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model_name = "dbmdz/bert-large-cased-finetuned-conll03-english"
ner_pipeline = pipeline('ner', model=model_name, tokenizer=model_name)
# List of simple sentences to analyze
sentences = [
"Barack Obama was born in Hawaii.",
"Apple Inc. was founded by Steve Job.",
"Microsoft is based in California.",
"The Great Wall of China is very long.",
"New York is a city in the USA.",
"The Pacific Ocean is the largest ocean.","Amazon is an online retailer."
]
# Analyze each sentence for NER
for i, sentence in enumerate(sentences):
print(f"\nSentence {i+1}:")
results = ner_pipeline(sentence)
# Print results
print("Named Entity Recognition Results:")
for entity in results:
# Extract the entity text from the original text using start and end indices
entity_text = sentence[entity['start']:entity['end']]
print(f"Entity: {entity_text}, Category: {entity['entity']}, Start:
{entity['start']}, End: {entity['end']}")