Introduction to ModelScope Community's Fundamental NLP Models and Their Applications

ModelScope, Alibaba Damo Academy's open platform, provides a collection of 50 foundational NLP models covering tokenization, part‑of‑speech tagging, named entity recognition (NER) and text representation.

Basic NLP tasks are explained with examples of Chinese e‑commerce titles and English sentences, describing tokenization, POS tagging, word weighting, central word identification, entity extraction and linking.

In ModelScope, six token‑POS models, forty NER models and four text‑embedding models are available, supporting various domains such as e‑commerce, news, resumes and medical texts.

Chinese tokenization uses encoder‑plus‑head architectures (e.g., BERT or StructBERT) with a boundary‑aware pre‑training strategy (ACE) that improves segmentation, POS and NER performance.

NER models follow a Transformer‑plus‑CRF design, achieving state‑of‑the‑art results on benchmarks like CoNLL and SemEval, and have been extended to fine‑grained, multilingual and multimodal scenarios.

Retrieval‑enhanced techniques (Retriever‑Reader, multi‑view training) inject external knowledge from sources such as Wikipedia to boost NER, speech‑to‑text understanding and multimodal NER, leading to top rankings in several competitions.

Text representation models adopt a two‑stage retrieval and re‑ranking pipeline, incorporating term‑weight information in pre‑training and a hybrid ranking model that attains SOTA on MS MARCO passage ranking.

Future plans include expanding capabilities to address structure extraction, keyword extraction, encyclopedia linking and additional similarity models, as well as promoting the AdaSeq toolkit for sequence‑understanding research.