---
library_name: transformers
tags:
- spanish
- mental-health
- longformer
- early-detection
- eating-disorder
- ice
- nlp
language:
- es
base_model:
- ELiRF/Longformer-es-mental-base
---
# Model Card

## Model Description

Longformer-es-m-base-ICE-MR23-ED is a Spanish long-context language model for **early detection of eating disorder risk**, trained using the **Incremental Context Expansion (ICE)** methodology.
This model builds upon the **Longformer-es-mental-base** foundation model and represents the **base-sized version** of the ICE-adapted Longformer models for the Eating Disorder task.

Compared to its large counterpart, this version contains **fewer parameters**, offering a more computationally efficient alternative while preserving the ability to process long user message histories.
The model is designed for scenarios where mental health–related evidence is distributed across multiple messages and predictions must be generated incrementally.

The ICE methodology restructures the training data at the **context level**, enabling the model to learn from progressively expanding user message histories rather than complete user timelines.
This setup better reflects real-world early detection conditions.

The model is based on the Longformer architecture and supports input sequences of up to **4096 tokens**.
It has been fine-tuned for the **Eating Disorder (ED)** task using the **MentalRisk 2023 (MR23)** benchmark under early detection evaluation settings.

- Developed by: ELiRF group, VRAIN (Valencian Research Institute for Artificial Intelligence), Universitat Politècnica de València
- Shared by: ELiRF
- Model type: Transformer-based sequence classification model (Longformer)
- Language: Spanish
- Base model: Longformer-es-mental-base
- License: Same as base model

## Uses

This model is intended for **research purposes** in early mental health risk detection.

### Direct Use

The model can be used directly for **early detection of eating disorder risk** from Spanish user-generated content, where predictions are generated incrementally as new user messages become available.

### Downstream Use

- Early risk detection for eating disorders
- User-level mental health screening
- Comparative studies of early detection methodologies
- Research on incremental and temporally-aware NLP models

### Out-of-Scope Use

- Automated intervention systems without human supervision
- Use on languages other than Spanish
- High-stakes or real-time decision-making affecting individuals’ health

## ICE Methodology

Incremental Context Expansion (ICE) is a training methodology designed for early detection tasks.
Instead of training on full user histories, ICE generates **multiple incremental contexts per user**, each corresponding to a partial message history.

This approach allows the model to:
- Learn from early and incomplete evidence
- Reduce detection latency
- Improve robustness under early detection evaluation metrics

ICE modifies the dataset construction process while keeping the standard fine-tuning pipeline unchanged.

## Bias, Risks, and Limitations

- Training data originates from social media platforms and may contain demographic and cultural biases.
- Automatically translated texts may include translation artifacts.
- Early detection tasks are inherently uncertain due to limited available evidence.
- The model does not provide explanations or clinical interpretations of its predictions.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ELiRF/Longformer-es-m-base-ICE-MR23-ED")
model = AutoModelForSequenceClassification.from_pretrained(
    "ELiRF/Longformer-es-m-base-ICE-MR23-ED"
)

inputs = tokenizer(
    "Ejemplo de historial de mensajes relacionado con trastornos alimentarios.",
    return_tensors="pt",
    truncation=True,
    max_length=4096
)

outputs = model(**inputs)
```

## Training Details

### Training Data

The model was fine-tuned on the **MentalRisk 2023 Eating Disorder (MR23-ED)** dataset.
Training data was restructured using the **ICE methodology**, generating incremental user contexts from original user timelines.

### Training Procedure

- Base model: Longformer-es-mental-base
- Fine-tuning strategy: ICE-based context-level training
- Objective: Sequence classification
- Training regime: fp16 mixed precision

## Evaluation

### Results

When evaluated on the MentalRisk 2023 Eating Disorder task, Longformer-es-m-base-ICE-MR23-ED shows **competitive performance** under **early detection** evaluation settings and strong results in **full-context (user-level)** scenarios, offering a favorable trade-off between performance and computational efficiency.

## Environmental Impact

- Hardware type: NVIDIA A40 GPUs
- Training time: several hours (fine-tuning)

## Technical Specifications

### Model Architecture and Objective

- Architecture: Longformer (base)
- Objective: Sequence classification
- Maximum sequence length: 4096 tokens
- Model size: approximately 150M parameters

## Citation

This model is part of an ongoing research project.
The associated paper is currently under review and will be added to this model card once the publication process is completed.


## Model Card Authors

ELiRF research group (VRAIN, Universitat Politècnica de València)