# leerumor/nlp_tutorial

NLP超强入门指南，包括各任务sota模型汇总（文本分类、文本匹配、序列标注、文本生成、语言模型），以及代码、技巧

Repository: https://github.com/leerumor/nlp_tutorial
Canonical: https://ross.abutalabs.com/products/nlp_tutorial
License Family: other
Last push: 2022-10-16T06:57:52+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2081, "days_push": 1417, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1829, forks 303 (observed 2026-08-28T04:05:41.709105+00:00)

## What it is
A Chinese-language NLP learning guide that aggregates SOTA model lists, surveys, and practical tips for core tasks like text classification, text matching, sequence labeling, text generation, and language modeling. It provides a structured learning path from machine learning fundamentals through hands-on competition practice.

## Use cases
- learn nlp from scratch
- find sota models for text classification
- nlp learning roadmap
- study sequence labeling models
- prepare for nlp kaggle competitions
- understand bert and language model variants
- learn text matching techniques

## When to choose
- you want a structured beginner-to-intermediate NLP learning path
- you need curated lists of SOTA models and surveys per NLP task
- you prefer Chinese-language explanations of NLP concepts

## When to avoid
- you need runnable production code or a maintained library
- you want up-to-date coverage of LLM-era methods (last updated 2022)
- you need English-language tutorials

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, tutorials
- platform: python
- tags: nlp-tutorial, sota-models, text-classification, sequence-labeling, text-matching, language-models, chinese, learning-path, natural-language-processing

## Member repositories
- leerumor/nlp_tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.709105+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:19:22.800207+00:00, confidence not recorded.
  - readme: https://github.com/leerumor/nlp_tutorial (fetched 2026-08-28T04:05:41.709105+00:00, sha 5ae4a6195b16)
- Data as of 2026-08-30T08:39:29.467469+00:00.