Attach punctuation in examples

README.md

@@ -16,20 +16,20 @@ metrics:
 - f1
 widget:
 - text: Coexpression of HMG I/Y and Oct-2 in cell lines lacking Oct-2 results in high
-    levels of HLA-DRA gene expression , and in vitro DNA-binding studies reveal that
-    HMG I/Y stimulates Oct-2A binding to the HLA-DRA promoter .
+    levels of HLA-DRA gene expression, and in vitro DNA-binding studies reveal that
+    HMG I/Y stimulates Oct-2A binding to the HLA-DRA promoter.
 - text: In erythroid cells most of the transcription activity was contained in a 150
-    bp promoter fragment with binding sites for transcription factors AP2 , Sp1 and
-    the erythroid-specific GATA-1 .
+    bp promoter fragment with binding sites for transcription factors AP2, Sp1 and
+    the erythroid-specific GATA-1.
 - text: 'Synergy between signal transduction pathways is obligatory for expression
-    of c-fos in B and T cell lines : implication for c-fos control via surface immunoglobulin
-    and T cell antigen receptors .'
-- text: CIITA mRNA is normally inducible by IFN-gamma in class II non-inducible ,
-    RB-defective lines , and in one line , re-expression of RB has no effect on CIITA
-    mRNA induction levels .
-- text: As we reported previously , MNDA mRNA level in adherent monocytes is elevated
-    by IFN-alpha ; in this study , we further assessed MNDA expression in in vitro
-    monocyte-derived macrophages .
+    of c-fos in B and T cell lines: implication for c-fos control via surface immunoglobulin
+    and T cell antigen receptors.'
+- text: CIITA mRNA is normally inducible by IFN-gamma in class II non-inducible,
+    RB-defective lines, and in one line, re-expression of RB has no effect on CIITA
+    mRNA induction levels.
+- text: As we reported previously, MNDA mRNA level in adherent monocytes is elevated
+    by IFN-alpha; in this study, we further assessed MNDA expression in in vitro
+    monocyte-derived macrophages.
 pipeline_tag: token-classification
 co2_eq_emissions:
   emissions: 45.104
@@ -114,7 +114,7 @@ from span_marker import SpanMarkerModel
 # Download from the 🤗 Hub
 model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-bionlp")
 # Run inference
-entities = model.predict("In erythroid cells most of the transcription activity was contained in a 150 bp promoter fragment with binding sites for transcription factors AP2 , Sp1 and the erythroid-specific GATA-1 .")
+entities = model.predict("In erythroid cells most of the transcription activity was contained in a 150 bp promoter fragment with binding sites for transcription factors AP2, Sp1 and the erythroid-specific GATA-1.")
 ```
 
 ### Downstream Use