polyglot-tagger/language-identification
058
1---2library_name: transformers3license: mit4base_model: xlm-roberta-base5tags:6- generated_from_trainer7- language-identification8- codeswitching9metrics:10- precision11- recall12- f113- accuracy14language:15- multilingual16- af17- am18- ar19- as20- ba21- be22- bg23- bn24- bo25- br26- bs27- ca28- ce29- ckb30- cs31- cy32- da33- de34- dv35- el36- en37- eo38- es39- et40- eu41- fa42- fi43- fr44- ga45- gd46- gl47- gu48- he49- hi50- hr51- hu52- hy53- id54- is55- it56- ja57- jv58- ka59- kk60- km61- kn62- ko63- ku64- ky65- la66- lb67- lo68- lt69- lv70- mg71- mk72- ml73- mn74- mr75- ms76- mt77- my78- ne79- nl80- 'no'81- ny82- oc83- om84- or85- pa86- pl87- ps88- pt89- rm90- ro91- ru92- sd93- si94- sk95- sl96- so97- sq98- sr99- su100- sv101- sw102- ta103- te104- tg105- th106- ti107- tl108- tr109- tt110- ug111- uk112- ur113- uz114- vi115- yo116- yi117- zh118- zu119model-index:120- name: polyglot-tagger121 results: []122datasets:123- wikimedia/wikipedia124- HuggingFaceFW/finetranslations125- google/smol126- polyglot-tagger/nlp-noise-snippets127- polyglot-tagger/wikipedia-language-snippets-filtered128- polyglot-tagger/finetranslations-filtered129- polyglot-tagger/tatoeba-filtered130pipeline_tag: token-classification131---132 133 134135 136 137Fine-tuned `xlm-roberta-base` for sentence-level language tagging across 100 languages.138The model predicts BIO-style language tags over tokens, which makes it useful for139language identification, code-switch detection, and multilingual document analysis.140 141> Compared to version 2.2, this version had training data that attempted to fix the model scoring common grade-school words from major langauges as low confidence or in a minor language bucket.142 143## Model description144 145Introducing Polyglot Tagger, a new way to classify multi-lingual documents. By training specifically on token classification on individual sentences, the model 146generalizes well on a variety of languages, while also behaves as a multi-label classifier, and extracts sentences based on its language. 147 148## Intended uses & limitations149Note that as a general language tagging model, it can potentially get confused from shared language families or from short texts. For example, Danish and Norwegian, Spanish and Portuguese, and Russian and Ukrainian.150 151The model is trained on a sentence with a minimum of four tokens, so it may not accurately classify very short and ambigous statements. Note that this model is experimental152and may produce unexpected results compared to generic text classifiers. It is trained on cleaned text, therefore, "messy" text may unexpectedly produce different results.153 154> Note that Romanized versions of any language may have no representation in the training set, such as Romanized Russian, and Hindi.155 156### Training and Evaluation Data157 158A synthetic training row consists of 1-6 individual and mostly independent sentences extracted from various sources. To generalize well against multiple languages, several159factors were used to simulate messy text, and to reduce single character bias on certain languages:160- Low chance of deliberate accent stripping for languages such as Spanish and Portugeuse161- Random chance to add in, replace or delete punctuation, numeric, and delimiter artifiacts162- Insert same-script alphabets to family language. For example, randomly injecting Arabic characters in Arabic languages163- Random chance to change the casing of compatible language scripts, such as Latin and Cyrllic.164- Low chance of simulating OCR and messy text with character mutation.165 166To generalize well on both the target language and code switching a curriculum is provided:167- Pure documents 55%: Single language to learn its vocabulary, simulating a short paragraph of a single language.168- Homogenous 25%: Single language + one foreign sentence to learn simple code switching.169- Spliced 10%: A foreign sentence is centered between two same-language sentence, with the first sentence's punctuation stripped, and second sentence's forced to be lowercased.170- Mixed 10%: Generic mix of any languages.171 172 173 174 175| lang | train sentences | train tokens | eval sentences | eval tokens | all sentences | all tokens |176| :--- | ---: | ---: | ---: | ---: | ---: | ---: |177| en | 423264 (2.41%) | 9841704 (1.71%) | 3157 (3.84%) | 35025 (1.79%) | 426421 (2.41%) | 9876729 (1.71%) |178| es | 359106 (2.04%) | 9729675 (1.69%) | 2201 (2.68%) | 22340 (1.14%) | 361307 (2.04%) | 9752015 (1.69%) |179| ru | 356083 (2.02%) | 8945224 (1.56%) | 2226 (2.71%) | 21578 (1.10%) | 358309 (2.03%) | 8966802 (1.55%) |180| fr | 354645 (2.02%) | 10591338 (1.84%) | 2213 (2.69%) | 26148 (1.34%) | 356858 (2.02%) | 10617486 (1.84%) |181| ja | 352243 (2.00%) | 7945312 (1.38%) | 2219 (2.70%) | 25849 (1.32%) | 354462 (2.01%) | 7971161 (1.38%) |182| pt | 346042 (1.97%) | 8793674 (1.53%) | 2059 (2.50%) | 20881 (1.07%) | 348101 (1.97%) | 8814555 (1.53%) |183| de | 344644 (1.96%) | 8847457 (1.54%) | 2151 (2.61%) | 24958 (1.27%) | 346795 (1.96%) | 8872415 (1.54%) |184| it | 343887 (1.95%) | 8790806 (1.53%) | 2000 (2.43%) | 17342 (0.89%) | 345887 (1.96%) | 8808148 (1.53%) |185| fi | 299568 (1.70%) | 6905536 (1.20%) | 1576 (1.92%) | 14458 (0.74%) | 301144 (1.70%) | 6919994 (1.20%) |186| uk | 297565 (1.69%) | 7228987 (1.26%) | 1398 (1.70%) | 11391 (0.58%) | 298963 (1.69%) | 7240378 (1.26%) |187| zh | 294064 (1.67%) | 7329413 (1.28%) | 1717 (2.09%) | 33433 (1.71%) | 295781 (1.67%) | 7362846 (1.28%) |188| tr | 289328 (1.64%) | 6606625 (1.15%) | 1384 (1.68%) | 11089 (0.57%) | 290712 (1.64%) | 6617714 (1.15%) |189| he | 289239 (1.64%) | 7792338 (1.36%) | 1100 (1.34%) | 10342 (0.53%) | 290339 (1.64%) | 7802680 (1.35%) |190| pl | 288423 (1.64%) | 7707293 (1.34%) | 1305 (1.59%) | 11306 (0.58%) | 289728 (1.64%) | 7718599 (1.34%) |191| hu | 286880 (1.63%) | 7547282 (1.31%) | 1232 (1.50%) | 11115 (0.57%) | 288112 (1.63%) | 7558397 (1.31%) |192| nl | 280682 (1.60%) | 6057971 (1.05%) | 1296 (1.58%) | 10453 (0.53%) | 281978 (1.60%) | 6068424 (1.05%) |193| lt | 273386 (1.55%) | 6507776 (1.13%) | 1165 (1.42%) | 10491 (0.54%) | 274551 (1.55%) | 6518267 (1.13%) |194| eo | 267885 (1.52%) | 6554654 (1.14%) | 1055 (1.28%) | 13271 (0.68%) | 268940 (1.52%) | 6567925 (1.14%) |195| ar | 257437 (1.46%) | 5125573 (0.89%) | 1327 (1.61%) | 15180 (0.78%) | 258764 (1.46%) | 5140753 (0.89%) |196| cs | 240507 (1.37%) | 6406086 (1.11%) | 1082 (1.32%) | 9473 (0.48%) | 241589 (1.37%) | 6415559 (1.11%) |197| mk | 231103 (1.31%) | 6478376 (1.13%) | 953 (1.16%) | 7713 (0.39%) | 232056 (1.31%) | 6486089 (1.12%) |198| mr | 228596 (1.30%) | 5886608 (1.02%) | 776 (0.94%) | 6332 (0.32%) | 229372 (1.30%) | 5892940 (1.02%) |199| no | 223605 (1.27%) | 6137131 (1.07%) | 1396 (1.70%) | 40226 (2.05%) | 225001 (1.27%) | 6177357 (1.07%) |200| da | 222243 (1.26%) | 5375746 (0.94%) | 1201 (1.46%) | 10373 (0.53%) | 223444 (1.26%) | 5386119 (0.93%) |201| hy | 207937 (1.18%) | 6345675 (1.10%) | 791 (0.96%) | 9276 (0.47%) | 208728 (1.18%) | 6354951 (1.10%) |202| tl | 207674 (1.18%) | 5561702 (0.97%) | 1017 (1.24%) | 10926 (0.56%) | 208691 (1.18%) | 5572628 (0.97%) |203| hi | 206552 (1.17%) | 7796062 (1.36%) | 1079 (1.31%) | 47351 (2.42%) | 207631 (1.17%) | 7843413 (1.36%) |204| ko | 205625 (1.17%) | 6481034 (1.13%) | 1156 (1.41%) | 32355 (1.65%) | 206781 (1.17%) | 6513389 (1.13%) |205| el | 202334 (1.15%) | 7105554 (1.24%) | 826 (1.00%) | 13412 (0.68%) | 203160 (1.15%) | 7118966 (1.23%) |206| ro | 194999 (1.11%) | 6206913 (1.08%) | 820 (1.00%) | 14575 (0.74%) | 195819 (1.11%) | 6221488 (1.08%) |207| fa | 192050 (1.09%) | 5728246 (1.00%) | 696 (0.85%) | 14765 (0.75%) | 192746 (1.09%) | 5743011 (1.00%) |208| sk | 189330 (1.08%) | 5318617 (0.93%) | 873 (1.06%) | 20779 (1.06%) | 190203 (1.08%) | 5339396 (0.93%) |209| la | 188201 (1.07%) | 4591159 (0.80%) | 824 (1.00%) | 8557 (0.44%) | 189025 (1.07%) | 4599716 (0.80%) |210| bg | 187685 (1.07%) | 5860353 (1.02%) | 762 (0.93%) | 16804 (0.86%) | 188447 (1.07%) | 5877157 (1.02%) |211| be | 181543 (1.03%) | 6528657 (1.14%) | 869 (1.06%) | 25944 (1.32%) | 182412 (1.03%) | 6554601 (1.14%) |212| is | 180452 (1.03%) | 6146455 (1.07%) | 959 (1.17%) | 39591 (2.02%) | 181411 (1.03%) | 6186046 (1.07%) |213| lv | 179142 (1.02%) | 5867897 (1.02%) | 762 (0.93%) | 33481 (1.71%) | 179904 (1.02%) | 5901378 (1.02%) |214| ckb | 174282 (0.99%) | 7825141 (1.36%) | 667 (0.81%) | 28756 (1.47%) | 174949 (0.99%) | 7853897 (1.36%) |215| ms | 172573 (0.98%) | 4614764 (0.80%) | 815 (0.99%) | 24769 (1.26%) | 173388 (0.98%) | 4639533 (0.80%) |216| ka | 170876 (0.97%) | 5505127 (0.96%) | 673 (0.82%) | 20651 (1.05%) | 171549 (0.97%) | 5525778 (0.96%) |217| kk | 170695 (0.97%) | 5132560 (0.89%) | 676 (0.82%) | 18695 (0.95%) | 171371 (0.97%) | 5151255 (0.89%) |218| bn | 170721 (0.97%) | 6393448 (1.11%) | 441 (0.54%) | 14727 (0.75%) | 171162 (0.97%) | 6408175 (1.11%) |219| eu | 168462 (0.96%) | 5737310 (1.00%) | 746 (0.91%) | 37196 (1.90%) | 169208 (0.96%) | 5774506 (1.00%) |220| as | 168746 (0.96%) | 8564682 (1.49%) | 445 (0.54%) | 24444 (1.25%) | 169191 (0.96%) | 8589126 (1.49%) |221| mn | 167543 (0.95%) | 5678049 (0.99%) | 703 (0.85%) | 20347 (1.04%) | 168246 (0.95%) | 5698396 (0.99%) |222| ur | 165992 (0.94%) | 5361179 (0.93%) | 684 (0.83%) | 22622 (1.16%) | 166676 (0.94%) | 5383801 (0.93%) |223| oc | 165863 (0.94%) | 5735536 (1.00%) | 730 (0.89%) | 18599 (0.95%) | 166593 (0.94%) | 5754135 (1.00%) |224| ba | 164919 (0.94%) | 8387828 (1.46%) | 699 (0.85%) | 35927 (1.83%) | 165618 (0.94%) | 8423755 (1.46%) |225| th | 164429 (0.93%) | 5495248 (0.96%) | 649 (0.79%) | 22113 (1.13%) | 165078 (0.93%) | 5517361 (0.96%) |226| ky | 164374 (0.93%) | 5199548 (0.90%) | 683 (0.83%) | 18956 (0.97%) | 165057 (0.93%) | 5218504 (0.90%) |227| hr | 163828 (0.93%) | 5183677 (0.90%) | 711 (0.86%) | 33845 (1.73%) | 164539 (0.93%) | 5217522 (0.90%) |228| ps | 163238 (0.93%) | 4735113 (0.82%) | 674 (0.82%) | 18515 (0.95%) | 163912 (0.93%) | 4753628 (0.82%) |229| id | 163187 (0.93%) | 4025079 (0.70%) | 723 (0.88%) | 13371 (0.68%) | 163910 (0.93%) | 4038450 (0.70%) |230| pa | 162180 (0.92%) | 7621059 (1.33%) | 581 (0.71%) | 29036 (1.48%) | 162761 (0.92%) | 7650095 (1.33%) |231| sw | 161777 (0.92%) | 5013161 (0.87%) | 653 (0.79%) | 26493 (1.35%) | 162430 (0.92%) | 5039654 (0.87%) |232| af | 160455 (0.91%) | 4676798 (0.81%) | 932 (1.13%) | 27369 (1.40%) | 161387 (0.91%) | 4704167 (0.82%) |233| jv | 156292 (0.89%) | 4752381 (0.83%) | 576 (0.70%) | 22573 (1.15%) | 156868 (0.89%) | 4774954 (0.83%) |234| tt | 154833 (0.88%) | 5165763 (0.90%) | 578 (0.70%) | 7298 (0.37%) | 155411 (0.88%) | 5173061 (0.90%) |235| cy | 153551 (0.87%) | 5656404 (0.98%) | 653 (0.79%) | 29503 (1.51%) | 154204 (0.87%) | 5685907 (0.99%) |236| ga | 150458 (0.86%) | 5488243 (0.95%) | 680 (0.83%) | 33471 (1.71%) | 151138 (0.86%) | 5521714 (0.96%) |237| kn | 150184 (0.85%) | 14992479 (2.61%) | 697 (0.85%) | 49288 (2.52%) | 150881 (0.85%) | 15041767 (2.61%) |238| bs | 150037 (0.85%) | 4582900 (0.80%) | 649 (0.79%) | 25588 (1.31%) | 150686 (0.85%) | 4608488 (0.80%) |239| ca | 149401 (0.85%) | 5477662 (0.95%) | 629 (0.76%) | 21391 (1.09%) | 150030 (0.85%) | 5499053 (0.95%) |240| ne | 148716 (0.85%) | 4855198 (0.84%) | 535 (0.65%) | 16246 (0.83%) | 149251 (0.84%) | 4871444 (0.84%) |241| ku | 147702 (0.84%) | 4973601 (0.87%) | 574 (0.70%) | 28196 (1.44%) | 148276 (0.84%) | 5001797 (0.87%) |242| gl | 147011 (0.84%) | 4554907 (0.79%) | 658 (0.80%) | 20127 (1.03%) | 147669 (0.84%) | 4575034 (0.79%) |243| uz | 145433 (0.83%) | 4704898 (0.82%) | 573 (0.70%) | 21862 (1.12%) | 146006 (0.83%) | 4726760 (0.82%) |244| sl | 144084 (0.82%) | 3851696 (0.67%) | 651 (0.79%) | 18164 (0.93%) | 144735 (0.82%) | 3869860 (0.67%) |245| sv | 143041 (0.81%) | 4006332 (0.70%) | 905 (1.10%) | 7012 (0.36%) | 143946 (0.81%) | 4013344 (0.70%) |246| tg | 136703 (0.78%) | 7664329 (1.33%) | 572 (0.70%) | 34220 (1.75%) | 137275 (0.78%) | 7698549 (1.33%) |247| et | 131007 (0.74%) | 3280590 (0.57%) | 549 (0.67%) | 14021 (0.72%) | 131556 (0.74%) | 3294611 (0.57%) |248| br | 130223 (0.74%) | 4495403 (0.78%) | 546 (0.66%) | 17304 (0.88%) | 130769 (0.74%) | 4512707 (0.78%) |249| lb | 129528 (0.74%) | 4421411 (0.77%) | 495 (0.60%) | 17761 (0.91%) | 130023 (0.74%) | 4439172 (0.77%) |250| su | 129144 (0.73%) | 4215719 (0.73%) | 535 (0.65%) | 21391 (1.09%) | 129679 (0.73%) | 4237110 (0.73%) |251| mt | 128626 (0.73%) | 6671441 (1.16%) | 508 (0.62%) | 26729 (1.36%) | 129134 (0.73%) | 6698170 (1.16%) |252| sq | 119431 (0.68%) | 4107917 (0.71%) | 561 (0.68%) | 18633 (0.95%) | 119992 (0.68%) | 4126550 (0.72%) |253| sr | 117855 (0.67%) | 3160599 (0.55%) | 427 (0.52%) | 3505 (0.18%) | 118282 (0.67%) | 3164104 (0.55%) |254| or | 110709 (0.63%) | 3922431 (0.68%) | 410 (0.50%) | 13094 (0.67%) | 111119 (0.63%) | 3935525 (0.68%) |255| ml | 110085 (0.63%) | 10929013 (1.90%) | 464 (0.56%) | 36922 (1.89%) | 110549 (0.63%) | 10965935 (1.90%) |256| yi | 104494 (0.59%) | 4085563 (0.71%) | 400 (0.49%) | 6005 (0.31%) | 104894 (0.59%) | 4091568 (0.71%) |257| te | 101076 (0.57%) | 9757033 (1.70%) | 430 (0.52%) | 37897 (1.94%) | 101506 (0.57%) | 9794930 (1.70%) |258| ta | 94122 (0.53%) | 7917169 (1.38%) | 386 (0.47%) | 26610 (1.36%) | 94508 (0.53%) | 7943779 (1.38%) |259| mg | 93939 (0.53%) | 3291017 (0.57%) | 391 (0.48%) | 11698 (0.60%) | 94330 (0.53%) | 3302715 (0.57%) |260| si | 92723 (0.53%) | 5275463 (0.92%) | 364 (0.44%) | 18426 (0.94%) | 93087 (0.53%) | 5293889 (0.92%) |261| vi | 74916 (0.43%) | 2535825 (0.44%) | 335 (0.41%) | 3396 (0.17%) | 75251 (0.43%) | 2539221 (0.44%) |262| rm | 74806 (0.43%) | 2826708 (0.49%) | 318 (0.39%) | 12654 (0.65%) | 75124 (0.43%) | 2839362 (0.49%) |263| gu | 70961 (0.40%) | 7859622 (1.37%) | 335 (0.41%) | 28389 (1.45%) | 71296 (0.40%) | 7888011 (1.37%) |264| bo | 69565 (0.40%) | 1378245 (0.24%) | 263 (0.32%) | 5407 (0.28%) | 69828 (0.40%) | 1383652 (0.24%) |265| ug | 64297 (0.37%) | 1427585 (0.25%) | 260 (0.32%) | 4769 (0.24%) | 64557 (0.37%) | 1432354 (0.25%) |266| dv | 60328 (0.34%) | 1557497 (0.27%) | 215 (0.26%) | 5844 (0.30%) | 60543 (0.34%) | 1563341 (0.27%) |267| am | 59339 (0.34%) | 2705311 (0.47%) | 235 (0.29%) | 10768 (0.55%) | 59574 (0.34%) | 2716079 (0.47%) |268| yo | 59246 (0.34%) | 3649130 (0.63%) | 260 (0.32%) | 21157 (1.08%) | 59506 (0.34%) | 3670287 (0.64%) |269| my | 58575 (0.33%) | 2165089 (0.38%) | 214 (0.26%) | 8142 (0.42%) | 58789 (0.33%) | 2173231 (0.38%) |270| km | 57081 (0.32%) | 3056236 (0.53%) | 193 (0.23%) | 10606 (0.54%) | 57274 (0.32%) | 3066842 (0.53%) |271| so | 56160 (0.32%) | 2044409 (0.36%) | 212 (0.26%) | 8847 (0.45%) | 56372 (0.32%) | 2053256 (0.36%) |272| sd | 55359 (0.31%) | 3226018 (0.56%) | 217 (0.26%) | 10847 (0.55%) | 55576 (0.31%) | 3236865 (0.56%) |273| zu | 52465 (0.30%) | 2406841 (0.42%) | 203 (0.25%) | 9751 (0.50%) | 52668 (0.30%) | 2416592 (0.42%) |274| lo | 50641 (0.29%) | 1747495 (0.30%) | 189 (0.23%) | 6221 (0.32%) | 50830 (0.29%) | 1753716 (0.30%) |275| ti | 47785 (0.27%) | 2895617 (0.50%) | 195 (0.24%) | 12699 (0.65%) | 47980 (0.27%) | 2908316 (0.50%) |276| ce | 45014 (0.26%) | 2425219 (0.42%) | 188 (0.23%) | 9950 (0.51%) | 45202 (0.26%) | 2435169 (0.42%) |277| ny | 43552 (0.25%) | 2051132 (0.36%) | 171 (0.21%) | 8286 (0.42%) | 43723 (0.25%) | 2059418 (0.36%) |278| gd | 36623 (0.21%) | 1273243 (0.22%) | 156 (0.19%) | 3615 (0.18%) | 36779 (0.21%) | 1276858 (0.22%) |279| xh | 24432 (0.14%) | 911850 (0.16%) | 93 (0.11%) | 3528 (0.18%) | 24525 (0.14%) | 915378 (0.16%) |280| om | 15372 (0.09%) | 545603 (0.09%) | 77 (0.09%) | 2564 (0.13%) | 15449 (0.09%) | 548167 (0.10%) |281| sco | 8772 (0.05%) | 233030 (0.04%) | 37 (0.04%) | 828 (0.04%) | 8809 (0.05%) | 233858 (0.04%) |282| **total** | 17593786 (100.00%) | 574735483 (100.00%) | 82270 (100.00%) | 1958217 (100.00%) | 17676056 (100.00%) | 576693700 (100.00%) |283 284 285This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.286It achieves the following results on the evaluation set:287- Loss: 0.0306288- Precision: 0.9507289- Recall: 0.9644290- F1: 0.9575291- Accuracy: 0.9917292 293## Training procedure294 295### Training hyperparameters296 297The following hyperparameters were used during training:298- learning_rate: 5e-05299- train_batch_size: 72300- eval_batch_size: 36301- seed: 42302- gradient_accumulation_steps: 2303- total_train_batch_size: 144304- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments305- lr_scheduler_type: linear306- num_epochs: 2307- mixed_precision_training: Native AMP308 309### Training results310 311| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |312|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|313| 0.0918 | 0.0731 | 2500 | 0.1050 | 0.7984 | 0.8818 | 0.8381 | 0.9735 |314| 0.0717 | 0.1463 | 5000 | 0.0797 | 0.8393 | 0.9041 | 0.8705 | 0.9782 |315| 0.0624 | 0.2194 | 7500 | 0.0762 | 0.8664 | 0.9166 | 0.8908 | 0.9804 |316| 0.0562 | 0.2925 | 10000 | 0.0620 | 0.8758 | 0.9247 | 0.8995 | 0.9830 |317| 0.0516 | 0.3657 | 12500 | 0.0576 | 0.8844 | 0.9298 | 0.9065 | 0.9845 |318| 0.0465 | 0.4388 | 15000 | 0.0543 | 0.8993 | 0.9357 | 0.9172 | 0.9857 |319| 0.0433 | 0.5119 | 17500 | 0.0558 | 0.9005 | 0.9356 | 0.9177 | 0.9856 |320| 0.0411 | 0.5851 | 20000 | 0.0499 | 0.9012 | 0.9385 | 0.9195 | 0.9867 |321| 0.0420 | 0.6582 | 22500 | 0.0460 | 0.9167 | 0.9438 | 0.9300 | 0.9873 |322| 0.0392 | 0.7313 | 25000 | 0.0441 | 0.9149 | 0.9448 | 0.9296 | 0.9878 |323| 0.0386 | 0.8045 | 27500 | 0.0434 | 0.9200 | 0.9476 | 0.9336 | 0.9885 |324| 0.0357 | 0.8776 | 30000 | 0.0422 | 0.9235 | 0.9503 | 0.9367 | 0.9886 |325| 0.0356 | 0.9507 | 32500 | 0.0404 | 0.9272 | 0.9520 | 0.9395 | 0.9890 |326| 0.0261 | 1.0238 | 35000 | 0.0381 | 0.9293 | 0.9529 | 0.9409 | 0.9898 |327| 0.0322 | 1.0970 | 37500 | 0.0371 | 0.9346 | 0.9558 | 0.9451 | 0.9899 |328| 0.0303 | 1.1701 | 40000 | 0.0374 | 0.9375 | 0.9580 | 0.9476 | 0.9903 |329| 0.0276 | 1.2432 | 42500 | 0.0378 | 0.9355 | 0.9566 | 0.9460 | 0.9901 |330| 0.0264 | 1.3164 | 45000 | 0.0353 | 0.9373 | 0.9574 | 0.9472 | 0.9904 |331| 0.0228 | 1.3895 | 47500 | 0.0366 | 0.9398 | 0.9589 | 0.9493 | 0.9903 |332| 0.0234 | 1.4626 | 50000 | 0.0343 | 0.9430 | 0.9602 | 0.9516 | 0.9907 |333| 0.0274 | 1.5358 | 52500 | 0.0339 | 0.9396 | 0.9591 | 0.9492 | 0.9906 |334| 0.0236 | 1.6089 | 55000 | 0.0324 | 0.9438 | 0.9613 | 0.9525 | 0.9913 |335| 0.0244 | 1.6820 | 57500 | 0.0322 | 0.9478 | 0.9624 | 0.9551 | 0.9914 |336| 0.0222 | 1.7552 | 60000 | 0.0323 | 0.9483 | 0.9628 | 0.9555 | 0.9914 |337| 0.0238 | 1.8283 | 62500 | 0.0320 | 0.9480 | 0.9630 | 0.9554 | 0.9913 |338| 0.0223 | 1.9014 | 65000 | 0.0320 | 0.9485 | 0.9637 | 0.9560 | 0.9913 |339| 0.0208 | 1.9746 | 67500 | 0.0306 | 0.9507 | 0.9644 | 0.9575 | 0.9917 |340 341 342### Framework versions343 344- Transformers 5.0.0345- Pytorch 2.10.0+cu128346- Datasets 4.0.0347- Tokenizers 0.22.2348