# km1994/NLP-Interview-Notes

该仓库主要记录 NLP 算法工程师相关的面试题

Repository: https://github.com/km1994/NLP-Interview-Notes
Canonical: https://ross.abutalabs.com/products/nlp-interview-notes
License Family: other
Topics: bert, transformer, nlp, ner, deel-learning
Last push: 2023-10-10T12:38:46+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": 2018, "days_push": 1058, "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 2579, forks 497 (observed 2026-08-28T04:07:02.359249+00:00)

## What it is
A curated collection of NLP algorithm engineer interview questions and study notes, covering topics like HMM, MEMM, CRF, BERT, Transformer, and named entity recognition. It serves as a learning resource for preparing for NLP-related job interviews.

## Use cases
- prepare for an nlp algorithm engineer interview
- review transformer and bert interview questions
- study named entity recognition concepts like crf and bilstm-crf
- find common machine learning interview questions for nlp roles
- brush up on hmm and memm before an interview

## When to choose
- you are preparing for NLP or machine learning engineering interviews
- you want structured question-and-answer notes on classic NLP models
- you need a free study guide covering BERT, Transformer, and NER topics

## When to avoid
- you need production-ready NLP code or libraries
- you want a complete textbook with full derivations rather than interview Q&A
- you need up-to-date content on the latest LLM research

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, documentation
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: interview-preparation, study-notes, bert, transformer, ner, chinese, natural-language-processing

## Member repositories
- km1994/NLP-Interview-Notes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.359249+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 2026-08-30T02:22:39.734109+00:00, confidence not recorded.
  - readme: https://github.com/km1994/NLP-Interview-Notes (fetched 2026-08-28T04:07:02.359249+00:00, sha 4fb142a627e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.