Update README.md

README.md

@@ -1,10 +1,10 @@
 ---
-license: 
+license:
 - mit
 language:
 - en
 base_model:
-- google-bert/bert-base-uncased
+- Nucha/Nucha_SkillNER_BERT
 tags:
 - Skills
 - NER
@@ -12,12 +12,13 @@ tags:
 - BERT
 pipeline_tag: token-classification
 model-index:
-  - name: Nucha/Nucha_SkillNER_BERT
-    results: []
+- name: Nucha/Nucha_SkillNER_BERT
+  results: []
 widget:
-  - text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy."
-    example_title: "Sentiment analysis"
-
+- text: >-
+    Is this review positive or negative? Review: Best cast iron skillet you will
+    ever buy.
+  example_title: Sentiment analysis
 ---
 
 # Computing Skill NER
@@ -126,5 +127,4 @@ You can employ this model using the Transformers library's *pipeline* for NER, o
 ```
 
 ## Conclusion
-The **Nucha/Nucha_SkillNER_BERT** model demonstrates strong performance in identifying skills in text data, making it a valuable tool for applications in recruitment, resume screening, and skill extraction tasks. Continuous improvements and further evaluations will enhance its accuracy and adaptability to specific use cases.
-
+The **Nucha/Nucha_SkillNER_BERT** model demonstrates strong performance in identifying skills in text data, making it a valuable tool for applications in recruitment, resume screening, and skill extraction tasks. Continuous improvements and further evaluations will enhance its accuracy and adaptability to specific use cases.