End of training
Browse files- README.md +14 -89
- config.json +34 -34
- model.safetensors +1 -1
- tokenizer.json +1 -1
README.md
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model-index:
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- name: bert-base-spanish-analysis-app-questions
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results: []
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license: mit
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datasets:
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- devdroide/MiFirma-Ejemplo
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language:
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- es
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pipeline_tag: text-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-spanish-analysis-app-questions
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 1.0
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- F1: 1.0
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- Precision: 1.0
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## Model description
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* Perfil_cliente
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* Procesos
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* Productos
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* Personas_Firmantes
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* Error_324
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* Error_339
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* Error_507
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* Error_532
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* Error_517
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* Error_517_06
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* Error_517_10
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* Error_517_45
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* Error_517_1120
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* Error_301
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### num_labels: 17
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## Training and evaluation data
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### Training hyperparameters
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
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| No log | 1.0 | 22 | 0.
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| No log | 2.0 | 44 | 0.
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| No log | 3.0 | 66 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 4.0 | 88 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 5.0 | 110 | 0.
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| No log | 6.0 | 132 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 7.0 | 154 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 8.0 | 176 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 9.0 | 198 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 10.0 | 220 | 0.
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### Framework versions
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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## Demo - Basic Usage
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```python
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# Colab
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!pip install transformers
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name_model = "devdroide/bert-base-spanish-analysis-app-questions"
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained(name_model)
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model = AutoModelForSequenceClassification.from_pretrained(name_model)
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def classify_question(question):
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inputs = tokenizer(question, padding=True, truncation=True, return_tensors="pt")
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outputs = model(**inputs)
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predictions = outputs.logits.argmax(dim=-1)
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list_label = ['informacion_aplicacion', 'Perfiles', 'Perfil_adminsitrador', 'Perfil_cliente', 'Procesos', 'Productos', 'Personas_Firmantes', 'Error_324', 'Error_339', 'Error_507', 'Error_532', 'Error_517', 'Error_517_06', 'Error_517_10', 'Error_517_45', 'Error_517_1120', 'Error_301']
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return list_label[predictions.item()]
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questions = [
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"¿Qué es mi firma?",
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"Hola, Al cliente le salió en la aplicación el código de error 517:06 ¿Cuál es la recomendación?",
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"Buenas tardes ¿En la herramienta que perfiles hay?",
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"Buenos días, ¿Cuál es el listado de perfiles en la aplicación?",
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"Buenas tardes al cliente le salió el error 517 06 ¿Cuál es la recomendación",
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"Hola Tengo en la herramienta el código de error 517 ¿Cuál es la recomendación?",
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]
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for question in questions:
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category = classify_question(question)
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print(f"Question: {question}")
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print(f"Predicted category: {category}\n")
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# Response example
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# Question: ¿Qué es mi firma?
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# Predicted category: informacion_aplicacion
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# uestion: Hola, Al cliente le salió en la aplicación el código de error 517:06 ¿Cuál es la recomendación?
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# Predicted category: Error_517_06
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# Question: Buenas tardes ¿En la herramienta que perfiles hay?
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# Predicted category: Perfiles
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# Question: Buenos días, ¿Cuál es el listado de perfiles en la aplicación?
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# Predicted category: Perfiles
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# Question: Buenas tardes al cliente le salió el error 517 06 ¿Cuál es la recomendación
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# Predicted category: Error_517_06
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# Question: Hola Tengo en la herramienta el código de error 517 ¿Cuál es la recomendación?
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# Predicted category: Error_517
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```
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model-index:
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- name: bert-base-spanish-analysis-app-questions
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-spanish-analysis-app-questions
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0005
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- Accuracy: 1.0
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- F1: 1.0
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- Precision: 1.0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
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| No log | 1.0 | 22 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 2.0 | 44 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 3.0 | 66 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 4.0 | 88 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 5.0 | 110 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 6.0 | 132 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 7.0 | 154 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 8.0 | 176 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 9.0 | 198 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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| No log | 10.0 | 220 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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### Framework versions
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "
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"1": "
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "informacion_aplicacion",
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"1": "Perfiles",
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"10": "Error_532",
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"11": "Error_517",
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"12": "Error_517_06",
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"13": "Error_517_10",
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"14": "Error_517_45",
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"15": "Error_517_1120",
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"16": "Error_301",
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"2": "Perfil_adminsitrador",
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"3": "Perfil_cliente",
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"4": "Procesos",
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"5": "Productos",
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"6": "Personas_Firmantes",
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"7": "Error_324",
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"8": "Error_339",
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"9": "Error_507"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Error_301": "16",
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"Error_324": "7",
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"Error_339": "8",
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"Error_507": "9",
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"Error_517": "11",
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"Error_517_06": "12",
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"Error_517_10": "13",
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"Error_517_1120": "15",
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"Error_517_45": "14",
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"Error_532": "10",
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"Perfil_adminsitrador": "2",
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"Perfil_cliente": "3",
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"Perfiles": "1",
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"Personas_Firmantes": "6",
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"Procesos": "4",
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"Productos": "5",
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"informacion_aplicacion": "0"
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 439479348
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version https://git-lfs.github.com/spec/v1
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oid sha256:db85e3b5162c5def82c89449f435e72ad0011c863854b33116f1ff26e42c83fb
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size 439479348
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tokenizer.json
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length":
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"strategy": "LongestFirst",
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"stride": 0
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},
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length": 24,
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"strategy": "LongestFirst",
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"stride": 0
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},
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