A fast neural parser for torrent and release names.
NeuRelease is a fast, high-performance, multilingual neural parser for torrent and release names. It reads a name, extracts its fields - title, season and episode, year, quality, codecs, languages, release group and more - and classifies what the name is: a film, a series, music, a game, software, a book, and whether it is anime. Every value comes with a confidence score and the span it was read from.
It combines a character-level CNN with a Transformer encoder, running as int8 inference with runtime-dispatched SIMD kernels, behind C++, C and Python APIs. No ML runtime; nothing to download.
Fifteen fields read out of one name, each shown against the characters it came from. Try it in the browser.
Pattern-based parsers recognise known markers and guess the rest by position, so anything ambiguous - a number that may be a year or an episode, a word that may be a language or part of the title - is settled the same way every time, right or wrong. NeuRelease decides from context. It is trained on hundreds of thousands of real, labelled release names from a large torrent index - mostly English, with German, Spanish, French, Italian, Russian, Chinese and Japanese names as well. It is about 3.4× faster than GuessIt on one thread, ~10× in batch, and should come out ahead on most real-world names.
from neurelease import Parser
parser = Parser()
r = parser.parse("Ted.Lasso.S03E03.4-5-1.1080p.ATVP.WEB-DL.DDP5.1.H.264-NTb")
r.title # 'Ted Lasso'
r.season, r.episode, r.episode_title # 3, 3, '4-5-1'
r.streaming_service, r.release_group # 'ATVP', ('NTb',)
r.source == "WEB-DL" # True: enums equal their labels
r.year # None: the name does not say
r.title.confidence, r.episode_title.confidence # 1.00, 0.80: every value knows how sure the model was
r.to_dict() # {'title': 'Ted Lasso', 'season': 3, 'episode': 3, ...}
names = ["Blade.Runner.2049.2017.2160p.UHD.BluRay.x265-TERMiNAL.mkv",
"Stray_v1.5-Razor1911",
"El Joven Sheldon - Temporada 6 [HDTV 720p][Cap.604][AC3 5.1 Castellano][www.pctnew.org]",
"【高清剧集网 www.BTHDTV.com】邻家哥哥给我爱[第05-06集][简繁英字幕].Brother.Next.Door.2024.S01E05-06.1080p.WEB-DL.H264.AAC-BTHDTV",
"葬送のフリーレン 第28話 「また会ったときに恥ずかしいからね」 (1080p).mkv"]
releases = parser.parse_batch(names) # one result per name, in order
SHOW = ("title", "alternative_title", "season", "episode", "absolute_episode", "episode_title", "year", "content")
for r in releases:
d = r.to_dict()
print({k: d[k] for k in SHOW if d.get(k) is not None})
# {'title': 'Blade Runner 2049', 'year': 2017, 'content': 'movie'}
# {'title': 'Stray', 'content': 'game'}
# {'title': 'El Joven Sheldon', 'season': 6, 'episode': 4, 'content': 'series'}
# {'title': 'Brother Next Door', 'alternative_title': '邻家哥哥给我爱', 'season': 1, 'episode': [5, 6], 'year': 2024, 'content': 'series'}
# {'title': '葬送のフリーレン', 'absolute_episode': 28, 'episode_title': 'また会ったときに恥ずかしいからね', 'content': 'series', 'anime': True}Install the Python package with pip install ./bindings/python after building the library (see
Build). The wheel bundles the built library and the model files, so Parser() needs no paths and
works from any directory. parse_batch runs many names at once on several threads and returns them
in input order.
The C++ and C APIs: docs/API.md.
Everything a result contains, field by field: docs/RESULT.md. Titles and evidence
keep the original script - Latin, Han, Kana, Cyrillic.
Measured on the same machine on 2026-09-11 with the shipped model (version 3), on 3,344 video validation names:
| Metric, video only (3,344 names, 2026-09-11) | NeuRelease | GuessIt 4.4.0 |
|---|---|---|
| macro shared-field F1 | 97.57% | 86.45% |
| exact on every applicable shared field | 89.44% | 51.44% |
| single name, one thread, via Python | 2,446 us/name | 8,213 us/name |
| batch, 4 workers, via Python | 833 us/name | no batch API |
| GuessIt's 22 documented limitation cases solved | 19/22 | 0/22 |
| GuessIt's own published regression corpus | 683/859 | 804/859 |
NeuRelease is about 3.4× faster on one thread in this measurement. The four-worker batch result measures throughput through the Python binding, with complete results and evidence.
GuessIt's regression corpus is its own test suite: fixture strings such as FooBar.307.PDTV-FlexGet
and filesystem paths, written to exercise its rules, with every input assumed to be a video.
NeuRelease parses a single release name as found in real traffic and classifies it before assuming
anything, so on this corpus it scores 683 to 804 - and on real names the ranking reverses. The 22
limitation cases are GuessIt's own documented failures, not a representative sample.
Name-by-name comparisons, where the difference is visible rather than averaged: docs/ANIME.md and docs/LIVE_ACTION.md.
Method, exact model identity, scoring snapshot and reproduction: docs/GUESSIT_COMPARISON.md. Hardware and native kernel timings: docs/ARCHITECTURE.md.
Requirements: CMake 3.24+, a C++23 compiler, and Ninja or another CMake generator. PCRE2 is the only third-party library dependency and is fetched automatically when not installed.
git clone https://github.com/bonejay/neurelease.git
cd neurelease
cmake --preset release
cmake --build --preset release --parallel
ctest --preset release
python -m pytest bindings/python/testscmake --install build/release --prefix dist produces a self-contained package. Build options,
the optional GuessIt-corpus test, and benchmark instructions are in
docs/ARCHITECTURE.md.
| docs/API.md | Python, C++ and C usage |
| docs/RESULT.md | Every result field and its conventions |
| docs/ARCHITECTURE.md | Model, conversion layer, performance, build options, model versions |
| docs/C_ABI.md | The C binary interface |
| docs/GUESSIT_COMPARISON.md | Method and per-field numbers of the GuessIt comparison |
| docs/ANIME.md | The anime verdict, and five anime names read by both parsers |
| docs/LIVE_ACTION.md | Fourteen live-action names, in four languages, read by both parsers |
MIT.
PCRE2 is statically linked into the library and travels inside every binary this project distributes, including the Python wheels; its licence is in THIRD_PARTY_NOTICES.md.