# Jiakui/awesome-bert

bert nlp papers, applications and  github resources, including the newst xlnet  ， BERT、XLNet 相关论文和 github 项目

Repository: https://github.com/Jiakui/awesome-bert
Canonical: https://ross.abutalabs.com/products/awesome-bert
License Family: other
Topics: bert, google-bert, nlp, xlnet
Last push: 2021-03-21T03:06:33+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": 2841, "days_push": 1991, "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 1839, forks 344 (observed 2026-08-28T04:05:42.934663+00:00)

## What it is
A curated awesome-list collecting BERT and XLNet related papers, GitHub repositories, and pre-trained model resources for NLP. It links official and community implementations across TensorFlow, PyTorch, and MXNet.

## Use cases
- find BERT papers and implementations
- learn about BERT and XLNet models
- find pretrained BERT models in PyTorch
- research transformer-based NLP resources
- compare BERT implementations across frameworks

## When to choose
- you want a curated starting point for BERT research and code
- you need links to official and third-party BERT implementations

## When to avoid
- you need a maintained library rather than a link collection
- you need resources newer than ~2021, as the list is no longer updated

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: deep-learning, machine-learning, tutorials, awesome-lists
- platform: python
- tags: bert, xlnet, awesome-list, papers, pretrained-models, transformers, natural-language-processing

## Member repositories
- Jiakui/awesome-bert (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:42.934663+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 2026-08-30T03:18:06.014211+00:00, confidence not recorded.
  - readme: https://github.com/Jiakui/awesome-bert (fetched 2026-08-28T04:05:42.934663+00:00, sha 8b8490b62be4)
- Data as of 2026-08-30T08:39:29.467469+00:00.