update model card

README.md

@@ -15,7 +15,7 @@ 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 has then been fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities.
+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 has then been fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities.
 
 
 ## Intended uses & limitations
@@ -30,7 +30,7 @@ To have a quick check of the model:
 from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
 example = """<s>The <mask> overexpression in cells caused an increase in <mask> expression.</s>"""
 tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
-model = RobertaForTokenClassification.from_pretrained('EMBO/sd-roles')
+model = RobertaForTokenClassification.from_pretrained('EMBO/sd-smallmol-roles')
 ner = pipeline('ner', model, tokenizer=tokenizer)
 res = ner(example)
 for r in res:
@@ -43,7 +43,7 @@ The model must be used with the `roberta-base` tokenizer.
 
 ## Training data
 
-The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-panels) which includes manually annotated examples.
+The model was trained for token classification using the [EMBO/sd-panels dataset](https://huggingface.co/datasets/EMBO/sd-panels) which includes manually annotated examples.
 
 ## Training procedure
 
@@ -53,7 +53,7 @@ Training code is available at https://github.com/source-data/soda-roberta
 
 - Model fine tuned: EMBL/bio-lm
 - Tokenizer vocab size: 50265
-- Training data: EMBO/sd-nlp 
+- Training data: EMBO/sd-panels
 - Dataset configuration: SMALL_MOL_ROLES
 - Training with 48771 examples.
 - Evaluating on 13801 examples.