updated results

README.md

@@ -63,7 +63,20 @@ def predict_scibert_labels(sentence):
 
     return list(zip(final_tokens, final_labels))
 ```
-
+output:
+```
+CRISPR          -> B-Scns
+-               -> I-Scns
+Cas9            -> I-Scns
+is              -> O
+a               -> O
+powerful        -> O
+tool            -> O
+for             -> O
+genome          -> B-Scns
+editing         -> O
+.               -> O
+```
 # Model Card for Model ID
 
 This model is a fine-tuned version of `allenai/scibert_scivocab_uncased` for scientific terms/phrases detection in text. It is trained on a custom dataset [JonyC/ScienceGlossary-NER_fit](https://huggingface.co/JonyC/ScienceGlossary-NER_fit) for Named Entity Recognition (NER), aiming to identify scientific terms in a variety of academic and technical texts.
@@ -96,11 +109,13 @@ result = pipe(sentence)
 result
 ```
 results:
+```
 [{'entity': 'B-Scns', 'score': 0.9897461, 'index': 1, 'word': 'crispr', 'start': 0, 'end': 6},
  {'entity': 'I-Scns', 'score': 0.9474513, 'index': 2, 'word': '-', 'start': 6, 'end': 7},
  {'entity': 'I-Scns', 'score': 0.97595257, 'index': 3, 'word': 'cas', 'start': 7, 'end': 10},
  {'entity': 'I-Scns', 'score': 0.9894609, 'index': 4, 'word': '##9', 'start': 10, 'end': 11},
  {'entity': 'B-Scns', 'score': 0.999246, 'index': 10, 'word': 'genome', 'start': 35, 'end': 41}]
+```
 
 ### Out-of-Scope Use