Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- es
+pipeline_tag: text-classification
+tags:
+- sentence-transformers
+- text-classification
+- bert
+- biomedical
+- lexical semantics
+- bionlp
+---
+
+# Biomedical term classifier with SetFit in Spanish
+
+## 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)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Additional information](#additional-information)
+  - [Author](#author)
+  - [Licensing information](#licensing-information)
+  - [Citation information](#citation-information)
+  - [Disclaimer](#disclaimer)
+
+</details>
+
+## Model description
+This is a Transformer's [AutoModelForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSequenceClassification) trained for multilabel biomedical text classification in Spanish. 
+
+## Intended uses and limitations
+The model is prepared to classify medical entities among 21 classes, including diseases, medical procedures, symptoms, and drugs, among others. It still lacks some classes like body structures.
+
+## How to use
+This model is implemented as part of the KeyCARE library. Install first the keycare module to call the SetFit classifier:
+
+```bash
+python -m pip install keycare
+```
+
+You can then run the KeyCARE pipeline that uses the SetFit model:
+
+```python
+from keycare install TermExtractor.TermExtractor
+
+# initialize the termextractor object
+termextractor = TermExtractor(categorization_method='transformers')
+# Run the pipeline
+text = """Acude al Servicio de Urgencias por cefalea frontoparietal derecha.
+Mediante biopsia se diagnostica adenocarcinoma de próstata Gleason 4+4=8 con metástasis óseas múltiples.
+Se trata con Ácido Zoledrónico 4 mg iv/4 semanas.
+"""
+termextractor(text)
+# You can also access the class storing the SetFit model
+categorizer = termextractor.categorizer
+```
+
+## Training
+The model has been trained using data obtained from NER Gold Standard Corpora also generated by BSC-NLP4BIA, including [MedProcNER](https://temu.bsc.es/medprocner/), [DISTEMIST](https://temu.bsc.es/distemist/), [SympTEMIST](https://temu.bsc.es/symptemist/), [CANTEMIST](https://temu.bsc.es/cantemist/), and [PharmaCoNER](https://temu.bsc.es/pharmaconer/), among others.
+
+## Evaluation
+To be published
+
+## Additional information
+
+### Author
+NLP4BIA at the Barcelona Supercomputing Center
+
+### Licensing information
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Citation information
+To be published
+
+### Disclaimer
+<details>
+<summary>Click to expand</summary>
+
+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.
+
+When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+</details>