDkUJCW5oxrGp#scrollTo=HvaM3RKiklXS&uniqifier=1 -->
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config.json
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"architectures": [
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"GemmaForCausalLM"
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"head_dim": 256,
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"hidden_act": "gelu",
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"initializer_range": 0.02,
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"use_cache": true,
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"vocab_size": 256000
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}
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||||
}
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
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||||
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||||
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||||
},
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||||
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||||
},
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"pad_token": {
|
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"content": "<pad>",
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||||
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|
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"normalized": false,
|
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"rstrip": false,
|
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|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:05e97791a5e007260de1db7e1692e53150e08cea481e2bf25435553380c147ee
|
||||
size 17477929
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||
size 4241003
|
||||
71
tokenizer_config.json
Normal file
71
tokenizer_config.json
Normal file
@@ -0,0 +1,71 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<unk>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"106": {
|
||||
"content": "<start_of_turn>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"107": {
|
||||
"content": "<end_of_turn>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
|
||||
"bos_token": "<bos>",
|
||||
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<eos>",
|
||||
"legacy": null,
|
||||
"max_length": 2048,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<pad>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "GemmaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user