updated code to run pipeline

README.md

@@ -95,8 +95,8 @@ Use the code below to get started with the model:
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+tokenizer = AutoTokenizer.from_pretrained("jayant-yadav/roberta-base-multinerd")
+model = AutoModelForTokenClassification.from_pretrained("jayant-yadav/roberta-base-multinerd")
 
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 example = "My name is Wolfgang and I live in Berlin"
@@ -116,7 +116,7 @@ print(ner_results)
 
 #### Preprocessing [optional]
 
-English dataset was filterd out : `train_dataset = train_dataset.filter(lambda x: x['lang'] == 'en')`
+English dataset was filterd out : ```train_dataset = train_dataset.filter(lambda x: x['lang'] == 'en')```
 
 
 #### Training Hyperparameters
@@ -146,8 +146,8 @@ Tested on Full test split of MultiNERD dataset.
 
 
 #### Metrics
-Model versions and checkpoint were evaluated using F1, Precision and Recall.
-For this `seqeval` metric was used: `metric = load_metric("seqeval")`.
+Model versions and checkpoint were evaluated using F1, Precision and Recall.  
+For this `seqeval` metric was used: ```metric = load_metric("seqeval")```.
 
 ### Results
 
@@ -179,7 +179,7 @@ Follows the same as RoBERTa-BASE
 
 #### Hardware
 
-Kaggle - GPU T4x2
+Kaggle - GPU T4x2  
 Google Colab - GPU T4x1
 
 #### Software