Update README.md

README.md

@@ -40,9 +40,9 @@ This model has been trained to perform Named Entity Recognition (NER) and is bas
 
 ## Training and evaluation data
 - **Training Dataset**: CoNLL-2003
+
 - **Training Evaluation Metrics**:
-  
--               precision    recall  f1-score   support
+                precision    recall  f1-score   support
 
        B-PER       0.98      0.98      0.98     11273
        I-PER       0.98      0.99      0.99      9323
@@ -58,7 +58,7 @@ This model has been trained to perform Named Entity Recognition (NER) and is bas
 weighted avg       0.90      0.90      0.89     53190
 
 - **Validation Evaluation Metrics**:
--               precision    recall  f1-score   support
+                precision    recall  f1-score   support
 
        B-PER       0.97      0.98      0.97      3018
        I-PER       0.98      0.98      0.98      2741
@@ -74,7 +74,7 @@ weighted avg       0.90      0.90      0.89     53190
 weighted avg       0.90      0.89      0.88     13235
 
 - **Test Evaluation Metrics**:
--               precision    recall  f1-score   support
+                precision    recall  f1-score   support
 
        B-PER       0.96      0.95      0.96      2714
        I-PER       0.98      0.99      0.98      2487
@@ -142,6 +142,7 @@ tokenizer = AutoTokenizer.from_pretrained("huseyincenik/conll_ner_with_bert")
 model = AutoModelForTokenClassification.from_pretrained("huseyincenik/conll_ner_with_bert")
 
 ```
+
 Abbreviation|Description
 -|-
 O|Outside of a named entity
@@ -157,12 +158,14 @@ I-LOC |Location
 
 ### CoNLL-2003 English Dataset Statistics
 This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. 
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 #### # of training examples per entity type
 Dataset|LOC|MISC|ORG|PER
 -|-|-|-|-
 Train|7140|3438|6321|6600
 Dev|1837|922|1341|1842
 Test|1668|702|1661|1617
+
 #### # of articles/sentences/tokens per dataset
 Dataset |Articles |Sentences |Tokens
 -|-|-|-