reverting to dataset sd-nlp

README.md

@@ -7,7 +7,7 @@ tags:
 - 
 license: agpl-3.0
 datasets:
-- EMBO/sd-figures
+- EMBO/sd-nlp
 metrics:
 -
 ---
@@ -16,7 +16,7 @@ metrics:
 
 ## Model description
 
-This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-figures](https://huggingface.co/datasets/EMBO/sd-figures) dataset with the `PANELIZATION` task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels.
+This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `PANELIZATION` task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels.
 
 Figures are usually composite representations of results obtained with heterogeneous experimental approaches and systems. Breaking figures into panels allows identifying more coherent descriptions of individual scientific experiments.
 
@@ -44,7 +44,7 @@ The model must be used with the `roberta-base` tokenizer.
 
 ## Training data
 
-The model was trained for token classification using the [`EMBO/sd-figures PANELIZATION`](https://huggingface.co/datasets/EMBO/sd-figures) dataset which includes manually annotated examples.
+The model was trained for token classification using the [`EMBO/sd-nlp PANELIZATION`](https://huggingface.co/datasets/EMBO/sd-nlp) dataset which includes manually annotated examples.
 
 ## Training procedure
 
@@ -54,7 +54,7 @@ Training code is available at https://github.com/source-data/soda-roberta
 
 - Model fine-tuned: EMBO/bio-lm
 - Tokenizer vocab size: 50265
-- Training data: EMBO/sd-figures
+- Training data: EMBO/sd-nlp
 - Dataset configuration: PANELIZATION
 - TTraining with 2175 examples.                                  
 - Evaluating on 622 examples.