Update README.md

README.md

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 # Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the Cantemist dataset.
+
+## Table of contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model description](#model-description)
+- [Intended uses and limitations](#intended-use)
+- [How to use](#how-to-use)
+- [Limitations and bias](#limitations-and-bias)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Additional information](#additional-information)
+  - [Author](#author)
+  - [Contact information](#contact-information)
+  - [Copyright](#copyright)
+  - [Licensing information](#licensing-information)
+  - [Funding](#funding)
+  - [Citing information](#citing-information)
+  - [Disclaimer](#disclaimer)
+  
+</details>
+
+## Model description
 A fine-tuned version of the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish biomedical corpus known to date, composed of biomedical documents, clinical cases and EHR documents for a total of 1.1B tokens of clean and deduplicated text processed.
 
 For more details about the corpora and training, check the _bsc-bio-ehr-es_ model card.
 
-## Dataset
+## Intended uses and limitations
+
+## How to use
+
+## Limitations and bias
+At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. 
+
+## Training
 The dataset used is [CANTEMIST](https://huggingface.co/datasets/PlanTL-GOB-ES/cantemist-ner), a NER dataset annotated with tumor morphology entities. For further information, check the [official website](https://temu.bsc.es/cantemist/).
 
-## Evaluation and results
+## Evaluation 
 F1 Score: 0.8340
 
 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
 
-## Citing 
+## Additional information
+
+### Author
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
+
+### Contact information
+For further information, send an email to <[email protected]>
+
+### Copyright
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Licensing information
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Funding
+This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
+
+### Citing information
 If you use these models, please cite our work:
 
 ```bibtext
@@ -72,19 +119,7 @@ If you use these models, please cite our work:
 }
 ```
 
-## Copyright
-
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
-
-## Licensing information
-
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
-
-## Funding
-
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
-
-## Disclaimer
+### Disclaimer
 
 The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.