Update readme with results and biomedBERT name

README.md

@@ -98,8 +98,7 @@ This dataset was created to identity oligogenic variant combinations, i.e. relat
 
 The dataset can be used to train a model for ``text-classification`` (as the relation extraction task is here considered as a classification task). Success on this task is typically measured by achieving a high F1-score.
 
-*TO DO*
-*The (model name or model class) model currently achieves the following score.*
+The BioLinkBERT model (https://huggingface.co/michiyasunaga/BioLinkBERT-large) currently achieves the following score of 0.8207 F1-score, with a precision of 0.7941 and a recall of 0.8491.
 
 ### Languages
 
@@ -108,7 +107,8 @@ The dataset consists in text extracted from scientific articles written in engli
 ## Dataset Structure
 
 ### Data Instances
-Each instance describes the two genes and two variants composing the potential digenic variant combination, as well as the text with the masked entities, the PubMed Central identifier of the article and the label of the instance (i.e., if it is a digenic variant combination or not, respectively 1 and 0).
+
+Each instance describes the two genes and two variants composing the potential digenic variant combination, as well as the fragment of text with the masked entities, the PubMed Central identifier of the article and the label of the instance (i.e., if the fragment of text contains a valid digenic variant combination or not, respectively 1 and 0).
 
 ```json
 {
@@ -124,7 +124,7 @@ Each instance describes the two genes and two variants composing the potential d
 
 ### Data Fields
 
-- `sentence`: *string*, text containing the entities masked with either @GENE$ for the gene type or @VARIANT$ for the mutation type. The text can be either single or cross-sentence, but no longer than 256 tokens according to the PubMedBERT tokenizer (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)).  
+- `sentence`: *string*, text containing the entities masked with either @GENE$ for the gene type or @VARIANT$ for the mutation type. The text can be either single or cross-sentence, but no longer than 256 tokens according to the BiomedBERT tokenizer (see [BiomedBERT](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)).  
 - `pmcid`: *int*, PubMed Central identifier of the article from which the text was extracted (https://www.ncbi.nlm.nih.gov/pmc/)
 - `gene1`: *string*, first gene mention as it appears in the text and internal identifier.
 - `gene2`: *string*, second gene mention as it appears in the text and internal identifier.
@@ -154,9 +154,9 @@ The curation of oligogenic variant combinations requires high expertise and time
 
 #### Initial Data Collection and Normalization
 
-Scientific articles containing oligogenic variant combinations potentially causing genetic diseases were retrieved from [OLIDA](https://olida.ibsquare.be), the OLIgogenic diseases DAtabase. Articles were filtered to keep only those containing at least one digenic variant combination, i.e. combination between two genes and at least one variant in each gene. The articles were then pre-annotated with the help of PubTator API (https://www.ncbi.nlm.nih.gov/research/pubtator/api.html) to obtain the full-text articles with the genes and variants identified.
+Scientific articles containing oligogenic variant combinations potentially causing genetic diseases were retrieved from [OLIDA](https://olida.ibsquare.be), the OLIgogenic diseases DAtabase. Articles were filtered to keep only those containing at least one digenic variant combination, i.e. combination between two genes and at least one variant in each gene. The articles were then pre-annotated with the help of PubTator API (https://www.ncbi.nlm.nih.gov/research/pubtator/api.html) to obtain the full text of the articles with the genes and variants identified.
 
-Candidates were created by extracting all the text portion (both single and cross-sentence) containing two gene and two variant mentions with a maximum length of 256 tokens, as tokenized by the PubMedBERT tokenizer (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). Text containing tables or incomplete sentences were excluded during the annotation process. 
+Fragment of texts to annotate were created by extracting all the text (both single and cross-sentence) containing two different gene and two different variant mentions with a maximum length of 256 tokens, as tokenized by the BiomedBERT tokenizer (see [BiomedBERT](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)). Text containing tables or incomplete sentences were excluded during the annotation process.
 
 #### Who are the source language producers?
 
@@ -164,18 +164,21 @@ The dataset is machine-generated, as the full annotated text of the article is r
 
 ### Annotations
 
-The annotation was done with the ALAMBIC platform, with an Active Learning (AL) setting (see [Nachtegael 2023](https://aclanthology.org/2023.eacl-demo.14)). 
+The annotation was done with the ALAMBIC platform, with an Active Learning (AL) setting (see [Nachtegael 2023](https://aclanthology.org/2023.eacl-demo.14)).
 
 #### Annotation process
 
-1500 samples were randomly selected to be labelled, with 1000 samples for the test set and 500 as seed for the AL process. 9 iterations of AL selection of 500 samples with the Margin Sampling strategy was conducted with PubMEdBERT as the model used for the selection (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). The annotation limit was initially set at 6000 samples, but was exceeded due to several restarts of the process due to technical errors.
+1500 samples were randomly selected to be labelled, with 1000 samples for the test set and 500 as seed for the AL process. 9 iterations of AL selection of 500 samples with the Margin Sampling strategy was conducted with BiomedBERT as the model used for the selection (see [BiomedBERT](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)), samples subsequently annotated. The annotation limit was initially set at 6000 samples, but was exceeded due to several restarts of the process due to exclusion of invalid instances.
+
+The annotator had access to the genes and variants, the PMCID of the article the text was extracted from and the text with the masked entities. One out of three possible classes is given to each fragment of text :
 
-The annotator had access to the genes and variants, the PMCID of the article the text was extracted from and the text with the masked entities. One out of three possible classes is given to each variant combination candidate :
 - *0* for the absence of a digenic variant combination relation in the text.
-- *1* for the presence of a digenic variant combination relation. The genes and the variants need to be relating to each other for there to be a valid relation. If the entities are involved in an alleged digenic relation according to OLIDA, but the syntactic aspects of the text showed no clear relation between the entities, then the text contains no relation. The combination needs to be carried by at least one individual, as depicted in the text.
-- *-1* if the candidate is not valid. A candidate can be deemed as invalid if one of the entities is not a valid entity, i.e. not a valid gene name or mutation, or the text contains an unfinished sentence or invalid sentence, i.e. with part of the text being a table.
+- *1* for the presence of a digenic variant combination relation. The genes and the variants need to be relating to each other for there to be a valid relation. If the entities are involved in an alleged digenic relation according to OLIDA, but the syntactic aspects of the text showed no clear relation between the entities, then the text contains no relation. The combination needs to be carried by at least one individual.
+- *-1* if the fragment of text is not valid. The text can be deemed as invalid if one of the entities is not a valid entity, i.e. not a valid gene name or mutation, or the text contains an unfinished sentence or invalid sentence, i.e. with part of the text being a table. Invalid gene name and mutation comprised : (a) error in the annotation, e.g. P05, a patient denomination, which was annotated as a gene name or the cell line HEK293 which was annotated as variant; (b) genes in species not human; (c) Isoforms denominations of proteins and (d) gene products. Tables were excluded as it is not considered as comprehensive text without the notion of their structure. To be used, they would need to be parsed in order to convey this structure, which is not rendered in free text.
+
+Only instances from the positive and the negative classes (labels of *0* and *1*) are included in the final data set, all the invalid instances are excluded from further use as they do not fill our quality standards.
 
-It must be noted that while the articles were filtered for those containing digenic variant combinations, it is possible to also find oligogenic variant combinations involving more than two genes and/or two variants. In that case, a subset of those variant combinations, i.e. two gene-variant pairs which are connected in the text and are part of the variant combination, were considered as a valid digenic variant combinations and classified them as class *1*. 
+It must be noted that while the articles were filtered for those containing digenic variant combinations, it is possible to also find oligogenic variant combinations involving more than two genes and/or two variants. In that case, a subset of those variant combinations, i.e. two gene-variant pairs which are connected in the text and are part of the variant combination, were considered as a valid digenic variant combinations and classified them as class *1*.
 
 #### Who are the annotators?
 
@@ -228,4 +231,4 @@ TBA
 ### Contributions
 
 Thanks to Barbara Gravel and Sofia Papadimitriou for their initial work with OLIDA.
-Thanks to Jacopo de Stefani, Anthony Cnudde and Tom Lenaerts for their help with the experimental design and writing of the paper for DUVEL.
+Thanks to Jacopo De Stefani, Anthony Cnudde and Tom Lenaerts for their help with the experimental design and writing of the paper for DUVEL.