Create README.md

README.md

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+---
+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 for Longformer-es-m-base-ICE-MR23-ED
+
+## 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)