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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.venv/
+.env
+__pycache__/

LICENSE

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+PROPRIETARY SOFTWARE LICENSE
+
+Copyright (C) 2025 Cátedra Cajasiete BigData, OpenData & Blockchain
+
+Este software es propiedad de Cátedra Cajasiete BigData, OpenData & Blockchain y está protegido por las leyes de derechos de autor. Su uso está estrictamente restringido según los términos del convenio aplicable.
+
+## RESTRICCIONES:
+- No se permite la copia, modificación, distribución ni uso sin autorización previa por escrito de Cátedra Cajasiete BigData, OpenData & Blockchain.
+- Se prohíbe el uso de este software con fines comerciales sin un acuerdo de licencia válido.
+- No se otorga ninguna garantía sobre el software; su uso es bajo responsabilidad del usuario autorizado.
+
+Para obtener permisos de uso o licencias comerciales, por favor contacta a: catedrabob@ull.edu.es.

README.md

@@ -1,20 +1,327 @@
 ---
-title: CNO 11 Classification
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
 license: other
+license_name: proprietary-license
+license_link: LICENSE
+language:
+- es
+base_model:
+- intfloat/multilingual-e5-large
+pipeline_tag: text-classification
 ---
 
-# Welcome to Streamlit!
+# Model Card for Model ID
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+<!-- Provide a quick summary of what the model is/does. -->
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Cátedra Cajasiete de Big Data, Open Data y Blockchain de la Universidad de La Laguna
+- **Funded by:** Cajasiete y la Universidad de La Laguna
+- **Shared by:** Cátedra Cajasiete de Big Data, Open Data y Blockchain de la Universidad de La Laguna and Instituto Canario de Estadística
+- **Model type:** text-classification
+- **Language(s) (NLP):** Spanish
+- **License:** [Proprietary](LICENSE)
+- **Finetuned from model:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+- **Paper [TODO]:** [TODO](https://)
+
+## 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. -->
+This model has been trained to classify text into CNOs ([Código Nacional de Ocupaciones](https://enclaveformacion.com/cno-11/)) in Spanish. It is intended to be used by researchers, developers, and organizations interested in analyzing and classifying occupational data in the Spanish language.
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging 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. -->
+
+The model will not work well if used to classify non-Spanish text, as it was trained exclusively on it.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Because of the model has been trained with data from socioeconomic surveys, it may have inherent biases in the training data. These biases may manifest themselves in the classification of occupations, especially those that are less well represented in the data. In addition, the model may not generalize well to occupations that are not well represented in the training set.
+
+Another limitation to consider is that since the CNO was created, which is the national occupational classification system used in Spain, as will be explained later, new occupations have appeared that are not included in the model. Therefore, the model may not be able to correctly classify these new occupations. Such as, for example, Streamer, Influencer, etc.
+
+### 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.
+
+### Install necessary libraries
+
+```
+pip install torch
+pip install transformers
+```
+
+[TODO: Check if it's necessary to do anything more than this]
+
+### Load model
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained("bob-nlp/A5-CNO-BOB-ISTAC-D12")
+```
+
+### Load tokenizer
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bob-nlp/A5-CNO-BOB-ISTAC-D12")
+```
+
+### Using the model
+
+```python
+import torch
+from torch.nn.functional import softmax
+
+text_to_predict = []
+text = "text to classify"
+is_single_item = isinstance(text, str)
+if is_single_item: # Data to predict must be a list of strings, even if it's only one string
+    text_to_predict = [text]
+
+inputs = tokenizer(text_to_predict, padding=True, truncation=True, max_length=512, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+logits = outputs.logits
+probabilities = softmax(logits, dim=1)
+id2label = model.config.id2label
+
+sorted_predictions = []
+for i in range(logits.shape[0]):
+        single_probs = probabilities[i]
+        scores_dict = {id2label[j]: single_probs[j].item() for j in range(len(id2label))}
+        sorted_prediction = sorted(scores_dict.items(), key=lambda item: item[1], reverse=True)
+        sorted_predictions.append(sorted_prediction)
+best_prediction_info = sorted_predictions[0]
+best_label, best_label_prob = best_prediction_info[0]
+```
+
+### Convert result to CNO
+
+The result given by the model will be in a "LABEL_(NUMBER)" format. In order to translate it to a CNO, you must follow these steps:
+
+1) Download the file `cno_utils.py` in the `utils` folder of this repository.
+2) Add the following to in your code:
+```python
+from cno_utils import convert_to_cno
+
+cno_predicted_code = convert_to_cno(best_label)
+```
+
+You must have previously installed `pandas` and `huggingface_hub` for it to work:
+```
+pip install huggingface_hub
+pip install pandas
+```
+
+Alternatively, download the `idxs.csv` file found in the `data` folder of this repository and copy the following into your code:
+```python
+import pandas
+
+def _load_label_mapping():
+    csv_path = hf_hub_download(repo_id="bob-nlp/A5-CNO-BOB-ISTAC-D12", filename="LOCAL/PATH/TO/idxs.csv")
+    df = pd.read_csv(csv_path)
+    _label_mapping = dict(zip(df['label'], df['CNO']))
+    return _label_mapping
+
+def convert_to_cno(output_label):
+    mapping = _load_label_mapping()
+    return mapping.get(output_label, output_label)
+```
+
+And then simply call `convert_to_cno()`.
+
+
+### Get description of the CNO
+
+1) Download the file `cno_utils.py` in the `utils` folder of this repository.
+2) Add the following to in your code:
+```python
+from cno_utils import get_cno_description
+
+cno_description = get_cno_description(cno_predicted_code)
+```
+
+You must have previously installed `pandas` and `huggingface_hub` for it to work:
+```
+pip install huggingface_hub
+pip install pandas
+```
+
+Alternatively, download the `cno11_notas.csv` file found in the `data` folder of this repository and copy the following into your code:
+```python
+import pandas
+
+def _load_description_mapping():
+    csv_path = hf_hub_download(repo_id="bob-nlp/A5-CNO-BOB-ISTAC-D12", filename="LOCAL/PATH/TO/cno11_notas.csv")
+    df = pd.read_csv(csv_path)
+    _description_mapping = dict(zip(df['CNO'], df['DN4']))
+    return _description_mapping
+
+def get_cno_description(cno):
+    mapping = _load_description_mapping()
+    return mapping.get(cno, 'Unknown')
+```
+
+And then simply call `get_cno_description()`.
+
+
+## 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. -->
+
+This model has been trained using an aggregated data set from various socioeconomic surveys conducted by the Instituto Canario de Estadística (ISTAC). The ISTAC is the official statistical agency of the autonomous community of the Canary Islands, in charge of producing and disseminating statistical information of public interest.  
+
+The training dataset is composed of individual responses to surveys designed to capture a representative picture of the social and economic situation of the population in the Canary Islands.  
+Although the specific dataset used for this model cannot be directly redistributed, the original ISTAC surveys, such as the Survey of Income and Living Conditions of Canarian Households (EICVHC) or the Survey of Socioeconomic Habits and Confidence (ECOSOC), provide insight into the type of information collected. You can consult the microdata and documentation of these and other surveys in the [ISTAC data portal](https://datos.canarias.es/catalogos/estadisticas/organization/istac?_groups_limit=0&_res_format_limit=0&res_format=ODS&organization=istac&groups=sociedad-bienestar&license_id=istac-aviso-legal).  
+The variables included in the training dataset are fundamental to the task of occupational classification and reflect a variety of demographic and socioeconomic factors.
+
+The variables used are:  
+* EDAD_RANGO: Age range of the respondent.
+* SEXO: Sex of the respondent.
+* INGRESO: Income level of the household or individual.
+* ESTUDIOS: Level of education attained.
+* SITUACION: Employment status (e.g., employed, unemployed, inactive).
+* ACTIVIDAD: Sector of economic activity.
+* TAREA: Description of the main task performed at work.
+* CNO: National Code of Occupations.  
+
+The target variable of the model is CNO. The CNO is the national classification system of [occupations used in Spain](https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736177033&menu=ultiDatos&idp=1254735976614), managed by the National Statistics Institute (INE). This system organizes occupations in a hierarchical structure that facilitates the grouping and analysis of labor data. The model has been trained with the CNO-11 version of this classification.
+
+### 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 
+
+The main challenge of the training data was the class imbalance in the target variable CNO, as the most common occupations in the Canary Islands (e.g., "restaurant services and commerce") were overrepresented. To mitigate the bias towards the majority classes, a data augmentation technique was applied by generating synthetic entries for the underrepresented occupations. This process balances the distribution of classes, improving the generalizability of the model. In addition, categorical variables were coded into numerical format and null values were managed to ensure data quality.
+
+Preprocessing also included the following standard steps:
+
+* **Coding of categorical variables**: Variables such as AGE_RANGE, SEX, STUDIES, STATUS, and ACTIVITY were converted to a numerical format (e.g., by One-Hot Encoding) so that they could be processed by the model.
+* **Null Value Handling**: A strategy was implemented to deal with inputs with missing values.
+
+#### Training Hyperparameters
+
+The model was fine-tuned from **`intfloat/multilingual-e5-large`** using the following configuration:
+
+| Parameter | Value | Description |
+| :--- | :--- | :--- |
+| **Base Model** | `intfloat/multilingual-e5-large` | Pre-trained model used as a starting point. |
+| **`TEST_SIZE`** | `0.3` | Proportion of the dataset reserved for testing. |
+| **`RANDOM_STATE`** | `42` | Seed for reproducible data splitting. |
+| **`NUM_TRAIN_EPOCHS`** | `16` | Maximum number of training epochs. |
+| **`BATCH_SIZE`** | `24` | Batch size per device. |
+| **`LEARNING_RATE`** | `2e-05` | Learning rate for the optimizer. |
+| **`EARLY_STOPPING_PATIENCE`**| `2` | Epochs to wait for improvement before stopping training. |
+| **`EARLY_STOPPING_THRESHOLD`**| `0.01` | Minimum change to be considered an improvement. |
+| **`LOGGING_STEPS`** | `500` | Logging frequency (in steps). |
+
+- **Training regime:** fp32 (Full Precision) <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+
+## 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. -->
+
+The evaluation of the model was performed using a test set that was not used during training. This test set is composed of a representative sample of the population of the [Canary Islands](https://en.wikipedia.org/wiki/Canary_Islands), ensuring that the model's performance is evaluated on data that reflects the diversity and complexity of real-world scenarios.
+
+#### 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. -->
+
+The model's performance was assessed using a set of metrics carefully chosen to reflect the challenges of this classification task, namely the class imbalance and the hierarchical nature of the CNO labels.  
+
+* *Accuracy*: This is the most straightforward metric, representing the overall percentage of correctly predicted occupations. While it provides a general overview of performance, it can be misleading in imbalanced datasets. A model could achieve high accuracy by simply predicting the most common occupations well, while failing on rarer ones. It is included as a baseline reference.
+
+* *Balanced Accuracy*: This metric was chosen specifically to counteract the weakness of standard accuracy. It calculates the average recall across all classes, giving equal weight to each one regardless of how frequently it appears. A high Balanced Accuracy score indicates that the model is performing well on both common and rare occupations, making it a much fairer assessment of a model's true generalization capability on this dataset.
+
+* *Recall (macro)*: Recall measures the model's ability to correctly identify all relevant instances of a class ("What proportion of actual positives was identified correctly?"). The macro average calculates recall independently for each class and then takes the unweighted mean. This is crucial because it treats a failure to identify a rare occupation as equally important as a failure to identify a common one. It directly measures how well the model "finds" examples from every single category.
+
+* *F1-score (macro)*: The F1-score is the harmonic mean of precision and recall. By using the macro average, we get a single, balanced measure of performance across all classes. It is one of the most important metrics for this task because a high macro F1-score requires the model to have both good precision (not mislabeling other occupations as the target class) and good recall (finding all instances of the target class), and to do so for rare and common classes alike.
+
+* *H-F1-score (Hierarchical F1-score)*: This metric was chosen because the CNO classification is inherently hierarchical. A standard F1-score treats all errors equally; for instance, mistaking a "Web Developer" for a "Farmer" is just as bad as mistaking it for a "Software Engineer". The Hierarchical F1-score is more nuanced. It gives partial credit for predictions that are incorrect but "close" in the occupational hierarchy. This provides a more practical measure of the model's utility, as a prediction within the correct professional group is significantly more useful than one that is completely unrelated.
+
+### Results
+
+The model achieved the following performance on the test set:
+| Metric | Score |
+| :--- | :--- |
+| Accuracy | 0.81 |
+| Balanced Accuracy | 0.69 |
+| Recall (macro) | 0.65 |
+| F1-score (macro) | 0.64 |
+| H-F1-score | 0.85 |
+
+**Note:** `Recall` and `F1-score` were calculated using a macro average to provide a fair performance measure across all classes, including the underrepresented ones.
+
+#### Hardware
+
+This model is a fine-tuned version of intfloat/multilingual-e5-large, a large-sized transformer. As such, the hardware requirements depend on whether you are running the model for inference or for training.
+
+**Inference (Using the Model)**
+For running inference, a GPU is highly recommended for optimal performance, especially for batch processing.
+* CPU: While it is possible to run this model on a multi-core CPU, expect significant latency. This may be acceptable for offline, low-volume tasks, but it is not suitable for real-time applications.  
+
+* GPU (Recommended): For efficient inference, a modern GPU with at least 6-8 GB of VRAM is recommended (e.g., NVIDIA Tesla T4, RTX 3060). This will allow for reasonably fast predictions and the processing of multiple requests in batches.
+
+**Training (Reproducing the Fine-Tuning)**
+Fine-tuning a large-sized model is computationally intensive and requires a high-end GPU.
+
+## Citation 
+
+<!-- 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]
+
+## Model Card Contact
+- **Organization:** Cajasiete Chair Cajasiete BigData, OpenData & Blockchain
+- **Email:** catedrabob@ull.edu.es

app.py

@@ -0,0 +1,198 @@
+import streamlit as st
+from streamlit_chat import message
+import pandas as pd
+import os
+import json
+from transformers import pipeline
+from dotenv import load_dotenv
+from utils.cno_utils import convert_to_cno, get_cno_description
+
+AVATAR_PATH = "https://avatars.githubusercontent.com/u/122880210?s=200&v=4"
+st.set_page_config(
+    "Clasificador CNO 🤖", "🤖", layout="wide", initial_sidebar_state="expanded"
+)
+
+st.markdown(
+    """<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
+        <style>
+            html, body, [class*="css"] {font-family: "Inter", sans-serif;}
+            header, footer {visibility: hidden;}
+            .block-container {padding-top: 0.5rem; display: flex; flex-direction: column; min-height: 100vh;}
+            .stChatMessage.user {background: linear-gradient(120deg,#00c6ff 0%,#0072ff 100%); color:#fff; border-radius:1rem; padding:0.75rem 1rem; margin:0.25rem 0;}
+            .stChatMessage.bot  {background:#fff; border:1px solid #e0e0e0; border-radius:1rem; padding:0.75rem 1rem; margin:0.25rem 0;}
+            .stChatMessage.bot img {height:24px;width:24px;border-radius:50%;margin-right:0.5rem;}
+            ::-webkit-scrollbar         {width:8px;}
+            ::-webkit-scrollbar-thumb   {background:#8f9dff;border-radius:10px;}
+            .sidebar-title {font-size:0.9rem;font-weight:600;margin:0.5rem 0 0.25rem;color:#4b4b4b;}
+            div.msg ul{list-style:none;padding-left:0;margin:0;}
+            [data-testid="stSidebarCollapseButton"] {
+    display: none;
+}
+        </style>""",
+    unsafe_allow_html=True,
+)
+MODEL_ID = "bob-nlp/A5-CNO-BOB-ISTAC-D12"
+
+
+@st.cache_resource
+def load_huggingface_model():
+    """Carga el pipeline de inferencia desde Hugging Face Hub."""
+    load_dotenv()
+    hf_token = os.environ.get("HF_TOKEN")
+    if not hf_token:
+        # Si el token no está, mostramos un error claro en la app.
+        st.error(
+            "HF_TOKEN no encontrado. Por favor, configúralo en los 'Secrets' de tu Space.",
+            icon="🔑",
+        )
+        return None
+    try:
+        model_pipeline = pipeline(
+            "text-classification",
+            model=MODEL_ID,
+            token=hf_token,
+        )
+        return model_pipeline
+    except Exception as e:
+        st.error(f"Error al cargar el modelo '{MODEL_ID}': {e}", icon="🔥")
+        return None
+
+
+def load_json_file(filename):
+    try:
+        with open(filename, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except FileNotFoundError:
+        st.warning(
+            f"El archivo '{filename}' no se ha encontrado en el repositorio del Space."
+        )
+        return {}
+    except Exception as e:
+        st.error(f"Error al leer el archivo JSON '{filename}': {e}")
+        return {}
+
+
+pipe = load_huggingface_model()
+METADATA = load_json_file("data/metadata.json")
+PROBLEMATIC_CNOS = load_json_file("data/problematic_cnos.json")
+
+
+def run_inference(text_input):
+    """
+    Función que ejecuta la inferencia usando el pipeline de Hugging Face
+    y formatea la salida para mostrarla en la UI.
+    """
+    if not pipe:
+        return "Error: El modelo no está cargado."
+
+    try:
+        results = pipe(text_input, top_k=3)
+        PROBLEMATIC_CNO_MESSAGE = " ⚠️⚠️⚠️ **Cuidado: código poco fiable**  "
+        out = []
+
+        # TODO: Modificar descripción código
+        for response in results:
+            response["label"] = convert_to_cno(response["label"])
+            response["description"] = get_cno_description(response["label"])
+            main_msg = (
+                f"Predicción: **{response['label']}**: {response['description']} "
+                f"Certeza: **{response['score']:.2f}**  "
+            )
+
+            if response["label"] in PROBLEMATIC_CNOS:
+                main_msg += PROBLEMATIC_CNO_MESSAGE
+            out.append(main_msg)
+
+        return "\n".join(out)
+
+    except Exception as e:
+        return f"Ocurrió un error durante la inferencia: {e}"
+
+
+st.sidebar.title("Clasificador CNO-11")
+st.sidebar.markdown("---")
+st.sidebar.markdown(
+    "<div class='sidebar-title'>🎛️ Filtros</div>", unsafe_allow_html=True
+)
+
+
+def init_state() -> None:
+    defaults = load_json_file("data/defaults_session_state.json")
+    for k, v in defaults.items():
+        st.session_state.setdefault(k, v)
+
+
+init_state()
+
+
+def on_controls_change() -> None:
+    st.session_state.past.clear()
+    st.session_state.generated.clear()
+
+
+for col, metadatas in METADATA.items():
+    sel = st.sidebar.selectbox(
+        label=col,
+        options=list(metadatas),
+        key=f"select_{col}",
+        on_change=on_controls_change,
+        format_func=lambda x: x["textual"],
+    )
+    st.session_state.selections[col] = sel
+
+st.sidebar.divider()
+if st.sidebar.button("🗑️ Limpiar conversación", use_container_width=True):
+    on_controls_change()
+    st.rerun()
+
+st.title("🤖 Clasificador de Códigos CNO-11")
+st.info(
+    f"Utilizando el modelo: **[{MODEL_ID}](https://huggingface.co/bob-nlp/A5-CNO-BOB-ISTAC-D12)**"
+)
+
+
+def add_user_message(text: str):
+    st.session_state.past.append(text)
+    to_classify = f"{text}."
+    if st.session_state.selections:
+        for col, sel in st.session_state.selections.items():
+            if sel:
+                to_classify += f" {sel['textual']}."
+    response = run_inference(to_classify)
+    st.session_state.generated.append((response))
+
+
+def render_chat():
+    message(
+        "¡Hola! Soy el clasificador de códigos CNO-11. Por favor, introduce una descripción de la tarea o ocupación que quieres clasificar y te ayudaré a encontrar el código CNO correspondiente.",
+        is_user=False,
+        key="welcome",
+        logo=AVATAR_PATH,
+    )
+
+    for i, (u, b) in enumerate(zip(st.session_state.past, st.session_state.generated)):
+        message(u, is_user=True, key=f"u{i}", avatar_style="no-avatar")
+        message(b, key=f"b{i}", logo=AVATAR_PATH)
+
+
+chat_box = st.container()
+with chat_box:
+    st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+    render_chat()
+    new_text = st.chat_input("Escribe aquí el texto a clasificar…")
+    if new_text:
+        add_user_message(new_text)
+        st.rerun()
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# Footer
+st.markdown("---")
+st.markdown(
+    """
+    <div style="text-align: center; color: #666; font-size: 0.8rem; padding: 1rem 0;">
+        Desarrollado por <strong>Cátedra Cajasiete de Big Data, Open Data y Blockchain</strong><br>
+        Universidad de La Laguna
+    </div>
+    """,
+    unsafe_allow_html=True,
+)

config.json

@@ -0,0 +1,1036 @@
+{
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data/metadata.json

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+{
+  "EDAD_RANGO": [
+    { "numerico": 0, "textual": ""},
+    { "numerico": 1, "textual": "Menor de 20 años" },
+    { "numerico": 2, "textual": "Entre 20 y 29 años" },
+    { "numerico": 3, "textual": "Entre 30 y 39 años" },
+    { "numerico": 4, "textual": "Entre 40 y 49 años" },
+    { "numerico": 5, "textual": "Entre 50 y 59 años" },
+    { "numerico": 6, "textual": "Entre 60 y 69 años" },
+    { "numerico": 7, "textual": "Entre 70 y 79 años" },
+    { "numerico": 8, "textual": "Con 80 años o más" }
+  ],
+
+  "SEXO": [
+    { "numerico": 0, "textual": ""},
+    { "numerico": 1, "textual": "Hombre" },
+    { "numerico": 6, "textual": "Mujer" }
+  ],
+
+  "INGRESOS": [
+    { "numerico": 0, "textual": ""},
+    { "numerico": 1, "textual": "Ingresos del hogar hasta 500€" },
+    { "numerico": 2, "textual": "Ingresos del hogar de más de 500€ hasta 1000€" },
+    { "numerico": 3, "textual": "Ingresos del hogar de más de 1000€ hasta 1500€" },
+    { "numerico": 4, "textual": "Ingresos del hogar de más de 1500€ hasta 2000€" },
+    { "numerico": 5, "textual": "Ingresos del hogar de más de 2000€ hasta 2500€" },
+    { "numerico": 6, "textual": "Ingresos del hogar de más de 2500€ hasta 3500€" },
+    { "numerico": 7, "textual": "Ingresos del hogar de más de 3500€" }
+  ],
+
+  "ESTUDIOS": [
+    { "numerico": 0, "textual": ""},
+    { "numerico": 1,  "textual": "No sabe leer ni escribir" },
+    { "numerico": 2,  "textual": "Sabe leer y escribir pero fue menos de 5 años a la escuela" },
+    { "numerico": 3,  "textual": "Sabe leer y escribir y fue a la escuela 5 o más años sin completar: EGB, 3º ESO, Bachillerato Elemental o certificado de escolaridad. En esta categoría se incluye la Formación Básica Inicial de adultos completada" },
+    { "numerico": 4,  "textual": "Cursado 3º curso o superior de ESO sin título de Graduado en ESO, cursada la EGB completa sin título de Graduado Escolar, certificado de escolaridad" },
+    { "numerico": 5,  "textual": "EGB terminada (Graduado Escolar), Graduado en ESO, Bachillerato Elemental, certificado de estudios primarios o de profesionalidad niveles 1 y 2. Se incluye Formación Básica Postinicial de adultos terminada" },
+    { "numerico": 6,  "textual": "Bachiller Superior, BUP, Bachiller, COU, PREU" },
+    { "numerico": 7,  "textual": "FP1, Ciclo Formativo de Grado Medio, título de técnico auxiliar o equivalente. Incluye enseñanzas profesionales de música y/o danza, certificado de nivel avanzado de la Escuela Oficial de Idiomas y certificado de profesionalidad de nivel 3, Oficialía Industrial y Formación Profesional Básica" },
+    { "numerico": 8,  "textual": "FP2, Ciclo Formativo de Grado Superior, Maestría Industrial. Título de técnico especialista o equivalente" },
+    { "numerico": 9,  "textual": "Diplomatura, Grado, títulos superiores de música y/o danza" },
+    { "numerico": 10, "textual": "Licenciatura, Máster universitario u otros estudios de postgrado (especialistas, expertos)" },
+    { "numerico": 11, "textual": "Doctorado" }
+  ],
+
+  "SITUACION": [
+    { "numerico": 0, "textual": ""},
+    { "numerico": 1, "textual": "Asalariado/a del sector privado" },
+    { "numerico": 2, "textual": "Asalariado/a del sector público" },
+    { "numerico": 3, "textual": "Trabajador/a bajo programa público de empleo remunerado" },
+    { "numerico": 4, "textual": "Aprendiz remunerado" },
+    { "numerico": 5, "textual": "Empleador/a (no miembro de cooperativa) con menos de 10 empleados" },
+    { "numerico": 6, "textual": "Empleador/a (no miembro de cooperativa) con 10 o más empleados" },
+    { "numerico": 7, "textual": "Empresario/a sin asalariados o trabajador/a / profesional independiente / autónomo/a" },
+    { "numerico": 8, "textual": "Ayuda en negocios familiares" }
+  ]
+}

data/problematic_cnos.json

@@ -0,0 +1,9 @@
+[
+  "C1120", "C1219", "C1223", "C1312", "C1313", "C1316", "C1325", "C1419", "C1431", "C1432",
+  "C1501", "C2324", "C2413", "C2424", "C2471", "C2611", "C2622", "C2623", "C2722", "C2121",
+  "C3121", "C3122", "C3123", "C3124", "C3128", "C3129", "C3141", "C3143", "C3203", "C3339",
+  "C3402", "C3403", "C3404", "C3535", "C3621", "C3811", "C3713", "C4112", "C4113", "C4122",
+  "C5722", "C5824", "C5899", "C5210", "C6421", "C6422", "C7122", "C7223", "C7293", "C7314",
+  "C7521", "C7705", "C8121", "C8193", "C8199", "C9410", "C2431", "C2512", "C3125", "C3132",
+  "C3715", "C5612", "C6202", "C7211", "C7618"
+]

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:438a27f6fa94ae0c75c8f696083fd83bbb0fa90ffcd892d5424c1e059fab5697
+size 135

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+transformers
+torch
+streamlit
+streamlit-chat
+sentencepiece
+dotenv

sentencepiece.bpe.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8a54190d2b9256881ed34ab5428786629f929dd5a579350a6ef4735b86a9208
+size 132

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de6f09c3f9b891e5b98dd3af9463dcab5a97d5265e288271395324a0577e6c05
+size 133

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdff2120bb73ad47b72318a842a1c40c46b6c14d6871c0c6ab45e1318c3b28c8
+size 129

utils/cno_utils.py

@@ -0,0 +1,41 @@
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+def _load_label_mapping():
+    csv_path = hf_hub_download(repo_id="bob-nlp/A5-CNO-BOB-ISTAC-D12", filename="data/idxs.csv")
+    df = pd.read_csv(csv_path)
+    _label_mapping = dict(zip(df['label'], df['CNO']))
+    return _label_mapping
+
+def _load_description_mapping():
+    csv_path = hf_hub_download(repo_id="bob-nlp/A5-CNO-BOB-ISTAC-D12", filename="data/cno11_notas.csv")
+    df = pd.read_csv(csv_path)
+    _description_mapping = dict(zip(df['CNO'], df['DN4']))
+    return _description_mapping
+
+def convert_to_cno(output_label):
+    """
+    Converts a model label (e.g., 'LABEL_0') to the CNO format (e.g., 'C1111').
+
+    Parameters:
+        output_label (str): Label from the model (like 'LABEL_0')
+        model_id (str): The Hugging Face model ID
+
+    Returns:
+        str: Converted label, or original if not found.
+    """
+    mapping = _load_label_mapping()
+    return mapping.get(output_label, output_label)
+
+def get_cno_description(cno):
+    """
+    Retrieves the description for a given CNO code.
+
+    Parameters:
+        cno (str): The CNO code (e.g., 'C1111')
+
+    Returns:
+        str: Description of the CNO code, or 'Unknown' if not found.
+    """
+    mapping = _load_description_mapping()
+    return mapping.get(cno, 'Unknown')