NLP & Transformers1 code example
⚡ +100 XP
NER & Text Classification
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Named Entity Recognition (NER)
NER is a sequence labeling task that identifies and classifies named entities in text: Person (PER), Organization (ORG), Location (LOC), Date (DATE), etc. 'Apple CEO Tim Cook announced iPhone 16 in Cupertino.' → Apple=ORG, Tim Cook=PER, iPhone 16=PRODUCT, Cupertino=LOC Tagging scheme: BIO (Begin, Inside, Outside) or BIOES.
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BIO Tagging
B-PER = Beginning of a Person entity I-PER = Inside (continuation) of a Person entity O = Outside any entity 'Tim' → B-PER 'Cook' → I-PER 'Apple' → B-ORG 'is' → O
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NER with HuggingFace Pipeline
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Zero-shot classification with BART-MNLI is surprisingly powerful — often beats fine-tuned models when labeled data is scarce.
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