This model has been trained to classify text into CNOs (Código Nacional de Ocupaciones) in Spanish. It is intended to be used by researchers, developers, and organizations interested in analyzing and classifying occupational data in the Spanish language.
Bias, Risks, and 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 classification was created in 2011, which is the national occupational classification system used in Spain as it 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.
Similarly, the training data used is written exclusively in Spanish. Since the base model, multilingual-e5-large, is a multilingual model, it can still be used to classify text in all the languages it supports, but it may not be as accurate when doing so.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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.
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, 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
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
9
Maximum number of training epochs.
BATCH_SIZE
32
Batch size per device.
LEARNING_RATE
2.48992637448615e-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)
Evaluation
Testing Data, Factors & Metrics
Testing Data
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, ensuring that the model's performance is evaluated on data that reflects the diversity and complexity of real-world scenarios.
Metrics
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
0.83
Balanced Accuracy
0.73
Recall (macro)
0.68
F1-score (macro)
0.66
H-F1-score
0.86
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.
