{"adoption": {"forks": 99, "observed_at": "2026-08-28T04:04:12.971721+00:00", "stars": 1275}, "canonical_url": "https://ross.abutalabs.com/products/ma-lab-berkeley-crate", "card": {"archived": false, "artifact_type": "library", "description": "Code for CRATE (Coding RAte reduction TransformEr).", "domain": ["deep-learning", "machine-learning", "computer-vision"], "enriched": true, "function": ["deep-learning", "machine-learning", "transformers"], "health_score": 20, "homepage": null, "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "active", "member_repos": ["Ma-Lab-Berkeley/CRATE"], "name": "Ma-Lab-Berkeley/CRATE", "platform": ["python"], "pushed_at": "2024-10-23T15:24:28+00:00", "repo": "Ma-Lab-Berkeley/CRATE", "stars": 1275, "tags": ["pytorch", "white-box-transformer", "sparse-rate-reduction", "transformer-architecture", "interpretability", "vision-transformer", "masked-autoencoder", "self-supervised-learning", "image-segmentation", "research-code", "natural-language-processing", "gpu"], "topics": ["compression", "sparsification", "transformer-architecture", "white-box-architecture"], "urls": [], "use_cases": ["implement white-box transformer architectures in pytorch", "train an interpretable vision transformer", "reproduce the NeurIPS 2023 CRATE paper results", "study transformers as optimization of a compression objective", "experiment with sparse rate reduction losses", "pretrain masked autoencoding models with structured diffusion"], "what_it_is": "CRATE is the official PyTorch implementation of the Coding RAte reduction TransformEr, a family of 'white-box' transformer architectures derived mathematically as unrolled optimization of a sparse rate reduction objective. It accompanies published NeurIPS, CPAL, and ICLR papers and provides models and training code for vision tasks such as segmentation and masked autoencoding.", "when_to_avoid": ["you need a production-ready, plug-and-play transformer with state-of-the-art benchmarks", "you want a maintained general-purpose model zoo rather than research code", "you are not working in PyTorch or need heavy ecosystem tooling"], "when_to_choose": ["you are researching or teaching principled, mathematically derived transformer architectures", "you want to reproduce or extend the CRATE papers' vision experiments", "you need an interpretable alternative to black-box transformer blocks in PyTorch"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/ma-lab-berkeley-crate", "repo": "Ma-Lab-Berkeley/CRATE", "role": "main", "score": 29}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:12.971721+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T05:02:59.895932+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "549c52a8c25a39418f3950a2b4e5ad6774776f2739c02403869d3785ab5b0659", "fetched_at": "2026-08-28T04:04:12.971721+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Ma-Lab-Berkeley/CRATE"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 85, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 1192, "days_push": 679, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 29, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}