trainer: training complete at 2024-04-23 14:17:17.109666.

README.md

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: train[80%:100%]
+      split: train[0%:20%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9467033563850972
+      value: 0.9275243744460353
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +32,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4604
-- B: {'precision': 0.8831908831908832, 'recall': 0.8916586768935763, 'f1-score': 0.8874045801526719, 'support': 1043.0}
-- I: {'precision': 0.9586151553364668, 'recall': 0.9639193083573487, 'f1-score': 0.9612599149327509, 'support': 17350.0}
-- O: {'precision': 0.93125, 'recall': 0.9205506178192066, 'f1-score': 0.9258693993241034, 'support': 9226.0}
-- Accuracy: 0.9467
-- Macro avg: {'precision': 0.92435201284245, 'recall': 0.9253762010233771, 'f1-score': 0.924844631469842, 'support': 27619.0}
-- Weighted avg: {'precision': 0.9466256394603638, 'recall': 0.9467033563850972, 'f1-score': 0.9466488134742982, 'support': 27619.0}
+- Loss: 0.5306
+- B: {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0}
+- I: {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0}
+- O: {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0}
+- Accuracy: 0.9275
+- Macro avg: {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0}
+- Weighted avg: {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0}
 
 ## Model description
 
@@ -63,62 +63,32 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 50
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
 |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 81   | 0.2048          | {'precision': 0.7901977644024075, 'recall': 0.8811121764141898, 'f1-score': 0.8331822302810515, 'support': 1043.0} | {'precision': 0.9443616777446711, 'recall': 0.9499135446685879, 'f1-score': 0.9471294753175105, 'support': 17350.0} | {'precision': 0.9103731674811195, 'recall': 0.8884673748103187, 'f1-score': 0.8992868897421833, 'support': 9226.0} | 0.9268   | {'precision': 0.8816442032093993, 'recall': 0.9064976986310321, 'f1-score': 0.8931995317802484, 'support': 27619.0} | {'precision': 0.9271861479533134, 'recall': 0.9267895289474637, 'f1-score': 0.9268447919078652, 'support': 27619.0} |
-| No log        | 2.0   | 162  | 0.1657          | {'precision': 0.8806706114398422, 'recall': 0.8561840843720039, 'f1-score': 0.8682547399124939, 'support': 1043.0} | {'precision': 0.9592919333448654, 'recall': 0.9589048991354466, 'f1-score': 0.9590983771942466, 'support': 17350.0} | {'precision': 0.9208594256100194, 'recall': 0.9244526338608281, 'f1-score': 0.9226525313717006, 'support': 9226.0} | 0.9435   | {'precision': 0.9202739901315756, 'recall': 0.9131805391227594, 'f1-score': 0.9166685494928136, 'support': 27619.0} | {'precision': 0.9434846863370583, 'recall': 0.9435171439950758, 'f1-score': 0.9434932036816766, 'support': 27619.0} |
-| No log        | 3.0   | 243  | 0.1539          | {'precision': 0.8625461254612546, 'recall': 0.8964525407478428, 'f1-score': 0.8791725434884815, 'support': 1043.0} | {'precision': 0.9557349825345015, 'recall': 0.9619596541786744, 'f1-score': 0.9588372159825352, 'support': 17350.0} | {'precision': 0.9282407407407407, 'recall': 0.9127465857359636, 'f1-score': 0.9204284621270084, 'support': 9226.0} | 0.9430   | {'precision': 0.9155072829121655, 'recall': 0.9237195935541602, 'f1-score': 0.9194794071993417, 'support': 27619.0} | {'precision': 0.9430314866542512, 'recall': 0.9430464535283681, 'f1-score': 0.9429985029052194, 'support': 27619.0} |
-| No log        | 4.0   | 324  | 0.1980          | {'precision': 0.8572727272727273, 'recall': 0.9041227229146692, 'f1-score': 0.8800746616892207, 'support': 1043.0} | {'precision': 0.9488876212207644, 'recall': 0.9587319884726225, 'f1-score': 0.9537844036697248, 'support': 17350.0} | {'precision': 0.9220157970853265, 'recall': 0.8983308042488619, 'f1-score': 0.9100192149327477, 'support': 9226.0} | 0.9365   | {'precision': 0.9093920485262728, 'recall': 0.9203951718787179, 'f1-score': 0.9146260934305644, 'support': 27619.0} | {'precision': 0.9364514800186444, 'recall': 0.9364929939534379, 'f1-score': 0.9363812792925563, 'support': 27619.0} |
-| No log        | 5.0   | 405  | 0.2007          | {'precision': 0.8847262247838616, 'recall': 0.8830297219558965, 'f1-score': 0.883877159309021, 'support': 1043.0}  | {'precision': 0.9456643513331839, 'recall': 0.9730259365994236, 'f1-score': 0.9591500482927107, 'support': 17350.0} | {'precision': 0.9454503781801513, 'recall': 0.8942120095382614, 'f1-score': 0.9191176470588236, 'support': 9226.0} | 0.9433   | {'precision': 0.9252803180990656, 'recall': 0.9167558893645271, 'f1-score': 0.9207149515535183, 'support': 27619.0} | {'precision': 0.9432916158141273, 'recall': 0.9432999022412107, 'f1-score': 0.9429348139614955, 'support': 27619.0} |
-| No log        | 6.0   | 486  | 0.1657          | {'precision': 0.882186616399623, 'recall': 0.8974113135186961, 'f1-score': 0.8897338403041826, 'support': 1043.0}  | {'precision': 0.9707969272268809, 'recall': 0.954178674351585, 'f1-score': 0.9624160683661308, 'support': 17350.0}  | {'precision': 0.9161493950552341, 'recall': 0.9438543247344461, 'f1-score': 0.9297955261331482, 'support': 9226.0} | 0.9486   | {'precision': 0.9230443128939126, 'recall': 0.9318147708682424, 'f1-score': 0.9273151449344872, 'support': 27619.0} | {'precision': 0.9491959030765336, 'recall': 0.9485861182519281, 'f1-score': 0.9487745648174827, 'support': 27619.0} |
-| 0.1585        | 7.0   | 567  | 0.2591          | {'precision': 0.85, 'recall': 0.912751677852349, 'f1-score': 0.8802588996763754, 'support': 1043.0}                | {'precision': 0.9354661691269275, 'recall': 0.9825360230547551, 'f1-score': 0.958423523458803, 'support': 17350.0}  | {'precision': 0.9664088931851136, 'recall': 0.8668978972469109, 'f1-score': 0.9139526911210146, 'support': 9226.0} | 0.9413   | {'precision': 0.917291687437347, 'recall': 0.9207285327180049, 'f1-score': 0.9175450380853977, 'support': 27619.0}  | {'precision': 0.9425749115781907, 'recall': 0.9412723125384699, 'f1-score': 0.9406164485555297, 'support': 27619.0} |
-| 0.1585        | 8.0   | 648  | 0.2693          | {'precision': 0.84967919340055, 'recall': 0.8887823585810163, 'f1-score': 0.8687910028116214, 'support': 1043.0}   | {'precision': 0.956997878562009, 'recall': 0.9620172910662824, 'f1-score': 0.9595010203788336, 'support': 17350.0}  | {'precision': 0.9282491471332673, 'recall': 0.9142640364188164, 'f1-score': 0.9212035166275324, 'support': 9226.0} | 0.9433   | {'precision': 0.9116420730319422, 'recall': 0.9216878953553717, 'f1-score': 0.9164985132726624, 'support': 27619.0} | {'precision': 0.9433417293609165, 'recall': 0.9432999022412107, 'f1-score': 0.9432823550422136, 'support': 27619.0} |
-| 0.1585        | 9.0   | 729  | 0.3087          | {'precision': 0.8485687903970452, 'recall': 0.8811121764141898, 'f1-score': 0.8645343367826904, 'support': 1043.0} | {'precision': 0.934535260814599, 'recall': 0.9799423631123919, 'f1-score': 0.9567003348057282, 'support': 17350.0}  | {'precision': 0.9586479683567062, 'recall': 0.8668978972469109, 'f1-score': 0.9104673003585861, 'support': 9226.0} | 0.9384   | {'precision': 0.9139173398561168, 'recall': 0.9093174789244975, 'f1-score': 0.910567323982335, 'support': 27619.0}  | {'precision': 0.9393435743356523, 'recall': 0.9384481697382236, 'f1-score': 0.9377758584761231, 'support': 27619.0} |
-| 0.1585        | 10.0  | 810  | 0.2796          | {'precision': 0.897410358565737, 'recall': 0.8638542665388304, 'f1-score': 0.8803126526624329, 'support': 1043.0}  | {'precision': 0.9592919333448654, 'recall': 0.9589048991354466, 'f1-score': 0.9590983771942466, 'support': 17350.0} | {'precision': 0.9197584124245038, 'recall': 0.9243442445263386, 'f1-score': 0.9220456265542221, 'support': 9226.0} | 0.9438   | {'precision': 0.9254869014450354, 'recall': 0.9157011367335386, 'f1-score': 0.9204855521369671, 'support': 27619.0} | {'precision': 0.9437490553802075, 'recall': 0.9437705927079184, 'f1-score': 0.9437458232244597, 'support': 27619.0} |
-| 0.1585        | 11.0  | 891  | 0.3198          | {'precision': 0.8899707887049659, 'recall': 0.8763183125599233, 'f1-score': 0.8830917874396135, 'support': 1043.0} | {'precision': 0.9647182727751448, 'recall': 0.9503170028818444, 'f1-score': 0.9574634882843123, 'support': 17350.0} | {'precision': 0.9067466582465004, 'recall': 0.9337741166269239, 'f1-score': 0.9200619426496501, 'support': 9226.0} | 0.9420   | {'precision': 0.9204785732422037, 'recall': 0.9201364773562305, 'f1-score': 0.9202057394578587, 'support': 27619.0} | {'precision': 0.9425303680165918, 'recall': 0.9419964517180202, 'f1-score': 0.942161111514465, 'support': 27619.0}  |
-| 0.1585        | 12.0  | 972  | 0.3526          | {'precision': 0.8663507109004739, 'recall': 0.8763183125599233, 'f1-score': 0.871306005719733, 'support': 1043.0}  | {'precision': 0.9464205312922107, 'recall': 0.9692219020172911, 'f1-score': 0.9576855173984852, 'support': 17350.0} | {'precision': 0.9386084583901774, 'recall': 0.8948623455451984, 'f1-score': 0.9162135168127844, 'support': 9226.0} | 0.9409   | {'precision': 0.917126566860954, 'recall': 0.9134675200408043, 'f1-score': 0.9150683466436677, 'support': 27619.0}  | {'precision': 0.9407871989028143, 'recall': 0.9408740359897172, 'f1-score': 0.9405699625961891, 'support': 27619.0} |
-| 0.021         | 13.0  | 1053 | 0.3594          | {'precision': 0.875, 'recall': 0.8791946308724832, 'f1-score': 0.877092300334768, 'support': 1043.0}               | {'precision': 0.9486991778353419, 'recall': 0.9710086455331413, 'f1-score': 0.9597242793665263, 'support': 17350.0} | {'precision': 0.9419040054464994, 'recall': 0.8997398655972252, 'f1-score': 0.9203392649259936, 'support': 9226.0} | 0.9437   | {'precision': 0.9218677277606138, 'recall': 0.9166477140009498, 'f1-score': 0.9190519482090961, 'support': 27619.0} | {'precision': 0.9436461164304495, 'recall': 0.943734385748941, 'f1-score': 0.943447393984779, 'support': 27619.0}   |
-| 0.021         | 14.0  | 1134 | 0.3840          | {'precision': 0.8467741935483871, 'recall': 0.9060402684563759, 'f1-score': 0.8754052802223252, 'support': 1043.0} | {'precision': 0.9392880904856953, 'recall': 0.9764265129682997, 'f1-score': 0.9574973153224439, 'support': 17350.0} | {'precision': 0.9539388213062477, 'recall': 0.8754606546715803, 'f1-score': 0.9130164471825015, 'support': 9226.0} | 0.9400   | {'precision': 0.9133337017801101, 'recall': 0.9193091453654186, 'f1-score': 0.9153063475757568, 'support': 27619.0} | {'precision': 0.9406884180878825, 'recall': 0.9400412759332344, 'f1-score': 0.9395385738014427, 'support': 27619.0} |
-| 0.021         | 15.0  | 1215 | 0.3763          | {'precision': 0.8755935422602089, 'recall': 0.8839884947267498, 'f1-score': 0.8797709923664121, 'support': 1043.0} | {'precision': 0.954302299112224, 'recall': 0.9665129682997118, 'f1-score': 0.9603688219460511, 'support': 17350.0}  | {'precision': 0.9346230820547031, 'recall': 0.9111207457186213, 'f1-score': 0.922722283205269, 'support': 9226.0}  | 0.9449   | {'precision': 0.9215063078090453, 'recall': 0.920540736248361, 'f1-score': 0.9209540325059108, 'support': 27619.0}  | {'precision': 0.9447562007752335, 'recall': 0.9448930084362215, 'f1-score': 0.944749483712443, 'support': 27619.0}  |
-| 0.021         | 16.0  | 1296 | 0.3690          | {'precision': 0.8976377952755905, 'recall': 0.8744007670182167, 'f1-score': 0.8858669256920835, 'support': 1043.0} | {'precision': 0.9701351590627397, 'recall': 0.9473775216138328, 'f1-score': 0.9586212929752427, 'support': 17350.0} | {'precision': 0.9027950310559006, 'recall': 0.9452633860828095, 'f1-score': 0.9235412474849094, 'support': 9226.0} | 0.9439   | {'precision': 0.923522661798077, 'recall': 0.922347224904953, 'f1-score': 0.9226764887174118, 'support': 27619.0}   | {'precision': 0.9449027186622512, 'recall': 0.9439154205438285, 'f1-score': 0.9441554794131966, 'support': 27619.0} |
-| 0.021         | 17.0  | 1377 | 0.3570          | {'precision': 0.8867562380038387, 'recall': 0.8859060402684564, 'f1-score': 0.8863309352517986, 'support': 1043.0} | {'precision': 0.9659090909090909, 'recall': 0.9602305475504322, 'f1-score': 0.9630614486386496, 'support': 17350.0} | {'precision': 0.9245363918962375, 'recall': 0.9348580099718188, 'f1-score': 0.9296685529506872, 'support': 9226.0} | 0.9489   | {'precision': 0.9257339069363891, 'recall': 0.9269981992635691, 'f1-score': 0.9263536456137119, 'support': 27619.0} | {'precision': 0.9490996138580476, 'recall': 0.9489481878417032, 'f1-score': 0.9490090650954502, 'support': 27619.0} |
-| 0.021         | 18.0  | 1458 | 0.4152          | {'precision': 0.8671655753040225, 'recall': 0.8887823585810163, 'f1-score': 0.8778409090909091, 'support': 1043.0} | {'precision': 0.9355230471984544, 'recall': 0.9767723342939482, 'f1-score': 0.9557028055829692, 'support': 17350.0} | {'precision': 0.9524599881446354, 'recall': 0.8707999132885325, 'f1-score': 0.9098012570069645, 'support': 9226.0} | 0.9380   | {'precision': 0.9183828702157042, 'recall': 0.9121182020544989, 'f1-score': 0.9144483238936143, 'support': 27619.0} | {'precision': 0.9385993125948691, 'recall': 0.938049893189471, 'f1-score': 0.9374292386470396, 'support': 27619.0}  |
-| 0.0058        | 19.0  | 1539 | 0.3720          | {'precision': 0.8919182083739046, 'recall': 0.87823585810163, 'f1-score': 0.8850241545893719, 'support': 1043.0}   | {'precision': 0.956838628857761, 'recall': 0.9685302593659942, 'f1-score': 0.9626489459211732, 'support': 17350.0}  | {'precision': 0.9375415282392027, 'recall': 0.9176241057879905, 'f1-score': 0.927475898334794, 'support': 9226.0}  | 0.9481   | {'precision': 0.9287661218236227, 'recall': 0.9214634077518715, 'f1-score': 0.9250496662817796, 'support': 27619.0} | {'precision': 0.9479408755404257, 'recall': 0.9481154277852203, 'f1-score': 0.9479681394332119, 'support': 27619.0} |
-| 0.0058        | 20.0  | 1620 | 0.3677          | {'precision': 0.8684957426679281, 'recall': 0.8801534036433365, 'f1-score': 0.8742857142857142, 'support': 1043.0} | {'precision': 0.9469289396996189, 'recall': 0.9738904899135447, 'f1-score': 0.9602204921293401, 'support': 17350.0} | {'precision': 0.9473503097040605, 'recall': 0.8951875135486668, 'f1-score': 0.9205305394560855, 'support': 9226.0} | 0.9441   | {'precision': 0.9209249973572025, 'recall': 0.9164104690351826, 'f1-score': 0.9183455819570466, 'support': 27619.0} | {'precision': 0.9441077562808465, 'recall': 0.9440602483797386, 'f1-score': 0.9437170171065533, 'support': 27619.0} |
-| 0.0058        | 21.0  | 1701 | 0.3749          | {'precision': 0.868252516010979, 'recall': 0.909875359539789, 'f1-score': 0.8885767790262172, 'support': 1043.0}   | {'precision': 0.9537903271531439, 'recall': 0.9695677233429395, 'f1-score': 0.9616143138880155, 'support': 17350.0} | {'precision': 0.9420632242096973, 'recall': 0.9076522870149577, 'f1-score': 0.9245376759591498, 'support': 9226.0} | 0.9466   | {'precision': 0.9213686891246068, 'recall': 0.9290317899658954, 'f1-score': 0.9249095896244609, 'support': 27619.0} | {'precision': 0.9466427045463329, 'recall': 0.9466309424671422, 'f1-score': 0.9464708542988716, 'support': 27619.0} |
-| 0.0058        | 22.0  | 1782 | 0.3899          | {'precision': 0.8754789272030651, 'recall': 0.8763183125599233, 'f1-score': 0.875898418782942, 'support': 1043.0}  | {'precision': 0.9475306911822412, 'recall': 0.9742363112391931, 'f1-score': 0.9606979453806588, 'support': 17350.0} | {'precision': 0.94757326007326, 'recall': 0.897246910903967, 'f1-score': 0.9217236387930074, 'support': 9226.0}    | 0.9448   | {'precision': 0.9235276261528554, 'recall': 0.9159338449010278, 'f1-score': 0.919440000985536, 'support': 27619.0}  | {'precision': 0.9448239585256736, 'recall': 0.9448205945182664, 'f1-score': 0.9444764001104068, 'support': 27619.0} |
-| 0.0058        | 23.0  | 1863 | 0.4506          | {'precision': 0.8712121212121212, 'recall': 0.8820709491850431, 'f1-score': 0.8766079085278704, 'support': 1043.0} | {'precision': 0.9385663638378019, 'recall': 0.9765417867435159, 'f1-score': 0.9571775605897973, 'support': 17350.0} | {'precision': 0.9515920573375631, 'recall': 0.877845220030349, 'f1-score': 0.9132322264193494, 'support': 9226.0}  | 0.9400   | {'precision': 0.9204568474624955, 'recall': 0.9121526519863027, 'f1-score': 0.9156725651790056, 'support': 27619.0} | {'precision': 0.9403739808105457, 'recall': 0.9400050689742568, 'f1-score': 0.939455202786939, 'support': 27619.0}  |
-| 0.0058        | 24.0  | 1944 | 0.4239          | {'precision': 0.8859649122807017, 'recall': 0.8715244487056567, 'f1-score': 0.8786853552440792, 'support': 1043.0} | {'precision': 0.9575452599919023, 'recall': 0.954178674351585, 'f1-score': 0.9558590028580501, 'support': 17350.0}  | {'precision': 0.9121883061049011, 'recall': 0.9199002818122697, 'f1-score': 0.9160280626011873, 'support': 9226.0} | 0.9396   | {'precision': 0.9185661594591683, 'recall': 0.9152011349565038, 'f1-score': 0.9168574735677723, 'support': 27619.0} | {'precision': 0.9396908279261412, 'recall': 0.9396067924255042, 'f1-score': 0.9396392856607877, 'support': 27619.0} |
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-| 0.0015        | 36.0  | 2916 | 0.4413          | {'precision': 0.8901960784313725, 'recall': 0.8705656759348035, 'f1-score': 0.8802714493456132, 'support': 1043.0} | {'precision': 0.9574089997133849, 'recall': 0.9626512968299712, 'f1-score': 0.9600229918091681, 'support': 17350.0} | {'precision': 0.9270264365304784, 'recall': 0.9197918924777801, 'f1-score': 0.9233949945593034, 'support': 9226.0} | 0.9449   | {'precision': 0.924877171558412, 'recall': 0.9176696217475183, 'f1-score': 0.9212298119046949, 'support': 27619.0}  | {'precision': 0.9447216249053674, 'recall': 0.9448568014772439, 'f1-score': 0.944775851745562, 'support': 27619.0}  |
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-| 0.0012        | 38.0  | 3078 | 0.4364          | {'precision': 0.8781869688385269, 'recall': 0.8916586768935763, 'f1-score': 0.8848715509039011, 'support': 1043.0} | {'precision': 0.9536389977842168, 'recall': 0.9674351585014409, 'f1-score': 0.9604875396984349, 'support': 17350.0} | {'precision': 0.9374930237749749, 'recall': 0.9103620203771949, 'f1-score': 0.9237283475391805, 'support': 9226.0} | 0.9455   | {'precision': 0.9231063301325729, 'recall': 0.9231519519240706, 'f1-score': 0.9230291460471722, 'support': 27619.0} | {'precision': 0.9453961496579408, 'recall': 0.9455085267388392, 'f1-score': 0.9453527490407726, 'support': 27619.0} |
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-| 0.0008        | 49.0  | 3969 | 0.4590          | {'precision': 0.8819641170915958, 'recall': 0.8954937679769894, 'f1-score': 0.8886774500475738, 'support': 1043.0} | {'precision': 0.958698516354471, 'recall': 0.9646109510086456, 'f1-score': 0.961645645990749, 'support': 17350.0}   | {'precision': 0.9327694166758211, 'recall': 0.9203338391502276, 'f1-score': 0.9265099023405532, 'support': 9226.0} | 0.9472   | {'precision': 0.9244773500406293, 'recall': 0.9268128527119542, 'f1-score': 0.9256109994596254, 'support': 27619.0} | {'precision': 0.9471392328153709, 'recall': 0.9472102538107824, 'f1-score': 0.9471531517192171, 'support': 27619.0} |
-| 0.0009        | 50.0  | 4050 | 0.4604          | {'precision': 0.8831908831908832, 'recall': 0.8916586768935763, 'f1-score': 0.8874045801526719, 'support': 1043.0} | {'precision': 0.9586151553364668, 'recall': 0.9639193083573487, 'f1-score': 0.9612599149327509, 'support': 17350.0} | {'precision': 0.93125, 'recall': 0.9205506178192066, 'f1-score': 0.9258693993241034, 'support': 9226.0}            | 0.9467   | {'precision': 0.92435201284245, 'recall': 0.9253762010233771, 'f1-score': 0.924844631469842, 'support': 27619.0}    | {'precision': 0.9466256394603638, 'recall': 0.9467033563850972, 'f1-score': 0.9466488134742982, 'support': 27619.0} |
+| No log        | 1.0   | 81   | 0.2620          | {'precision': 0.7461594732991953, 'recall': 0.9002647837599294, 'f1-score': 0.816, 'support': 1133.0}              | {'precision': 0.9024103768767235, 'recall': 0.9638902525500463, 'f1-score': 0.9321376763813793, 'support': 18333.0} | {'precision': 0.931782945736434, 'recall': 0.7917511147142278, 'f1-score': 0.8560784528570645, 'support': 9868.0}  | 0.9035   | {'precision': 0.860117598637451, 'recall': 0.8853020503414012, 'f1-score': 0.8680720430794812, 'support': 29334.0}  | {'precision': 0.9062562975065145, 'recall': 0.9035249198881844, 'f1-score': 0.9020655278480035, 'support': 29334.0} |
+| No log        | 2.0   | 162  | 0.2253          | {'precision': 0.8167072181670721, 'recall': 0.8887908208296558, 'f1-score': 0.8512256973795435, 'support': 1133.0} | {'precision': 0.9152551099212274, 'recall': 0.9696721758577429, 'f1-score': 0.9416781438711729, 'support': 18333.0} | {'precision': 0.9380041484212952, 'recall': 0.8248885285772193, 'f1-score': 0.8778173190984578, 'support': 9868.0} | 0.9178   | {'precision': 0.8899888255031981, 'recall': 0.8944505084215394, 'f1-score': 0.8902403867830581, 'support': 29334.0} | {'precision': 0.9191015935430046, 'recall': 0.9178427763005387, 'f1-score': 0.9167016237671239, 'support': 29334.0} |
+| No log        | 3.0   | 243  | 0.2279          | {'precision': 0.8050117462803446, 'recall': 0.9073256840247131, 'f1-score': 0.8531120331950207, 'support': 1133.0} | {'precision': 0.9280963603037444, 'recall': 0.9666721213112965, 'f1-score': 0.9469915571230095, 'support': 18333.0} | {'precision': 0.9353938852934612, 'recall': 0.8495135792460479, 'f1-score': 0.8903876792352629, 'support': 9868.0} | 0.9250   | {'precision': 0.8895006639591835, 'recall': 0.9078371281940192, 'f1-score': 0.8968304231844311, 'support': 29334.0} | {'precision': 0.9257972230878861, 'recall': 0.9249676143724006, 'f1-score': 0.9243239165827936, 'support': 29334.0} |
+| No log        | 4.0   | 324  | 0.2390          | {'precision': 0.8217179902755267, 'recall': 0.8949691085613416, 'f1-score': 0.8567807351077312, 'support': 1133.0} | {'precision': 0.9432635621180161, 'recall': 0.9512900234549719, 'f1-score': 0.9472597903427299, 'support': 18333.0} | {'precision': 0.9099989595255437, 'recall': 0.8862991487636805, 'f1-score': 0.8979927100980543, 'support': 9868.0} | 0.9273   | {'precision': 0.8916601706396955, 'recall': 0.910852760259998, 'f1-score': 0.9006777451828384, 'support': 29334.0}  | {'precision': 0.9273787107073643, 'recall': 0.9272516533715143, 'f1-score': 0.9271915992526735, 'support': 29334.0} |
+| No log        | 5.0   | 405  | 0.2539          | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9335059992600032, 'recall': 0.9633447880870561, 'f1-score': 0.948190701170407, 'support': 18333.0}  | {'precision': 0.9265359193845487, 'recall': 0.8665383056343737, 'f1-score': 0.8955333298423835, 'support': 9868.0} | 0.9277   | {'precision': 0.9010707463497579, 'recall': 0.9041652089397999, 'f1-score': 0.9020549127380568, 'support': 29334.0} | {'precision': 0.9276721180179627, 'recall': 0.9276607349832958, 'f1-score': 0.9271646670996412, 'support': 29334.0} |
+| No log        | 6.0   | 486  | 0.2930          | {'precision': 0.841927303465765, 'recall': 0.8790820829655781, 'f1-score': 0.8601036269430052, 'support': 1133.0}  | {'precision': 0.9452679589509693, 'recall': 0.9495990836197021, 'f1-score': 0.9474285714285714, 'support': 18333.0} | {'precision': 0.9045613314156564, 'recall': 0.8922780705310093, 'f1-score': 0.8983777165595348, 'support': 9868.0} | 0.9276   | {'precision': 0.8972521979441302, 'recall': 0.9069864123720964, 'f1-score': 0.9019699716437038, 'support': 29334.0} | {'precision': 0.9275827485063247, 'recall': 0.9275925547146656, 'f1-score': 0.9275549436263691, 'support': 29334.0} |
+| 0.1621        | 7.0   | 567  | 0.3149          | {'precision': 0.8406639004149378, 'recall': 0.8940864960282436, 'f1-score': 0.8665526090675792, 'support': 1133.0} | {'precision': 0.9382959450098577, 'recall': 0.9605083728795069, 'f1-score': 0.9492722371967655, 'support': 18333.0} | {'precision': 0.9227729117709891, 'recall': 0.8754560194568302, 'f1-score': 0.8984919396775871, 'support': 9868.0} | 0.9293   | {'precision': 0.9005775857319281, 'recall': 0.9100169627881934, 'f1-score': 0.9047722619806439, 'support': 29334.0} | {'precision': 0.9293030221719495, 'recall': 0.9293311515647371, 'f1-score': 0.9289946986889036, 'support': 29334.0} |
+| 0.1621        | 8.0   | 648  | 0.3477          | {'precision': 0.8284552845528456, 'recall': 0.8993821712268314, 'f1-score': 0.8624629707998307, 'support': 1133.0} | {'precision': 0.9356556940449557, 'recall': 0.9581628756886489, 'f1-score': 0.9467755410030451, 'support': 18333.0} | {'precision': 0.9191854233654877, 'recall': 0.8690717470612079, 'f1-score': 0.8934263985831857, 'support': 9868.0} | 0.9259   | {'precision': 0.8944321339877629, 'recall': 0.9088722646588961, 'f1-score': 0.9008883034620205, 'support': 29334.0} | {'precision': 0.9259745494680297, 'recall': 0.9259221381332242, 'f1-score': 0.9255723133682386, 'support': 29334.0} |
+| 0.1621        | 9.0   | 729  | 0.3808          | {'precision': 0.8464135021097047, 'recall': 0.8852603706972639, 'f1-score': 0.8654012079378774, 'support': 1133.0} | {'precision': 0.9316216786166175, 'recall': 0.9638902525500463, 'f1-score': 0.9474813007694164, 'support': 18333.0} | {'precision': 0.9268053588933667, 'recall': 0.8622821240372922, 'f1-score': 0.8933802299333298, 'support': 9868.0} | 0.9267   | {'precision': 0.9016135132065629, 'recall': 0.9038109157615342, 'f1-score': 0.9020875795468745, 'support': 29334.0} | {'precision': 0.9267103706800465, 'recall': 0.9266721210881571, 'f1-score': 0.9261113508073029, 'support': 29334.0} |
+| 0.1621        | 10.0  | 810  | 0.4663          | {'precision': 0.8380872483221476, 'recall': 0.881729920564872, 'f1-score': 0.8593548387096774, 'support': 1133.0}  | {'precision': 0.9158687080751703, 'recall': 0.9756177385043364, 'f1-score': 0.9447995351539802, 'support': 18333.0} | {'precision': 0.9469406710786021, 'recall': 0.8265099310903932, 'f1-score': 0.8826362209837131, 'support': 9868.0} | 0.9218   | {'precision': 0.9002988758253068, 'recall': 0.8946191967198672, 'f1-score': 0.8955968649491236, 'support': 29334.0} | {'precision': 0.9233171207368492, 'recall': 0.9218313220154087, 'f1-score': 0.9205874800198834, 'support': 29334.0} |
+| 0.1621        | 11.0  | 891  | 0.3998          | {'precision': 0.8421052631578947, 'recall': 0.8755516328331863, 'f1-score': 0.8585028126352229, 'support': 1133.0} | {'precision': 0.941814648890808, 'recall': 0.9517809414716631, 'f1-score': 0.9467715680954965, 'support': 18333.0}  | {'precision': 0.906636203136359, 'recall': 0.8846777462505067, 'f1-score': 0.8955223880597014, 'support': 9868.0}  | 0.9263   | {'precision': 0.8968520383950206, 'recall': 0.9040034401851186, 'f1-score': 0.9002655895968069, 'support': 29334.0} | {'precision': 0.9261293813943774, 'recall': 0.9262630394763756, 'f1-score': 0.9261219666592889, 'support': 29334.0} |
+| 0.1621        | 12.0  | 972  | 0.4524          | {'precision': 0.8503401360544217, 'recall': 0.8826125330979699, 'f1-score': 0.8661758336942399, 'support': 1133.0} | {'precision': 0.9383342231713828, 'recall': 0.9586537937053401, 'f1-score': 0.9483851819874267, 'support': 18333.0} | {'precision': 0.9182223165040305, 'recall': 0.8772800972841508, 'f1-score': 0.8972844112769486, 'support': 9868.0} | 0.9283   | {'precision': 0.9022988919099451, 'recall': 0.906182141362487, 'f1-score': 0.9039484756528716, 'support': 29334.0}  | {'precision': 0.9281698543264606, 'recall': 0.9283425376695984, 'f1-score': 0.9280195449455239, 'support': 29334.0} |
+| 0.0212        | 13.0  | 1053 | 0.4537          | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9365968111768783, 'recall': 0.9580537827960508, 'f1-score': 0.94720379658092, 'support': 18333.0}   | {'precision': 0.9167642362959021, 'recall': 0.8728212403729225, 'f1-score': 0.8942532315838654, 'support': 9868.0} | 0.9265   | {'precision': 0.8988437892925006, 'recall': 0.9044958520889811, 'f1-score': 0.9012992451220551, 'support': 29334.0} | {'precision': 0.926316588126141, 'recall': 0.9264675802822663, 'f1-score': 0.9261172500595471, 'support': 29334.0}  |
+| 0.0212        | 14.0  | 1134 | 0.4902          | {'precision': 0.8573883161512027, 'recall': 0.880847308031774, 'f1-score': 0.8689595124074879, 'support': 1133.0}  | {'precision': 0.9300970873786408, 'recall': 0.9667266677575956, 'f1-score': 0.9480582004921365, 'support': 18333.0} | {'precision': 0.9303346132748217, 'recall': 0.8593433319821646, 'f1-score': 0.8934309645472265, 'support': 9868.0} | 0.9273   | {'precision': 0.9059400056015551, 'recall': 0.9023057692571781, 'f1-score': 0.9034828924822836, 'support': 29334.0} | {'precision': 0.9273686789700647, 'recall': 0.9272857435058294, 'f1-score': 0.9266264019680934, 'support': 29334.0} |
+| 0.0212        | 15.0  | 1215 | 0.4631          | {'precision': 0.8514090520922288, 'recall': 0.8799646954986761, 'f1-score': 0.865451388888889, 'support': 1133.0}  | {'precision': 0.943136407819419, 'recall': 0.9526536846124475, 'f1-score': 0.9478711568207105, 'support': 18333.0}  | {'precision': 0.9084499740798341, 'recall': 0.8879205512768544, 'f1-score': 0.8980679546968688, 'support': 9868.0} | 0.9281   | {'precision': 0.9009984779971606, 'recall': 0.9068463104626593, 'f1-score': 0.9037968334688228, 'support': 29334.0} | {'precision': 0.9279249527781314, 'recall': 0.9280698165950774, 'f1-score': 0.9279338964530544, 'support': 29334.0} |
+| 0.0212        | 16.0  | 1296 | 0.4685          | {'precision': 0.8621291448516579, 'recall': 0.8720211827007943, 'f1-score': 0.8670469504168494, 'support': 1133.0} | {'precision': 0.9403208556149732, 'recall': 0.9591447117220313, 'f1-score': 0.949639510706667, 'support': 18333.0}  | {'precision': 0.917685497470489, 'recall': 0.8823469801378192, 'f1-score': 0.8996693531721429, 'support': 9868.0}  | 0.9299   | {'precision': 0.9067118326457067, 'recall': 0.9045042915202149, 'f1-score': 0.9054519380985532, 'support': 29334.0} | {'precision': 0.9296862022276206, 'recall': 0.9299447739824095, 'f1-score': 0.9296394123443894, 'support': 29334.0} |
+| 0.0212        | 17.0  | 1377 | 0.5305          | {'precision': 0.8462823725981621, 'recall': 0.8940864960282436, 'f1-score': 0.8695278969957082, 'support': 1133.0} | {'precision': 0.9287246847035429, 'recall': 0.9680357824687722, 'f1-score': 0.9479728646973986, 'support': 18333.0} | {'precision': 0.9344262295081968, 'recall': 0.8548844750709363, 'f1-score': 0.892887383573243, 'support': 9868.0}  | 0.9271   | {'precision': 0.9031444289366339, 'recall': 0.9056689178559841, 'f1-score': 0.9034627150887832, 'support': 29334.0} | {'precision': 0.927458430681484, 'recall': 0.9271152928342538, 'f1-score': 0.9264121612086422, 'support': 29334.0}  |
+| 0.0212        | 18.0  | 1458 | 0.5198          | {'precision': 0.847972972972973, 'recall': 0.8861429832303619, 'f1-score': 0.8666378938282262, 'support': 1133.0}  | {'precision': 0.9337531086300862, 'recall': 0.9625811378388698, 'f1-score': 0.9479480017189514, 'support': 18333.0} | {'precision': 0.9244406010161064, 'recall': 0.866639643291447, 'f1-score': 0.8946074585490874, 'support': 9868.0}  | 0.9274   | {'precision': 0.9020555608730553, 'recall': 0.9051212547868929, 'f1-score': 0.9030644513654217, 'support': 29334.0} | {'precision': 0.9273071851680879, 'recall': 0.9273539237744597, 'f1-score': 0.9268636343554685, 'support': 29334.0} |
+| 0.0055        | 19.0  | 1539 | 0.5277          | {'precision': 0.8447986577181208, 'recall': 0.8887908208296558, 'f1-score': 0.8662365591397849, 'support': 1133.0} | {'precision': 0.9328933474128828, 'recall': 0.9637811596574484, 'f1-score': 0.9480857457140558, 'support': 18333.0} | {'precision': 0.9267550532492936, 'recall': 0.8642075395216863, 'f1-score': 0.8943890928159414, 'support': 9868.0} | 0.9274   | {'precision': 0.9014823527934324, 'recall': 0.9055931733362635, 'f1-score': 0.9029037992232607, 'support': 29334.0} | {'precision': 0.9274258363257326, 'recall': 0.9273880139087748, 'f1-score': 0.9268607610823233, 'support': 29334.0} |
+| 0.0055        | 20.0  | 1620 | 0.5306          | {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0} | {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0} | {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0} | 0.9275   | {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0}   | {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0} |
 
 
 ### Framework versions

meta_data/README_s42_e20.md

@@ -0,0 +1,99 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[0%:20%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9275243744460353
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5306
+- B: {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0}
+- I: {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0}
+- O: {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0}
+- Accuracy: 0.9275
+- Macro avg: {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0}
+- Weighted avg: {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 81   | 0.2620          | {'precision': 0.7461594732991953, 'recall': 0.9002647837599294, 'f1-score': 0.816, 'support': 1133.0}              | {'precision': 0.9024103768767235, 'recall': 0.9638902525500463, 'f1-score': 0.9321376763813793, 'support': 18333.0} | {'precision': 0.931782945736434, 'recall': 0.7917511147142278, 'f1-score': 0.8560784528570645, 'support': 9868.0}  | 0.9035   | {'precision': 0.860117598637451, 'recall': 0.8853020503414012, 'f1-score': 0.8680720430794812, 'support': 29334.0}  | {'precision': 0.9062562975065145, 'recall': 0.9035249198881844, 'f1-score': 0.9020655278480035, 'support': 29334.0} |
+| No log        | 2.0   | 162  | 0.2253          | {'precision': 0.8167072181670721, 'recall': 0.8887908208296558, 'f1-score': 0.8512256973795435, 'support': 1133.0} | {'precision': 0.9152551099212274, 'recall': 0.9696721758577429, 'f1-score': 0.9416781438711729, 'support': 18333.0} | {'precision': 0.9380041484212952, 'recall': 0.8248885285772193, 'f1-score': 0.8778173190984578, 'support': 9868.0} | 0.9178   | {'precision': 0.8899888255031981, 'recall': 0.8944505084215394, 'f1-score': 0.8902403867830581, 'support': 29334.0} | {'precision': 0.9191015935430046, 'recall': 0.9178427763005387, 'f1-score': 0.9167016237671239, 'support': 29334.0} |
+| No log        | 3.0   | 243  | 0.2279          | {'precision': 0.8050117462803446, 'recall': 0.9073256840247131, 'f1-score': 0.8531120331950207, 'support': 1133.0} | {'precision': 0.9280963603037444, 'recall': 0.9666721213112965, 'f1-score': 0.9469915571230095, 'support': 18333.0} | {'precision': 0.9353938852934612, 'recall': 0.8495135792460479, 'f1-score': 0.8903876792352629, 'support': 9868.0} | 0.9250   | {'precision': 0.8895006639591835, 'recall': 0.9078371281940192, 'f1-score': 0.8968304231844311, 'support': 29334.0} | {'precision': 0.9257972230878861, 'recall': 0.9249676143724006, 'f1-score': 0.9243239165827936, 'support': 29334.0} |
+| No log        | 4.0   | 324  | 0.2390          | {'precision': 0.8217179902755267, 'recall': 0.8949691085613416, 'f1-score': 0.8567807351077312, 'support': 1133.0} | {'precision': 0.9432635621180161, 'recall': 0.9512900234549719, 'f1-score': 0.9472597903427299, 'support': 18333.0} | {'precision': 0.9099989595255437, 'recall': 0.8862991487636805, 'f1-score': 0.8979927100980543, 'support': 9868.0} | 0.9273   | {'precision': 0.8916601706396955, 'recall': 0.910852760259998, 'f1-score': 0.9006777451828384, 'support': 29334.0}  | {'precision': 0.9273787107073643, 'recall': 0.9272516533715143, 'f1-score': 0.9271915992526735, 'support': 29334.0} |
+| No log        | 5.0   | 405  | 0.2539          | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9335059992600032, 'recall': 0.9633447880870561, 'f1-score': 0.948190701170407, 'support': 18333.0}  | {'precision': 0.9265359193845487, 'recall': 0.8665383056343737, 'f1-score': 0.8955333298423835, 'support': 9868.0} | 0.9277   | {'precision': 0.9010707463497579, 'recall': 0.9041652089397999, 'f1-score': 0.9020549127380568, 'support': 29334.0} | {'precision': 0.9276721180179627, 'recall': 0.9276607349832958, 'f1-score': 0.9271646670996412, 'support': 29334.0} |
+| No log        | 6.0   | 486  | 0.2930          | {'precision': 0.841927303465765, 'recall': 0.8790820829655781, 'f1-score': 0.8601036269430052, 'support': 1133.0}  | {'precision': 0.9452679589509693, 'recall': 0.9495990836197021, 'f1-score': 0.9474285714285714, 'support': 18333.0} | {'precision': 0.9045613314156564, 'recall': 0.8922780705310093, 'f1-score': 0.8983777165595348, 'support': 9868.0} | 0.9276   | {'precision': 0.8972521979441302, 'recall': 0.9069864123720964, 'f1-score': 0.9019699716437038, 'support': 29334.0} | {'precision': 0.9275827485063247, 'recall': 0.9275925547146656, 'f1-score': 0.9275549436263691, 'support': 29334.0} |
+| 0.1621        | 7.0   | 567  | 0.3149          | {'precision': 0.8406639004149378, 'recall': 0.8940864960282436, 'f1-score': 0.8665526090675792, 'support': 1133.0} | {'precision': 0.9382959450098577, 'recall': 0.9605083728795069, 'f1-score': 0.9492722371967655, 'support': 18333.0} | {'precision': 0.9227729117709891, 'recall': 0.8754560194568302, 'f1-score': 0.8984919396775871, 'support': 9868.0} | 0.9293   | {'precision': 0.9005775857319281, 'recall': 0.9100169627881934, 'f1-score': 0.9047722619806439, 'support': 29334.0} | {'precision': 0.9293030221719495, 'recall': 0.9293311515647371, 'f1-score': 0.9289946986889036, 'support': 29334.0} |
+| 0.1621        | 8.0   | 648  | 0.3477          | {'precision': 0.8284552845528456, 'recall': 0.8993821712268314, 'f1-score': 0.8624629707998307, 'support': 1133.0} | {'precision': 0.9356556940449557, 'recall': 0.9581628756886489, 'f1-score': 0.9467755410030451, 'support': 18333.0} | {'precision': 0.9191854233654877, 'recall': 0.8690717470612079, 'f1-score': 0.8934263985831857, 'support': 9868.0} | 0.9259   | {'precision': 0.8944321339877629, 'recall': 0.9088722646588961, 'f1-score': 0.9008883034620205, 'support': 29334.0} | {'precision': 0.9259745494680297, 'recall': 0.9259221381332242, 'f1-score': 0.9255723133682386, 'support': 29334.0} |
+| 0.1621        | 9.0   | 729  | 0.3808          | {'precision': 0.8464135021097047, 'recall': 0.8852603706972639, 'f1-score': 0.8654012079378774, 'support': 1133.0} | {'precision': 0.9316216786166175, 'recall': 0.9638902525500463, 'f1-score': 0.9474813007694164, 'support': 18333.0} | {'precision': 0.9268053588933667, 'recall': 0.8622821240372922, 'f1-score': 0.8933802299333298, 'support': 9868.0} | 0.9267   | {'precision': 0.9016135132065629, 'recall': 0.9038109157615342, 'f1-score': 0.9020875795468745, 'support': 29334.0} | {'precision': 0.9267103706800465, 'recall': 0.9266721210881571, 'f1-score': 0.9261113508073029, 'support': 29334.0} |
+| 0.1621        | 10.0  | 810  | 0.4663          | {'precision': 0.8380872483221476, 'recall': 0.881729920564872, 'f1-score': 0.8593548387096774, 'support': 1133.0}  | {'precision': 0.9158687080751703, 'recall': 0.9756177385043364, 'f1-score': 0.9447995351539802, 'support': 18333.0} | {'precision': 0.9469406710786021, 'recall': 0.8265099310903932, 'f1-score': 0.8826362209837131, 'support': 9868.0} | 0.9218   | {'precision': 0.9002988758253068, 'recall': 0.8946191967198672, 'f1-score': 0.8955968649491236, 'support': 29334.0} | {'precision': 0.9233171207368492, 'recall': 0.9218313220154087, 'f1-score': 0.9205874800198834, 'support': 29334.0} |
+| 0.1621        | 11.0  | 891  | 0.3998          | {'precision': 0.8421052631578947, 'recall': 0.8755516328331863, 'f1-score': 0.8585028126352229, 'support': 1133.0} | {'precision': 0.941814648890808, 'recall': 0.9517809414716631, 'f1-score': 0.9467715680954965, 'support': 18333.0}  | {'precision': 0.906636203136359, 'recall': 0.8846777462505067, 'f1-score': 0.8955223880597014, 'support': 9868.0}  | 0.9263   | {'precision': 0.8968520383950206, 'recall': 0.9040034401851186, 'f1-score': 0.9002655895968069, 'support': 29334.0} | {'precision': 0.9261293813943774, 'recall': 0.9262630394763756, 'f1-score': 0.9261219666592889, 'support': 29334.0} |
+| 0.1621        | 12.0  | 972  | 0.4524          | {'precision': 0.8503401360544217, 'recall': 0.8826125330979699, 'f1-score': 0.8661758336942399, 'support': 1133.0} | {'precision': 0.9383342231713828, 'recall': 0.9586537937053401, 'f1-score': 0.9483851819874267, 'support': 18333.0} | {'precision': 0.9182223165040305, 'recall': 0.8772800972841508, 'f1-score': 0.8972844112769486, 'support': 9868.0} | 0.9283   | {'precision': 0.9022988919099451, 'recall': 0.906182141362487, 'f1-score': 0.9039484756528716, 'support': 29334.0}  | {'precision': 0.9281698543264606, 'recall': 0.9283425376695984, 'f1-score': 0.9280195449455239, 'support': 29334.0} |
+| 0.0212        | 13.0  | 1053 | 0.4537          | {'precision': 0.8431703204047217, 'recall': 0.8826125330979699, 'f1-score': 0.8624407072013798, 'support': 1133.0} | {'precision': 0.9365968111768783, 'recall': 0.9580537827960508, 'f1-score': 0.94720379658092, 'support': 18333.0}   | {'precision': 0.9167642362959021, 'recall': 0.8728212403729225, 'f1-score': 0.8942532315838654, 'support': 9868.0} | 0.9265   | {'precision': 0.8988437892925006, 'recall': 0.9044958520889811, 'f1-score': 0.9012992451220551, 'support': 29334.0} | {'precision': 0.926316588126141, 'recall': 0.9264675802822663, 'f1-score': 0.9261172500595471, 'support': 29334.0}  |
+| 0.0212        | 14.0  | 1134 | 0.4902          | {'precision': 0.8573883161512027, 'recall': 0.880847308031774, 'f1-score': 0.8689595124074879, 'support': 1133.0}  | {'precision': 0.9300970873786408, 'recall': 0.9667266677575956, 'f1-score': 0.9480582004921365, 'support': 18333.0} | {'precision': 0.9303346132748217, 'recall': 0.8593433319821646, 'f1-score': 0.8934309645472265, 'support': 9868.0} | 0.9273   | {'precision': 0.9059400056015551, 'recall': 0.9023057692571781, 'f1-score': 0.9034828924822836, 'support': 29334.0} | {'precision': 0.9273686789700647, 'recall': 0.9272857435058294, 'f1-score': 0.9266264019680934, 'support': 29334.0} |
+| 0.0212        | 15.0  | 1215 | 0.4631          | {'precision': 0.8514090520922288, 'recall': 0.8799646954986761, 'f1-score': 0.865451388888889, 'support': 1133.0}  | {'precision': 0.943136407819419, 'recall': 0.9526536846124475, 'f1-score': 0.9478711568207105, 'support': 18333.0}  | {'precision': 0.9084499740798341, 'recall': 0.8879205512768544, 'f1-score': 0.8980679546968688, 'support': 9868.0} | 0.9281   | {'precision': 0.9009984779971606, 'recall': 0.9068463104626593, 'f1-score': 0.9037968334688228, 'support': 29334.0} | {'precision': 0.9279249527781314, 'recall': 0.9280698165950774, 'f1-score': 0.9279338964530544, 'support': 29334.0} |
+| 0.0212        | 16.0  | 1296 | 0.4685          | {'precision': 0.8621291448516579, 'recall': 0.8720211827007943, 'f1-score': 0.8670469504168494, 'support': 1133.0} | {'precision': 0.9403208556149732, 'recall': 0.9591447117220313, 'f1-score': 0.949639510706667, 'support': 18333.0}  | {'precision': 0.917685497470489, 'recall': 0.8823469801378192, 'f1-score': 0.8996693531721429, 'support': 9868.0}  | 0.9299   | {'precision': 0.9067118326457067, 'recall': 0.9045042915202149, 'f1-score': 0.9054519380985532, 'support': 29334.0} | {'precision': 0.9296862022276206, 'recall': 0.9299447739824095, 'f1-score': 0.9296394123443894, 'support': 29334.0} |
+| 0.0212        | 17.0  | 1377 | 0.5305          | {'precision': 0.8462823725981621, 'recall': 0.8940864960282436, 'f1-score': 0.8695278969957082, 'support': 1133.0} | {'precision': 0.9287246847035429, 'recall': 0.9680357824687722, 'f1-score': 0.9479728646973986, 'support': 18333.0} | {'precision': 0.9344262295081968, 'recall': 0.8548844750709363, 'f1-score': 0.892887383573243, 'support': 9868.0}  | 0.9271   | {'precision': 0.9031444289366339, 'recall': 0.9056689178559841, 'f1-score': 0.9034627150887832, 'support': 29334.0} | {'precision': 0.927458430681484, 'recall': 0.9271152928342538, 'f1-score': 0.9264121612086422, 'support': 29334.0}  |
+| 0.0212        | 18.0  | 1458 | 0.5198          | {'precision': 0.847972972972973, 'recall': 0.8861429832303619, 'f1-score': 0.8666378938282262, 'support': 1133.0}  | {'precision': 0.9337531086300862, 'recall': 0.9625811378388698, 'f1-score': 0.9479480017189514, 'support': 18333.0} | {'precision': 0.9244406010161064, 'recall': 0.866639643291447, 'f1-score': 0.8946074585490874, 'support': 9868.0}  | 0.9274   | {'precision': 0.9020555608730553, 'recall': 0.9051212547868929, 'f1-score': 0.9030644513654217, 'support': 29334.0} | {'precision': 0.9273071851680879, 'recall': 0.9273539237744597, 'f1-score': 0.9268636343554685, 'support': 29334.0} |
+| 0.0055        | 19.0  | 1539 | 0.5277          | {'precision': 0.8447986577181208, 'recall': 0.8887908208296558, 'f1-score': 0.8662365591397849, 'support': 1133.0} | {'precision': 0.9328933474128828, 'recall': 0.9637811596574484, 'f1-score': 0.9480857457140558, 'support': 18333.0} | {'precision': 0.9267550532492936, 'recall': 0.8642075395216863, 'f1-score': 0.8943890928159414, 'support': 9868.0} | 0.9274   | {'precision': 0.9014823527934324, 'recall': 0.9055931733362635, 'f1-score': 0.9029037992232607, 'support': 29334.0} | {'precision': 0.9274258363257326, 'recall': 0.9273880139087748, 'f1-score': 0.9268607610823233, 'support': 29334.0} |
+| 0.0055        | 20.0  | 1620 | 0.5306          | {'precision': 0.8433835845896147, 'recall': 0.8887908208296558, 'f1-score': 0.8654920498495917, 'support': 1133.0} | {'precision': 0.9324545214869496, 'recall': 0.9645993563519337, 'f1-score': 0.9482545981017748, 'support': 18333.0} | {'precision': 0.9282833787465941, 'recall': 0.8630928252938792, 'f1-score': 0.8945019167148034, 'support': 9868.0} | 0.9275   | {'precision': 0.9013738282743861, 'recall': 0.9054943341584897, 'f1-score': 0.90274952155539, 'support': 29334.0}   | {'precision': 0.9276110562907095, 'recall': 0.9275243744460353, 'f1-score': 0.9269754876123645, 'support': 29334.0} |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

model.safetensors

@@ -1,3 +1,3 @@
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 size 592318676