Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+datasets:
+- IEETA/SPACCC-Spanish-NER
+language:
+- es
+metrics:
+- f1
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+Our model focuses on Biomedical Named Entity Recognition (NER) in Spanish clinical texts, crucial for automated information extraction in medical research and treatment improvements. 
+It proposes a novel approach using a Multi-Head Conditional Random Field (CRF) classifier to tackle multi-class NER tasks, overcoming challenges of overlapping entity instances. 
+Classes: symptoms, procedures, diseases, chemicals, and proteins
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+- **Developed by:** IEETA
+- **Shared by [optional]:** IEETA
+- **Model type:** Multi-Head-CRF, Roberta Base
+- **Language(s) (NLP):** Spanish
+- **License:** MIT
+- **Finetuned from model [optional]:** lcampillos/roberta-es-clinical-trials-ner
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/ieeta-pt/Multi-Head-CR
+- **Paper:** [More Information Needed]
+
+## Uses
+
+Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only. It performs Named Entity Recognition over 5 classes namely: SYMPTOM PROCEDURE DISEASE PROTEIN CHEMICAL
+
+## How to Get Started with the Model
+
+Please refer to our GitHub repository for more information on how to train the model and run inference.  https://github.com/ieeta-pt/Multi-Head-CRF
+
+## Training Details
+
+### Training Data
+
+The training data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card.
+
+[More Information Needed]
+
+
+
+
+### Speeds, Sizes, Times [optional]
+
+The models were trained using an Nvidia Quadra RTX 8000. The models for 5 classes took approximately 1 hour to train and occupies around 1gb of disk space. Further this model shows linear complexity (+8 minutes) per entity class to classify.
+
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+The testing data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card.
+
+
+#### Metrics
+
+The models were evaluated using the F1 score metric, the standard for entity recognition tasks.
+
+### Results
+
+We provide 4 seperate models with various hyperparmeter changes:
+
+| HLs per head | Augmentation | Percentage Tags | Augmentation Probability | F1     |
+|--------------|--------------|-----------------|--------------------------|--------|
+| 3            | Random       | 0.25            | 0.50                     | 78.73  |
+| 3            | Unknown      | 0.50            | 0.25                     | 78.50  |
+| 3            | None         | -               | -                        | **78.89** |
+| 1            | Random       | 0.25            | 0.50                     | **78.89** |
+
+All models are trained with a context size of 32 for 60 epochs.
+
+#### Summary
+
+
+## Citation [optional]
+
+
+**BibTeX:**
+
+[More Information Needed]
+
+
+
+
+