linkedin/detext

Health v2 · maintenance only

Adoption not part of the score

1263 stars · 135 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeText is a deep neural text understanding framework from LinkedIn for NLP ranking, classification, and language generation tasks. It uses semantic matching with configurable text encoders (CNN, BERT, LSTM) combined with wide & deep feature processing, built on TensorFlow.

Use cases

• build a learning-to-rank model for search results
• train a semantic matching model for query understanding
• classify text into multiple categories with deep neural networks
• rank items in a recommender system using text embeddings
• fine-tune BERT for a ranking task
• combine deep text features with traditional features in a wide and deep model

When to choose

• you need a configurable TensorFlow framework for text-based ranking or classification
• you want to combine BERT/CNN/LSTM encoders with wide & deep feature processing
• you need learning-to-rank losses with semantic matching interaction layers

When to avoid

• you need a lightweight or non-TensorFlow NLP stack
• you want actively developed tooling with frequent releases
• you need simple keyword search without deep learning

Facets

framework · maturity maintenance

machine-learning nlp deep-learning search-engine machine-learning python ranking learning-to-rank text-classification semantic-matching tensorflow bert query-understanding natural-language-processing search

Member repositories

For agents

markdown · JSON · MCP: product_card(name="linkedin/detext")