# Alic-yuan/nlp-beginner-finish

此项目完成了关于 NLP-Beginner：自然语言处理入门练习 的所有任务，所有代码都经过测试,可以正常运行。

Repository: https://github.com/Alic-yuan/nlp-beginner-finish
Canonical: https://ross.abutalabs.com/products/nlp-beginner-finish
Language: Python
License Family: other
Topics: python
Last push: 2022-08-25T12:03:59+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": 2338, "days_push": 1469, "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 1393, forks 317 (observed 2026-08-28T04:04:36.289889+00:00)

## What it is
A completed set of solutions to Fudan University's NLP-Beginner introductory exercises, covering five tasks from machine-learning text classification to neural language models. All code is tested and runnable, serving as a reference for learners studying natural language processing fundamentals.

## Use cases
- learn nlp basics with worked examples
- see how to implement text classification with machine learning
- study attention-based text matching code
- understand LSTM+CRF sequence labeling implementation
- find a reference solution for a neural language model assignment
- practice deep learning text classification in python

## When to choose
- you are working through the FudanNLP NLP-Beginner exercises and want reference solutions
- you want runnable Python examples of classic NLP tasks
- you learn best by reading completed implementations

## When to avoid
- you need a production NLP library or framework
- you want maintained, licensed software for a real project
- you need solutions guaranteed to be plagiarism-free or up to date

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, tutorials
- platform: python
- tags: nlp-beginner, text-classification, sequence-labeling, language-model, attention, lstm-crf, educational, natural-language-processing

## Member repositories
- Alic-yuan/nlp-beginner-finish (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.289889+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 2026-08-30T04:39:27.779715+00:00, confidence not recorded.
  - readme: https://github.com/Alic-yuan/nlp-beginner-finish (fetched 2026-08-28T04:04:36.289889+00:00, sha a1c9e3aafbfa)
- Data as of 2026-08-30T08:39:29.467469+00:00.