Datasets:

EMBO
/

sd-nlp-non-tokenized

@@ -33,7 +33,7 @@ task_ids:
 ---
 # Dataset Card for sd-nlp
 ## Table of Contents
-- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
@@ -61,16 +61,19 @@ task_ids:
     - [Licensing Information](#licensing-information)
     - [Citation Information](#citation-information)
     - [Contributions](#contributions)
 ## Dataset Description
 - **Homepage:** https://sourcedata.embo.org
 - **Repository:** https://github.com/source-data/soda-roberta
 - **Paper:**
 - **Leaderboard:**
 - **Point of Contact:** [email protected], [email protected]
 ### Dataset Summary
 This dataset is based on the content of the SourceData (https://sourcedata.embo.org) database, which contains manually annotated figure legends written in English and extracted from scientific papers in the domain of cell and molecular biology (Liechti et al, Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471).
 Unlike the dataset [`sd-nlp`](https://huggingface.co/datasets/EMBO/sd-nlp), pre-tokenized with the `roberta-base` tokenizer, this dataset is not previously tokenized, but just splitted into words. Users can therefore use it to fine-tune other models.
 Additional details at https://github.com/source-data/soda-roberta
 ### Supported Tasks and Leaderboards
 Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)).
 `PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.
@@ -86,10 +89,14 @@ Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%8
 - `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.
 - `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements.
 `BORING`: entities are marked with the tag `BORING` when they are more of descriptive value and not directly associated with causal hypotheses ('boring' is not an ideal choice of word, but it is short...). Typically, these entities are so-called 'reporter' geneproducts, entities used as common baseline across samples, or specify the context of the experiment (cellular system, species, etc...).
 ### Languages
 The text in the dataset is English.
 ## Dataset Structure
 ### Data Instances
 ```json
 {
     "words": [

 ---
 # Dataset Card for sd-nlp
 ## Table of Contents
+- [Dataset Card for [EMBO/sd-nlp-non-tokenized]](#dataset-card-for-dataset-name)
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
     - [Licensing Information](#licensing-information)
     - [Citation Information](#citation-information)
     - [Contributions](#contributions)
 ## Dataset Description
 - **Homepage:** https://sourcedata.embo.org
 - **Repository:** https://github.com/source-data/soda-roberta
 - **Paper:**
 - **Leaderboard:**
 - **Point of Contact:** [email protected], [email protected]
 ### Dataset Summary
 This dataset is based on the content of the SourceData (https://sourcedata.embo.org) database, which contains manually annotated figure legends written in English and extracted from scientific papers in the domain of cell and molecular biology (Liechti et al, Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471).
 Unlike the dataset [`sd-nlp`](https://huggingface.co/datasets/EMBO/sd-nlp), pre-tokenized with the `roberta-base` tokenizer, this dataset is not previously tokenized, but just splitted into words. Users can therefore use it to fine-tune other models.
 Additional details at https://github.com/source-data/soda-roberta
 ### Supported Tasks and Leaderboards
 Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)).
 `PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.
 - `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.
 - `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements.
 `BORING`: entities are marked with the tag `BORING` when they are more of descriptive value and not directly associated with causal hypotheses ('boring' is not an ideal choice of word, but it is short...). Typically, these entities are so-called 'reporter' geneproducts, entities used as common baseline across samples, or specify the context of the experiment (cellular system, species, etc...).
 ### Languages
 The text in the dataset is English.
 ## Dataset Structure
 ### Data Instances
 ```json
 {
     "words": [