# tomohideshibata/BERT-related-papers

BERT-related papers

Repository: https://github.com/tomohideshibata/BERT-related-papers
Canonical: https://ross.abutalabs.com/products/bert-related-papers
License Family: other
Last push: 2023-08-12T08:28:50+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": 2639, "days_push": 1117, "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 2032, forks 277 (observed 2026-08-28T04:06:07.661778+00:00)

## What it is
A curated list of research papers related to BERT and transformer-based language models, organized by topic such as surveys, downstream tasks, model compression, and multilingual models. It serves as a reading resource for NLP researchers and practitioners rather than usable software.

## Use cases
- find papers about BERT and pretrained language models
- survey transformer model variants for NLP research
- find literature on model compression and distillation of language models
- look up papers on multilingual and domain-specific BERT models
- find RLHF and large language model research papers
- get started reading about transfer learning in NLP

## When to choose
- you need a curated, categorized reading list of BERT/transformer papers
- you are doing a literature review on pretrained language models
- you want paper links organized by NLP subtopic

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: large-language-models, deep-learning, tutorials
- platform: cross-platform
- tags: awesome-list, papers, bert, transformers, research-papers, curated-list, natural-language-processing

## Member repositories
- tomohideshibata/BERT-related-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:07.661778+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 2026-08-30T02:59:30.462037+00:00, confidence not recorded.
  - readme: https://github.com/tomohideshibata/BERT-related-papers (fetched 2026-08-28T04:06:07.661778+00:00, sha ed6a806dba4b)
- Data as of 2026-08-30T08:39:29.467469+00:00.