Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- spanish
+- mental-health
+- longformer
+- disorder-detection
+- mentalrisk2024
+- early-detection
+- ice
+- nlp
+language:
+- es
+base_model:
+- ELiRF/Longformer-es-mental-base
+---
+# Model Card
+
+## Model Description
+
+Longformer-es-m-base-ICE-MR24-DD is a Spanish long-context language model for **mental disorder detection**, trained using the **Incremental Context Expansion (ICE)** methodology.
+The model was developed for the **MentalRisk 2024 Disorder Detection (MR24-DD)** shared task and builds upon the **Longformer-es-mental-base** foundation model.
+
+This model corresponds to the **base-sized version** of the ICE-adapted Longformer models.
+Compared to its large counterpart, it contains **fewer parameters**, providing a more computationally efficient alternative while preserving the ability to process long user message histories.
+
+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 training strategy allows the model to better capture early and partial signals of mental health disorders, while remaining effective in **full-context (user-level)** evaluation settings.
+
+The model is based on the Longformer architecture and supports input sequences of up to **4096 tokens**, making it suitable for disorder detection scenarios where evidence is distributed across long and heterogeneous user histories.
+
+- 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 mental health disorder detection.
+
+### Direct Use
+
+The model can be used directly for **mental disorder detection** from Spanish user-generated content, producing predictions from incrementally available user message histories.
+
+### Downstream Use
+
+- Mental disorder detection (user-level classification)  
+- Early and incremental mental health screening  
+- Comparative studies on disorder detection methodologies  
+- Research on temporally-aware and incremental NLP models  
+
+### Out-of-Scope Use
+
+- Automated intervention systems without human supervision  
+- Use on languages other than Spanish  
+- High-stakes decision-making affecting individuals’ health or safety  
+
+## ICE Methodology
+
+Incremental Context Expansion (ICE) is a training methodology designed for early and incremental mental health detection tasks.
+Instead of training models on full user histories, ICE generates **multiple incremental contexts per user**, each corresponding to a partial message history.
+
+This approach enables the model to:
+- Learn from incomplete and early evidence  
+- Improve robustness under early and incremental evaluation metrics  
+- Better align training conditions with real-world deployment scenarios  
+
+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, cultural, and linguistic biases.  
+- Automatically translated texts may introduce translation artifacts or subtle semantic shifts.  
+- Disorder detection is inherently subjective and context-dependent.  
+- 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-MR24-DD")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "ELiRF/Longformer-es-m-base-ICE-MR24-DD"
+)
+
+inputs = tokenizer(
+    "Ejemplo de historial de mensajes relacionado con salud mental.",
+    return_tensors="pt",
+    truncation=True,
+    max_length=4096
+)
+
+outputs = model(**inputs)
+```
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on the **MentalRisk 2024 Disorder Detection (MR24-DD)** 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 2024 Disorder Detection task, **Longformer-es-m-base-ICE-MR24-DD** shows **competitive performance** under both **full-context (user-level)** and **early / incremental detection** evaluation settings, 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)