lint

.gitignore

@@ -12,4 +12,4 @@ eval-queue-bk/
 eval-results-bk/
 logs/
 /.pdm-python
-leaderboard.csv
+leaderboard.csv

pyproject.toml

@@ -65,3 +65,4 @@ select= [
     #"D",   # pydocstyle
 ]
 fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+ignore = ["RUF001"]

requirements.txt

@@ -131,6 +131,9 @@ idna==3.7 \
 importlib-resources==6.4.0 \
     --hash=sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c \
     --hash=sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145
+iniconfig==2.0.0 \
+    --hash=sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3 \
+    --hash=sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374
 jinja2==3.1.4 \
     --hash=sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369 \
     --hash=sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d
@@ -172,6 +175,56 @@ kiwisolver==1.4.5 \
     --hash=sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7 \
     --hash=sha256:ec20916e7b4cbfb1f12380e46486ec4bcbaa91a9c448b97023fde0d5bbf9e4ff \
     --hash=sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892
+lxml==5.2.2 \
+    --hash=sha256:05f8757b03208c3f50097761be2dea0aba02e94f0dc7023ed73a7bb14ff11eb0 \
+    --hash=sha256:06668e39e1f3c065349c51ac27ae430719d7806c026fec462e5693b08b95696b \
+    --hash=sha256:13e69be35391ce72712184f69000cda04fc89689429179bc4c0ae5f0b7a8c21b \
+    --hash=sha256:1a7aca7964ac4bb07680d5c9d63b9d7028cace3e2d43175cb50bba8c5ad33316 \
+    --hash=sha256:1e275ea572389e41e8b039ac076a46cb87ee6b8542df3fff26f5baab43713bca \
+    --hash=sha256:34e17913c431f5ae01d8658dbf792fdc457073dcdfbb31dc0cc6ab256e664a8d \
+    --hash=sha256:364d03207f3e603922d0d3932ef363d55bbf48e3647395765f9bfcbdf6d23632 \
+    --hash=sha256:3b6a30a9ab040b3f545b697cb3adbf3696c05a3a68aad172e3fd7ca73ab3c835 \
+    --hash=sha256:3d98de734abee23e61f6b8c2e08a88453ada7d6486dc7cdc82922a03968928db \
+    --hash=sha256:4820c02195d6dfb7b8508ff276752f6b2ff8b64ae5d13ebe02e7667e035000b9 \
+    --hash=sha256:50127c186f191b8917ea2fb8b206fbebe87fd414a6084d15568c27d0a21d60db \
+    --hash=sha256:55ce6b6d803890bd3cc89975fca9de1dff39729b43b73cb15ddd933b8bc20484 \
+    --hash=sha256:5e097646944b66207023bc3c634827de858aebc226d5d4d6d16f0b77566ea182 \
+    --hash=sha256:69ab77a1373f1e7563e0fb5a29a8440367dec051da6c7405333699d07444f511 \
+    --hash=sha256:6a520b4f9974b0a0a6ed73c2154de57cdfd0c8800f4f15ab2b73238ffed0b36e \
+    --hash=sha256:74e4f025ef3db1c6da4460dd27c118d8cd136d0391da4e387a15e48e5c975147 \
+    --hash=sha256:76acba4c66c47d27c8365e7c10b3d8016a7da83d3191d053a58382311a8bf4e1 \
+    --hash=sha256:875a3f90d7eb5c5d77e529080d95140eacb3c6d13ad5b616ee8095447b1d22e7 \
+    --hash=sha256:89feb82ca055af0fe797a2323ec9043b26bc371365847dbe83c7fd2e2f181c34 \
+    --hash=sha256:8ab6a358d1286498d80fe67bd3d69fcbc7d1359b45b41e74c4a26964ca99c3f8 \
+    --hash=sha256:8e8d351ff44c1638cb6e980623d517abd9f580d2e53bfcd18d8941c052a5a009 \
+    --hash=sha256:96e85aa09274955bb6bd483eaf5b12abadade01010478154b0ec70284c1b1526 \
+    --hash=sha256:981a06a3076997adf7c743dcd0d7a0415582661e2517c7d961493572e909aa1d \
+    --hash=sha256:9cd5323344d8ebb9fb5e96da5de5ad4ebab993bbf51674259dbe9d7a18049525 \
+    --hash=sha256:9d6c6ea6a11ca0ff9cd0390b885984ed31157c168565702959c25e2191674a14 \
+    --hash=sha256:a233bb68625a85126ac9f1fc66d24337d6e8a0f9207b688eec2e7c880f012ec0 \
+    --hash=sha256:a2f6a1bc2460e643785a2cde17293bd7a8f990884b822f7bca47bee0a82fc66b \
+    --hash=sha256:ae4073a60ab98529ab8a72ebf429f2a8cc612619a8c04e08bed27450d52103c0 \
+    --hash=sha256:ae791f6bd43305aade8c0e22f816b34f3b72b6c820477aab4d18473a37e8090b \
+    --hash=sha256:aef5474d913d3b05e613906ba4090433c515e13ea49c837aca18bde190853dff \
+    --hash=sha256:b128092c927eaf485928cec0c28f6b8bead277e28acf56800e972aa2c2abd7a2 \
+    --hash=sha256:b47633251727c8fe279f34025844b3b3a3e40cd1b198356d003aa146258d13a2 \
+    --hash=sha256:b537bd04d7ccd7c6350cdaaaad911f6312cbd61e6e6045542f781c7f8b2e99d2 \
+    --hash=sha256:b5e4ef22ff25bfd4ede5f8fb30f7b24446345f3e79d9b7455aef2836437bc38a \
+    --hash=sha256:bb2dc4898180bea79863d5487e5f9c7c34297414bad54bcd0f0852aee9cfdb87 \
+    --hash=sha256:bcc98f911f10278d1daf14b87d65325851a1d29153caaf146877ec37031d5f36 \
+    --hash=sha256:bec4bd9133420c5c52d562469c754f27c5c9e36ee06abc169612c959bd7dbb07 \
+    --hash=sha256:dfa7c241073d8f2b8e8dbc7803c434f57dbb83ae2a3d7892dd068d99e96efe2c \
+    --hash=sha256:e290d79a4107d7d794634ce3e985b9ae4f920380a813717adf61804904dc4393 \
+    --hash=sha256:e481bba1e11ba585fb06db666bfc23dbe181dbafc7b25776156120bf12e0d5a6 \
+    --hash=sha256:f2a09f6184f17a80897172863a655467da2b11151ec98ba8d7af89f17bf63dae \
+    --hash=sha256:f5b65529bb2f21ac7861a0e94fdbf5dc0daab41497d18223b46ee8515e5ad297 \
+    --hash=sha256:f956196ef61369f1685d14dad80611488d8dc1ef00be57c0c5a03064005b0f30 \
+    --hash=sha256:fbc9d316552f9ef7bba39f4edfad4a734d3d6f93341232a9dddadec4f15d425f \
+    --hash=sha256:ff69a9a0b4b17d78170c73abe2ab12084bdf1691550c5629ad1fe7849433f324 \
+    --hash=sha256:ffb2be176fed4457e445fe540617f0252a72a8bc56208fd65a690fdb1f57660b
+markdown==3.6 \
+    --hash=sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f \
+    --hash=sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224
 markdown-it-py==3.0.0 \
     --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \
     --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb
@@ -202,6 +255,24 @@ matplotlib==3.9.0 \
 mdurl==0.1.2 \
     --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
     --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+multidict==6.0.5 \
+    --hash=sha256:0d63c74e3d7ab26de115c49bffc92cc77ed23395303d496eae515d4204a625e7 \
+    --hash=sha256:1d147090048129ce3c453f0292e7697d333db95e52616b3793922945804a433c \
+    --hash=sha256:215ed703caf15f578dca76ee6f6b21b7603791ae090fbf1ef9d865571039ade5 \
+    --hash=sha256:21fd81c4ebdb4f214161be351eb5bcf385426bf023041da2fd9e60681f3cebae \
+    --hash=sha256:220dd781e3f7af2c2c1053da9fa96d9cf3072ca58f057f4c5adaaa1cab8fc442 \
+    --hash=sha256:228b644ae063c10e7f324ab1ab6b548bdf6f8b47f3ec234fef1093bc2735e5f9 \
+    --hash=sha256:3cc2ad10255f903656017363cd59436f2111443a76f996584d1077e43ee51182 \
+    --hash=sha256:411bf8515f3be9813d06004cac41ccf7d1cd46dfe233705933dd163b60e37600 \
+    --hash=sha256:6939c95381e003f54cd4c5516740faba40cf5ad3eeff460c3ad1d3e0ea2549bf \
+    --hash=sha256:766c8f7511df26d9f11cd3a8be623e59cca73d44643abab3f8c8c07620524e4a \
+    --hash=sha256:7afcdd1fc07befad18ec4523a782cde4e93e0a2bf71239894b8d61ee578c1319 \
+    --hash=sha256:7c6390cf87ff6234643428991b7359b5f59cc15155695deb4eda5c777d2b880f \
+    --hash=sha256:896ebdcf62683551312c30e20614305f53125750803b614e9e6ce74a96232604 \
+    --hash=sha256:99f60d34c048c5c2fabc766108c103612344c46e35d4ed9ae0673d33c8fb26e8 \
+    --hash=sha256:c1c1496e73051918fcd4f58ff2e0f2f3066d1c76a0c6aeffd9b45d53243702cc \
+    --hash=sha256:f7e301075edaf50500f0b341543c41194d8df3ae5caf4702f2095f3ca73dd8da \
+    --hash=sha256:fe5d7785250541f7f5019ab9cba2c71169dc7d74d0f45253f8313f436458a4ef
 numpy==1.26.4 \
     --hash=sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010 \
     --hash=sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a \
@@ -265,6 +336,9 @@ pillow==10.3.0 \
     --hash=sha256:d93480005693d247f8346bc8ee28c72a2191bdf1f6b5db469c096c0c867ac015 \
     --hash=sha256:dd78700f5788ae180b5ee8902c6aea5a5726bac7c364b202b4b3e3ba2d293170 \
     --hash=sha256:f0d0591a0aeaefdaf9a5e545e7485f89910c977087e7de2b6c388aec32011e9f
+pluggy==1.5.0 \
+    --hash=sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1 \
+    --hash=sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669
 pyarrow==16.1.0 \
     --hash=sha256:15fbb22ea96d11f0b5768504a3f961edab25eaf4197c341720c4a387f6c60315 \
     --hash=sha256:17e23b9a65a70cc733d8b738baa6ad3722298fa0c81d88f63ff94bf25eaa77b9 \
@@ -307,6 +381,9 @@ pydantic-core==2.18.4 \
     --hash=sha256:eae237477a873ab46e8dd748e515c72c0c804fb380fbe6c85533c7de51f23a8f \
     --hash=sha256:ec3beeada09ff865c344ff3bc2f427f5e6c26401cc6113d77e372c3fdac73864 \
     --hash=sha256:f76d0ad001edd426b92233d45c746fd08f467d56100fd8f30e9ace4b005266e4
+pydantic-settings==2.3.3 \
+    --hash=sha256:87fda838b64b5039b970cd47c3e8a1ee460ce136278ff672980af21516f6e6ce \
+    --hash=sha256:e4ed62ad851670975ec11285141db888fd24947f9440bd4380d7d8788d4965de
 pydub==0.25.1 \
     --hash=sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6 \
     --hash=sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f
@@ -316,6 +393,12 @@ pygments==2.18.0 \
 pyparsing==3.1.2 \
     --hash=sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad \
     --hash=sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742
+pytest==8.2.2 \
+    --hash=sha256:c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343 \
+    --hash=sha256:de4bb8104e201939ccdc688b27a89a7be2079b22e2bd2b07f806b6ba71117977
+pytest-vcr==1.0.2 \
+    --hash=sha256:23ee51b75abbcc43d926272773aae4f39f93aceb75ed56852d0bf618f92e1896 \
+    --hash=sha256:2f316e0539399bea0296e8b8401145c62b6f85e9066af7e57b6151481b0d6d9c
 python-dateutil==2.9.0.post0 \
     --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
     --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
@@ -428,6 +511,9 @@ sniffio==1.3.1 \
 starlette==0.37.2 \
     --hash=sha256:6fe59f29268538e5d0d182f2791a479a0c64638e6935d1c6989e63fb2699c6ee \
     --hash=sha256:9af890290133b79fc3db55474ade20f6220a364a0402e0b556e7cd5e1e093823
+tomli==2.0.1; python_version < "3.11" \
+    --hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \
+    --hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f
 tomlkit==0.12.0 \
     --hash=sha256:01f0477981119c7d8ee0f67ebe0297a7c95b14cf9f4b102b45486deb77018716 \
     --hash=sha256:926f1f37a1587c7a4f6c7484dae538f1345d96d793d9adab5d3675957b1d0766
@@ -492,6 +578,9 @@ uvloop==0.19.0; (sys_platform != "cygwin" and sys_platform != "win32") and platf
     --hash=sha256:7b1fd71c3843327f3bbc3237bedcdb6504fd50368ab3e04d0410e52ec293f5b8 \
     --hash=sha256:cd81bdc2b8219cb4b2556eea39d2e36bfa375a2dd021404f90a62e44efaaf957 \
     --hash=sha256:de4313d7f575474c8f5a12e163f6d89c0a878bc49219641d49e6f1444369a90e
+vcrpy==5.1.0 \
+    --hash=sha256:605e7b7a63dcd940db1df3ab2697ca7faf0e835c0852882142bafb19649d599e \
+    --hash=sha256:bbf1532f2618a04f11bce2a99af3a9647a32c880957293ff91e0a5f187b6b3d2
 watchfiles==0.22.0 \
     --hash=sha256:00ad0bcd399503a84cc688590cdffbe7a991691314dde5b57b3ed50a41319a31 \
     --hash=sha256:030bc4e68d14bcad2294ff68c1ed87215fbd9a10d9dea74e7cfe8a17869785ab \
@@ -547,3 +636,34 @@ websockets==11.0.3 \
     --hash=sha256:ed058398f55163a79bb9f06a90ef9ccc063b204bb346c4de78efc5d15abfe602 \
     --hash=sha256:f2e58f2c36cc52d41f2659e4c0cbf7353e28c8c9e63e30d8c6d3494dc9fdedcf \
     --hash=sha256:ffd7dcaf744f25f82190856bc26ed81721508fc5cbf2a330751e135ff1283564
+wrapt==1.16.0 \
+    --hash=sha256:2a88e6010048489cda82b1326889ec075a8c856c2e6a256072b28eaee3ccf487 \
+    --hash=sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d \
+    --hash=sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1 \
+    --hash=sha256:73aa7d98215d39b8455f103de64391cb79dfcad601701a3aa0dddacf74911d72 \
+    --hash=sha256:807cc8543a477ab7422f1120a217054f958a66ef7314f76dd9e77d3f02cdccd0 \
+    --hash=sha256:ac83a914ebaf589b69f7d0a1277602ff494e21f4c2f743313414378f8f50a4cf \
+    --hash=sha256:bb2dee3874a500de01c93d5c71415fcaef1d858370d405824783e7a8ef5db440 \
+    --hash=sha256:bf5703fdeb350e36885f2875d853ce13172ae281c56e509f4e6eca049bdfb136 \
+    --hash=sha256:decbfa2f618fa8ed81c95ee18a387ff973143c656ef800c9f24fb7e9c16054e2 \
+    --hash=sha256:e4fdb9275308292e880dcbeb12546df7f3e0f96c6b41197e0cf37d2826359020 \
+    --hash=sha256:f6b2d0c6703c988d334f297aa5df18c45e97b0af3679bb75059e0e0bd8b1069d \
+    --hash=sha256:ffa565331890b90056c01db69c0fe634a776f8019c143a5ae265f9c6bc4bd6d4
+yarl==1.9.4 \
+    --hash=sha256:357495293086c5b6d34ca9616a43d329317feab7917518bc97a08f9e55648455 \
+    --hash=sha256:54525ae423d7b7a8ee81ba189f131054defdb122cde31ff17477951464c1691c \
+    --hash=sha256:54beabb809ffcacbd9d28ac57b0db46e42a6e341a030293fb3185c409e626b8b \
+    --hash=sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf \
+    --hash=sha256:7855426dfbddac81896b6e533ebefc0af2f132d4a47340cee6d22cac7190022d \
+    --hash=sha256:7d5aaac37d19b2904bb9dfe12cdb08c8443e7ba7d2852894ad448d4b8f442863 \
+    --hash=sha256:801e9264d19643548651b9db361ce3287176671fb0117f96b5ac0ee1c3530d53 \
+    --hash=sha256:848cd2a1df56ddbffeb375535fb62c9d1645dde33ca4d51341378b3f5954429b \
+    --hash=sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad \
+    --hash=sha256:a3a6ed1d525bfb91b3fc9b690c5a21bb52de28c018530ad85093cc488bee2dd2 \
+    --hash=sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e \
+    --hash=sha256:b8477c1ee4bd47c57d49621a062121c3023609f7a13b8a46953eb6c9716ca392 \
+    --hash=sha256:bac8d525a8dbc2a1507ec731d2867025d11ceadcb4dd421423a5d42c56818541 \
+    --hash=sha256:c38c9ddb6103ceae4e4498f9c08fac9b590c5c71b0370f98714768e22ac6fa66 \
+    --hash=sha256:d5ff2c858f5f6a42c2a8e751100f237c5e869cbde669a724f2062d4c4ef93551 \
+    --hash=sha256:d9e09c9d74f4566e905a0b8fa668c58109f7624db96a2171f21747abc7524234 \
+    --hash=sha256:e516dc8baf7b380e6c1c26792610230f37147bb754d6426462ab115a02944385

src/encodechka/about.py

@@ -28,21 +28,20 @@ INTRODUCTION_TEXT = """
 <a href="https://github.com/avidale/encodechka">Оригинальный репозиторий GitHub</a>
 
 Задачи
-- Semantic text similarity (**STS**) на основе переведённого датасета 
+- Semantic text similarity (**STS**) на основе переведённого датасета
 [STS-B](https://huggingface.co/datasets/stsb_multi_mt);
 - Paraphrase identification (**PI**) на основе датасета paraphraser.ru;
 - Natural language inference (**NLI**) на датасете [XNLI](https://github.com/facebookresearch/XNLI);
 - Sentiment analysis (**SA**) на данных [SentiRuEval2016](http://www.dialog-21.ru/evaluation/2016/sentiment/).
-- Toxicity identification (**TI**) на датасете токсичных комментариев из 
+- Toxicity identification (**TI**) на датасете токсичных комментариев из
 [OKMLCup](https://cups.mail.ru/ru/contests/okmlcup2020);
-- Inappropriateness identification (**II**) на 
+- Inappropriateness identification (**II**) на
 [датасете Сколтеха](https://github.com/skoltech-nlp/inappropriate-sensitive-topics);
-- Intent classification (**IC**) и её кросс-язычная версия **ICX** на датасете 
-[NLU-evaluation-data](https://github.com/xliuhw/NLU-Evaluation-Data), который я автоматически перевёл на русский. 
+- Intent classification (**IC**) и её кросс-язычная версия **ICX** на датасете
+[NLU-evaluation-data](https://github.com/xliuhw/NLU-Evaluation-Data), который я автоматически перевёл на русский.
 В IC классификатор обучается на русских данных, а в ICX – на английских, а тестируется в обоих случаях на русских.
-- Распознавание именованных сущностей на датасетах 
-[factRuEval-2016](https://github.com/dialogue-evaluation/factRuEval-2016) (**NE1**) и 
-[RuDReC](https://github.com/cimm-kzn/RuDReC) (**NE2**). Эти две задачи требуют получать эмбеддинги отдельных токенов, 
+- Распознавание именованных сущностей на датасетах
+[factRuEval-2016](https://github.com/dialogue-evaluation/factRuEval-2016) (**NE1**) и
+[RuDReC](https://github.com/cimm-kzn/RuDReC) (**NE2**). Эти две задачи требуют получать эмбеддинги отдельных токенов,
 а не целых предложений; поэтому там участвуют не все модели.
 """
-

src/encodechka/app.py

@@ -1,28 +1,16 @@
 import gradio as gr
 import pandas as pd
-from about import (
-    INTRODUCTION_TEXT,
-    TITLE,
-)
+from about import INTRODUCTION_TEXT, TITLE
 from apscheduler.schedulers.background import BackgroundScheduler
 from display.css_html_js import custom_css
-from display.utils import (
-    COLS,
-    TYPES,
-    AutoEvalColumn,
-    fields,
-)
-
+from display.utils import COLS, TYPES, AutoEvalColumn, fields
 from parser import update_leaderboard_table
 from populate import get_leaderboard_df
-from settings import (
-    get_settings,
-)
+from settings import get_settings
 
 settings = get_settings()
 
 
-
 def filter_table(
     hidden_df: pd.DataFrame,
     columns: list,
@@ -87,7 +75,7 @@ def get_leaderboard() -> gr.TabItem:
                 with gr.Row():
                     search_bar = gr.Textbox(
                         placeholder=" 🔍 Search for your model (separate multiple queries with `;`) "
-                                    "and press ENTER...",
+                        "and press ENTER...",
                         show_label=False,
                         elem_id="search-bar",
                     )

src/encodechka/display/utils.py

@@ -1,7 +1,5 @@
 from dataclasses import dataclass, make_dataclass
-from enum import Enum
 
-import pandas as pd
 from about import Tasks
 
 
@@ -27,9 +25,8 @@ auto_eval_column_dict = [
         ColumnContent,
         ColumnContent("model", "markdown", True, never_hidden=True),
     ),
-    (
-        "CPU", ColumnContent, ColumnContent("CPU", "number", True)
-    ), ("GPU", ColumnContent, ColumnContent("GPU", "number", True)),
+    ("CPU", ColumnContent, ColumnContent("CPU", "number", True)),
+    ("GPU", ColumnContent, ColumnContent("GPU", "number", True)),
     ("size", ColumnContent, ColumnContent("size", "number", True)),
     ("MeanS", ColumnContent, ColumnContent("Mean S", "number", True)),
     ("MeanSW", ColumnContent, ColumnContent("Mean S+W", "number", True)),
@@ -44,7 +41,11 @@ auto_eval_column_dict = [
     ("ICX", ColumnContent, ColumnContent("ICX", "number", True)),
     ("NE1", ColumnContent, ColumnContent("NE1", "number", True)),
     ("NE2", ColumnContent, ColumnContent("NE2", "number", True)),
-    ("is_private", ColumnContent, ColumnContent("is_private", "boolean", True, hidden=True)),
+    (
+        "is_private",
+        ColumnContent,
+        ColumnContent("is_private", "boolean", True, hidden=True),
+    ),
 ]
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)

src/encodechka/parser.py

@@ -1,7 +1,7 @@
 from io import StringIO
 
-import pandas as pd
 import markdown
+import pandas as pd
 import requests
 from settings import get_settings
 
@@ -13,7 +13,7 @@ def get_readme() -> str:
 
 
 def get_readme_html() -> str:
-    return markdown.markdown(get_readme(), extensions=['tables'])
+    return markdown.markdown(get_readme(), extensions=["tables"])
 
 
 def get_readme_df() -> pd.DataFrame:

src/encodechka/populate.py

@@ -1,5 +1,4 @@
 import pandas as pd
-
 from display.formatting import make_clickable_model
 from display.utils import AutoEvalColumn
 from settings import Settings

src/encodechka/settings.py

@@ -1,6 +1,5 @@
 import os
 
-from huggingface_hub import HfApi
 from pydantic_settings import BaseSettings
 
 

tests/test_parser.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import pytest
+
 from src.encodechka import parser
 
 
@@ -7,4 +8,4 @@ from src.encodechka import parser
 def test_parser():
     df = parser.get_readme_df()
     assert isinstance(df, pd.DataFrame)
-    assert df.shape[1] == 16
+    assert df.shape[1] == 16