Update README.md

README.md

@@ -16,16 +16,51 @@ widget:
 ---
 
 # Biomedical-clinical language model for 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)
+- [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
 Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
 
-## Tokenization and model pretraining
+
+## Intended uses and limitations
+The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
+
+
+## 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
+
+### Tokenization and model pretraining
 This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
 **biomedical-clinical** corpus in Spanish collected from several sources (see next section). 
 The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
 used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.
 
-## Training corpora and preprocessing
-
+### Training corpora and preprocessing
 The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are:
 
 - data parsing in different formats
@@ -53,10 +88,7 @@ Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus
 | PubMed                                                                                  | 1,858,966   | Open-access articles from the PubMed repository crawled in 2017.                                                                                                                                              |
 
 
-
-## Evaluation and results
-
-
+## Evaluation 
 The model has been fine-tuned on three Named Entity Recognition (NER) tasks using three clinical NER datasets:
 
  - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
@@ -76,13 +108,26 @@ The table below shows the F1 scores obtained:
 
 
 The fine-tuning scripts can be found in the official GitHub [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
-## Intended uses & limitations
 
-The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
 
-However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
+## Additional information
+
+### Author
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
+
+### Contact information
+For further information, send an email to <plantl-gob-es@bsc.es>
+
+### 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.
 
-## Cite
+### Citing information
 If you use these models, please cite our work:
 
 ```bibtext
@@ -109,31 +154,21 @@ 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
+<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.
+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.
 
-In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
+In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
 
 
 Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
 
 Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
 
-En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+</details>