typos

README.md

@@ -15,14 +15,14 @@ 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-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset wit the `NER` task to perform Named Entity Recognition 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 was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `NER` configuration to perform Named Entity Recognition of bioentities. 
 
 
 ## Intended uses & limitations
 
 #### How to use
 
-The intended use of this model is for Named Entity Recognition of biological entitie used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
+The intended use of this model is for Named Entity Recognition of biological entities used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
 
 To have a quick check of the model:
 
@@ -51,8 +51,10 @@ The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
 
 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-nlp NER
+- Training data: EMBO/sd-nlp
+- Dataset configuration: NER
 - Training with 48771 examples.
 - Evaluating on 13801 examples.
 - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY