End of training

README.md

@@ -10,12 +10,6 @@ metrics:
 model-index:
 - name: bert-base-spanish-analysis-app-questions
   results: []
-license: mit
-datasets:
-- devdroide/MiFirma-Ejemplo
-language:
-- es
-pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -23,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-spanish-analysis-app-questions
 
-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 [devdroide/MiFirma-Ejemplo](https://huggingface.co/datasets/devdroide/MiFirma-Ejemplo) dataset.
+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.
 It achieves the following results on the evaluation set:
-- Loss: 0.0004
+- Loss: 0.0005
 - Accuracy: 1.0
 - F1: 1.0
 - Precision: 1.0
@@ -33,31 +27,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This model was fine-tuned for question classification in a fictitious app. List label from dataset:
-
-*  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
-
-### num_labels: 17
+More information needed
+
+## Intended uses & limitations
+
+More information needed
 
 ## Training and evaluation data
 
-Set of frequently asked questions for an application. The set of questions consists of approximately 680 questions in Spanish. The set has the split of training, validation and testing.
+More information needed
+
+## Training procedure
 
 ### Training hyperparameters
 
@@ -74,16 +54,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
-| No log        | 1.0   | 22   | 0.0026          | 1.0      | 1.0 | 1.0       | 1.0    |
-| No log        | 2.0   | 44   | 0.0014          | 1.0      | 1.0 | 1.0       | 1.0    |
+| No log        | 1.0   | 22   | 0.0028          | 1.0      | 1.0 | 1.0       | 1.0    |
+| No log        | 2.0   | 44   | 0.0015          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 3.0   | 66   | 0.0010          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 4.0   | 88   | 0.0008          | 1.0      | 1.0 | 1.0       | 1.0    |
-| No log        | 5.0   | 110  | 0.0006          | 1.0      | 1.0 | 1.0       | 1.0    |
+| No log        | 5.0   | 110  | 0.0007          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 6.0   | 132  | 0.0006          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 7.0   | 154  | 0.0005          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 8.0   | 176  | 0.0005          | 1.0      | 1.0 | 1.0       | 1.0    |
 | No log        | 9.0   | 198  | 0.0005          | 1.0      | 1.0 | 1.0       | 1.0    |
-| No log        | 10.0  | 220  | 0.0004          | 1.0      | 1.0 | 1.0       | 1.0    |
+| No log        | 10.0  | 220  | 0.0005          | 1.0      | 1.0 | 1.0       | 1.0    |
 
 
 ### Framework versions
@@ -92,58 +72,3 @@ The following hyperparameters were used during training:
 - Pytorch 2.3.1+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1
-
-## Demo - Basic Usage
-
-```python
-# Colab
-
-!pip install transformers
-
-name_model = "devdroide/bert-base-spanish-analysis-app-questions"
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained(name_model)
-model = AutoModelForSequenceClassification.from_pretrained(name_model)
-
-def classify_question(question):
-    inputs = tokenizer(question, padding=True, truncation=True, return_tensors="pt")
-    outputs = model(**inputs)
-    predictions = outputs.logits.argmax(dim=-1)
-    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']
-    return list_label[predictions.item()]
-
-questions = [
-    "¿Qué es mi firma?",
-    "Hola, Al cliente le salió en la aplicación el código de error 517:06 ¿Cuál es la recomendación?",
-    "Buenas tardes ¿En la herramienta que perfiles hay?",
-    "Buenos días, ¿Cuál es el listado de perfiles en la aplicación?",
-    "Buenas tardes al cliente le salió el error 517 06 ¿Cuál es la recomendación",
-    "Hola Tengo en la herramienta el código de error 517 ¿Cuál es la recomendación?",
-]
-
-for question in questions:
-    category = classify_question(question)
-    print(f"Question: {question}")
-    print(f"Predicted category: {category}\n")
-
-# Response example
-# Question: ¿Qué es mi firma?
-# Predicted category: informacion_aplicacion
-
-# uestion: Hola, Al cliente le salió en la aplicación el código de error 517:06 ¿Cuál es la recomendación?
-# Predicted category: Error_517_06
-
-# Question: Buenas tardes ¿En la herramienta que perfiles hay?
-# Predicted category: Perfiles
-
-# Question: Buenos días, ¿Cuál es el listado de perfiles en la aplicación?
-# Predicted category: Perfiles
-
-# Question: Buenas tardes al cliente le salió el error 517 06 ¿Cuál es la recomendación
-# Predicted category: Error_517_06
-
-# Question: Hola Tengo en la herramienta el código de error 517 ¿Cuál es la recomendación?
-# Predicted category: Error_517
-
-```

config.json

@@ -10,44 +10,44 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5",
-    "6": "LABEL_6",
-    "7": "LABEL_7",
-    "8": "LABEL_8",
-    "9": "LABEL_9",
-    "10": "LABEL_10",
-    "11": "LABEL_11",
-    "12": "LABEL_12",
-    "13": "LABEL_13",
-    "14": "LABEL_14",
-    "15": "LABEL_15",
-    "16": "LABEL_16"
+    "0": "informacion_aplicacion",
+    "1": "Perfiles",
+    "10": "Error_532",
+    "11": "Error_517",
+    "12": "Error_517_06",
+    "13": "Error_517_10",
+    "14": "Error_517_45",
+    "15": "Error_517_1120",
+    "16": "Error_301",
+    "2": "Perfil_adminsitrador",
+    "3": "Perfil_cliente",
+    "4": "Procesos",
+    "5": "Productos",
+    "6": "Personas_Firmantes",
+    "7": "Error_324",
+    "8": "Error_339",
+    "9": "Error_507"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_10": 10,
-    "LABEL_11": 11,
-    "LABEL_12": 12,
-    "LABEL_13": 13,
-    "LABEL_14": 14,
-    "LABEL_15": 15,
-    "LABEL_16": 16,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4,
-    "LABEL_5": 5,
-    "LABEL_6": 6,
-    "LABEL_7": 7,
-    "LABEL_8": 8,
-    "LABEL_9": 9
+    "Error_301": "16",
+    "Error_324": "7",
+    "Error_339": "8",
+    "Error_507": "9",
+    "Error_517": "11",
+    "Error_517_06": "12",
+    "Error_517_10": "13",
+    "Error_517_1120": "15",
+    "Error_517_45": "14",
+    "Error_532": "10",
+    "Perfil_adminsitrador": "2",
+    "Perfil_cliente": "3",
+    "Perfiles": "1",
+    "Personas_Firmantes": "6",
+    "Procesos": "4",
+    "Productos": "5",
+    "informacion_aplicacion": "0"
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d7cb23705e90854dbcef51e3ef2d907cd492e2a4de419dcd8f560b4c553c60df
+oid sha256:db85e3b5162c5def82c89449f435e72ad0011c863854b33116f1ff26e42c83fb
 size 439479348

tokenizer.json

@@ -2,7 +2,7 @@
   "version": "1.0",
   "truncation": {
     "direction": "Right",
-    "max_length": 34,
+    "max_length": 24,
     "strategy": "LongestFirst",
     "stride": 0
   },