Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 23:03 UTC

ZeyuChen / PaddleNLP

Easy-to-use and Fast NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications.

PythonApache-2.0★ 0 stars⑂ 1 forksince Apr 2022forkView on GitHub ↗

ZeyuChen/PaddleNLP holds a health index of 21 out of 100, placing it in the At Risk band. It scores highest on Sustainability & Governance (79/100) and lowest on Community & Adoption (12/100). It was last updated 215 days ago. 6 contributors account for most of its recent work.

21
overall / 100
At Risk

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

21
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 40 is calibrated to 35 on the published index scale (record calibration 2026-08-02).

Ownership

Zeyu ChenPersonal account
311 followers39 public repossince Jan 2012Baidu, Inc.

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpaddlenlppoints to another repo — not scored2.8.115,91095811 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

22At Risk · 21% of overall
How it's scored
3.6/36Push recencylast push 215 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push215
active_weeks_last_year0
How it's scored
16.2/27Ships releases18 version tags (no GitHub releases)
0/36Release recencylatest release 1,608 days ago
27/27Release cadencea release every ~28.8 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count18
latest_release_tagv2.2.6
releases_from_tagsyes
days_since_latest_release1,608
mean_days_between_releases28.8

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

12Critical · 17% of overall
How it's scored
0/60Stars0 stars
0/25Forks1 forks
0/15Watchers0 watchers
Inputs used
forks1
stars0
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
0/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeno
has_licenseno
readme_badges4
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

79Good · 23% of overall
How it's scored
48.6/54Bus factor6 contributor(s) cover half of all commits
19.8/22.5Commit distributiontop contributor authored 12% of commits
13.5/13.5Contributor breadth80 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor6
contributors_sampled80
top_contributor_share0.12
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
17.9/25Owner reach311 followers of ZeyuChen
23.7/25Track record39 public repos, account ~14 yr old
Inputs used
followers311
owner_typeUser
is_verified
owner_loginZeyuChen
public_repos39
account_age_days5,343
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

57Moderate · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

50Moderate
How it's scored
0/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://paddlenlp.readthedocs.io
0/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepagehttps://paddlenlp.readthedocs.io
docs_sitehttps://paddlenlp.readthedocs.io
has_readmeno
has_docs_diryes
has_descriptionno

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

14Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsdocs/requirements.txt, requirements.txt, setup.py, tests/requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
How it's scored
9.5/35Direct dependencies free of known advisories1 affected: onnx 1.17.0 (critical 9.1)
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
32.6/40No advisories left outstanding1 advisory-carrying package(s) unaddressed past 90 days; oldest published 414 days ago
Inputs used
sourceosv
advisories16
affected_packages1
assessed_packages89
unassessed_packages0
affected_by_severitycritical 1
direct_affected_packages1
Matched the pypi:paddlenlp@2.8.1 runtime dependency closure — what installing the published package pulls in — 89 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

56Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
34.1/40Legible commit history64 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.64
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapapplications/neural_search/recall/domain_adaptive_pretraining/data_tools/Makefile, docs/Makefile, examples/language_model/data_tools/Makefile, examples/language_model/electra/deploy/lite/Makefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesapplications/neural_search/recall/domain_adaptive_pretraining/data_tools/Makefile, docs/Makefile, examples/language_model/data_tools/Makefile, examples/language_model/electra/deploy/lite/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54.3/55Manageable file sizes14/1,160 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes145,536
source_files_sampled1,160
oversized_source_files14
How it's scored
40/40API schema (OpenAPI/GraphQL/proto)examples/language_model/experimental/ernie/propeller/data/example.proto, examples/language_model/experimental/ernie/propeller/data/feature.proto
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, sample, samples
Inputs used
example_dirsexamples, sample, samples
has_mcp_signalno
api_schema_filesexamples/language_model/experimental/ernie/propeller/data/example.proto, examples/language_model/experimental/ernie/propeller/data/feature.proto
interfaces_expected_of
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

0GitHub stars
80contributors
0commits, last 12 months
215days since last push
18releases
6bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Community profile unavailable
  • pypi package 'paddlenlp' points at a different repository (https://github.com/PaddlePaddle/PaddleNLP); excluded from ecosystem scoring
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/09 23:00:47 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 1

Installing pypi:paddlenlp@2.8.1 pulls in 89 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
onnx1.17.0directcritical161.22.0

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.