{"adoption": {"forks": 1353, "observed_at": "2026-08-28T04:09:28.156026+00:00", "stars": 5707}, "canonical_url": "https://ross.abutalabs.com/products/pytorch-seq2seq", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.", "domain": ["deep-learning", "tutorials"], "enriched": true, "function": ["machine-learning", "nlp", "deep-learning"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["bentrevett/pytorch-seq2seq"], "name": "bentrevett/pytorch-seq2seq", "platform": ["python"], "pushed_at": "2024-01-20T16:51:04+00:00", "repo": "bentrevett/pytorch-seq2seq", "stars": 5707, "tags": ["seq2seq", "pytorch", "encoder-decoder", "attention", "transformer", "neural-machine-translation", "jupyter-notebooks", "torchtext", "natural-language-processing"], "topics": ["pytorch", "seq2seq", "sequence-to-sequence", "tutorial", "rnn", "gru", "lstm", "torchtext", "pytorch-tutorial", "pytorch-implmention", "encoder-decoder", "encoder-decoder-model", "neural-machine-translation", "pytorch-seq2seq", "attention", "transformer", "cnn-seq2seq", "pytorch-implementation", "pytorch-tutorials", "pytorch-nlp"], "urls": [], "use_cases": ["learn how to implement seq2seq models in pytorch", "understand encoder-decoder architectures with attention", "tutorial on building a neural machine translation model", "implement lstm and gru encoder-decoder from scratch", "learn transformers for sequence-to-sequence tasks", "hands-on pytorch nlp tutorial notebooks"], "what_it_is": "A collection of Jupyter notebook tutorials on implementing sequence-to-sequence (seq2seq) models in PyTorch, covering encoder-decoder RNNs, attention, and transformers, trained for German-to-English translation. It is an educational resource rather than a production library.", "when_to_avoid": ["you need a production-ready seq2seq library or pretrained models", "you want maintained, up-to-date tooling (TorchText is deprecated)", "you need large-scale or multilingual translation beyond the tutorial's German-English example"], "when_to_choose": ["you want to learn seq2seq concepts by implementing models step by step in PyTorch", "you need guided notebooks covering RNN, GRU, LSTM, attention, and transformer variants", "you are studying neural machine translation fundamentals"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/pytorch-seq2seq", "repo": "bentrevett/pytorch-seq2seq", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.156026+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:53:32.850256+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "13c0f83311d12d0b60c8dfde0108f297b031bd723b621ee8d58a6e5369bd97d4", "fetched_at": "2026-08-28T04:09:28.156026+00:00", "kind": "readme", "missing": false, "url": "https://github.com/bentrevett/pytorch-seq2seq"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2969, "days_push": 956, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}