Update README.md

README.md

@@ -3,6 +3,9 @@ license: apache-2.0
 base_model: bert-base-cased
 tags:
 - generated_from_trainer
+- bert-finetuned
+- Named Entity Recognition
+- NER
 datasets:
 - conll2003
 metrics:
@@ -35,6 +38,7 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.9863572143403779
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -59,15 +63,18 @@ This model is a Named Entity Recognition (NER) model built using PyTorch and tra
 To use this model, load it through the Hugging Face Transformers library. Below is a basic example:
 
 ```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-# Load the NER pipeline
-ner_pipeline = pipeline("ner", model="Ashaduzzaman/bert-finetuned-ner")
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Ashaduzzaman/bert-finetuned-ner")
+model = AutoModelForTokenClassification.from_pretrained("Ashaduzzaman/bert-finetuned-ner")
 
-# Example text
-text = "Hugging Face Inc. is based in New York City."
+# Create a pipeline for NER
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
 
-# Perform NER
+# Example inference
+text = "Hugging Face Inc. is based in New York City."
 entities = ner_pipeline(text)
 
 print(entities)
@@ -115,4 +122,4 @@ These results indicate the model's ability to correctly identify and classify na
 - Transformers 4.42.4
 - Pytorch 2.3.1+cu121
 - Datasets 2.21.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1