Update README.md

README.md

@@ -34,21 +34,15 @@ widget:
   - [Licensing Information](#licensing-information)
   - [Funding](#funding)
   - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
   - [Disclaimer](#disclaimer)
   
 </details>
 
 ## Model description
-
-Biomedical pretrained language model for Spanish.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. 
-
+Biomedical pretrained language model for Spanish. 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. 
 
 ## 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.
+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
 
@@ -102,11 +96,9 @@ unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
 ```
 
 ## Limitations and bias
-
-[N/A]
+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 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.
 
@@ -137,8 +129,7 @@ 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 evaluated on the Named Entity Recognition (NER) using the following 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/).
@@ -160,23 +151,18 @@ The evaluation results are compared against the [mBERT](https://huggingface.co/b
 ## Additional information
 
 ### Author
-
 Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
 
 ### Contact Information
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 For further information, send an email to <plantl-gob-es@bsc.es>
 
 ### Copyright
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 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.
 
 ### Citation Information
@@ -210,11 +196,6 @@ If you use our Medical Crawler corpus, please cite the preprint:
 
 ```
 
-### Contributions
-
-[N/A]
-
-
 ### Disclaimer
 
 <details>