Theoreticallyhugo
/

longformer-sep_tok

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: sep_tok
-      split: train[60%:80%]
       args: sep_tok
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9117647058823529
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,14 +32,14 @@ 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: 0.4143
-- Claim: {'precision': 0.6836630634305527, 'recall': 0.6785425101214575, 'f1-score': 0.6810931626536625, 'support': 4940.0}
-- Majorclaim: {'precision': 0.8679727427597955, 'recall': 0.9314442413162706, 'f1-score': 0.8985890652557319, 'support': 2188.0}
-- O: {'precision': 0.999474513925381, 'recall': 0.9970793080206695, 'f1-score': 0.9982754742445827, 'support': 13353.0}
-- Premise: {'precision': 0.9151062753036437, 'recall': 0.9098685451915215, 'f1-score': 0.9124798940297096, 'support': 15899.0}
-- Accuracy: 0.9118
-- Macro avg: {'precision': 0.8665541488548433, 'recall': 0.8792336511624799, 'f1-score': 0.8726093990459217, 'support': 36380.0}
-- Weighted avg: {'precision': 0.9118108232546347, 'recall': 0.9117647058823529, 'f1-score': 0.9117153199850166, 'support': 36380.0}
 ## Model description
@@ -68,24 +68,24 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                   | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.3470          | {'precision': 0.564843099139128, 'recall': 0.4117408906882591, 'f1-score': 0.476290832455216, 'support': 4940.0}    | {'precision': 0.5601374570446735, 'recall': 0.8939670932358318, 'f1-score': 0.6887323943661973, 'support': 2188.0} | {'precision': 0.9936889556724268, 'recall': 0.9904890286826931, 'f1-score': 0.9920864118816337, 'support': 13353.0} | {'precision': 0.8845214996557551, 'recall': 0.888860934649978, 'f1-score': 0.8866859078930858, 'support': 15899.0}  | 0.8617   | {'precision': 0.7507977528779959, 'recall': 0.7962644868141905, 'f1-score': 0.7609488866490333, 'support': 36380.0} | {'precision': 0.8616723918054371, 'recall': 0.8616822429906542, 'f1-score': 0.8577397553229695, 'support': 36380.0} |
-| No log        | 2.0   | 82   | 0.2482          | {'precision': 0.609009009009009, 'recall': 0.6842105263157895, 'f1-score': 0.6444232602478551, 'support': 4940.0}   | {'precision': 0.8622502628811777, 'recall': 0.7495429616087751, 'f1-score': 0.8019559902200488, 'support': 2188.0} | {'precision': 0.9991750412479377, 'recall': 0.9977533138620535, 'f1-score': 0.998463671450519, 'support': 13353.0}  | {'precision': 0.9146466589713993, 'recall': 0.8971004465689666, 'f1-score': 0.9057885879401772, 'support': 15899.0} | 0.8963   | {'precision': 0.846270243027381, 'recall': 0.8321518120888962, 'f1-score': 0.83765787746465, 'support': 36380.0}    | {'precision': 0.901018681595891, 'recall': 0.8962616822429906, 'f1-score': 0.898068960328904, 'support': 36380.0}   |
-| No log        | 3.0   | 123  | 0.2560          | {'precision': 0.6481652790625964, 'recall': 0.42550607287449393, 'f1-score': 0.5137480141757302, 'support': 4940.0} | {'precision': 0.9458357600465929, 'recall': 0.7422303473491774, 'f1-score': 0.8317541613316262, 'support': 2188.0} | {'precision': 0.9993245271690183, 'recall': 0.997154197558601, 'f1-score': 0.9982381827042021, 'support': 13353.0}  | {'precision': 0.8452144120247569, 'recall': 0.962010189320083, 'f1-score': 0.8998382115016914, 'support': 15899.0}  | 0.8888   | {'precision': 0.8596349945757411, 'recall': 0.7817252017755888, 'f1-score': 0.8108946424283126, 'support': 36380.0} | {'precision': 0.8810739271473523, 'recall': 0.8888400219901045, 'f1-score': 0.8794336303830761, 'support': 36380.0} |
-| No log        | 4.0   | 164  | 0.2400          | {'precision': 0.7123867069486405, 'recall': 0.4773279352226721, 'f1-score': 0.5716363636363636, 'support': 4940.0}  | {'precision': 0.9357889497262319, 'recall': 0.8592321755027422, 'f1-score': 0.8958780081010246, 'support': 2188.0} | {'precision': 0.999699270731524, 'recall': 0.9958061858758331, 'f1-score': 0.9977489307421025, 'support': 13353.0}  | {'precision': 0.8584459459459459, 'recall': 0.958928234480156, 'f1-score': 0.9059092664666211, 'support': 15899.0}  | 0.9011   | {'precision': 0.8765802183380855, 'recall': 0.8228236327703509, 'f1-score': 0.8427931422365279, 'support': 36380.0} | {'precision': 0.8951103081638239, 'recall': 0.9010720175920836, 'f1-score': 0.8936244534865525, 'support': 36380.0} |
-| No log        | 5.0   | 205  | 0.2714          | {'precision': 0.7058679106309845, 'recall': 0.5819838056680162, 'f1-score': 0.6379673804504604, 'support': 4940.0}  | {'precision': 0.9069548872180451, 'recall': 0.8820840950639853, 'f1-score': 0.8943466172381834, 'support': 2188.0} | {'precision': 0.999171686746988, 'recall': 0.9937092788137497, 'f1-score': 0.9964329966582812, 'support': 13353.0}  | {'precision': 0.8842535061246227, 'recall': 0.939870432102648, 'f1-score': 0.9112140984206354, 'support': 15899.0}  | 0.9076   | {'precision': 0.8740619976801601, 'recall': 0.8494119029120998, 'f1-score': 0.8599902731918901, 'support': 36380.0} | {'precision': 0.9035758878163292, 'recall': 0.9075590984057175, 'f1-score': 0.9043747117402454, 'support': 36380.0} |
-| No log        | 6.0   | 246  | 0.2659          | {'precision': 0.6969630281690141, 'recall': 0.6410931174089068, 'f1-score': 0.6678616617460986, 'support': 4940.0}  | {'precision': 0.8673602080624188, 'recall': 0.9145338208409507, 'f1-score': 0.8903225806451615, 'support': 2188.0} | {'precision': 1.0, 'recall': 0.9901145809930353, 'f1-score': 0.9950327387672161, 'support': 13353.0}                | {'precision': 0.8999877360804514, 'recall': 0.9231398201144726, 'f1-score': 0.9114167727512653, 'support': 15899.0} | 0.9089   | {'precision': 0.8660777430779711, 'recall': 0.8672203348393414, 'f1-score': 0.8661584384774353, 'support': 36380.0} | {'precision': 0.9071656544897909, 'recall': 0.9089059923034635, 'f1-score': 0.9077666257891245, 'support': 36380.0} |
-| No log        | 7.0   | 287  | 0.3056          | {'precision': 0.7278708133971292, 'recall': 0.49271255060728747, 'f1-score': 0.5876388218252052, 'support': 4940.0} | {'precision': 0.8874715261958998, 'recall': 0.8903107861060329, 'f1-score': 0.888888888888889, 'support': 2188.0}  | {'precision': 1.0, 'recall': 0.9936343892758182, 'f1-score': 0.9968070320423725, 'support': 13353.0}                | {'precision': 0.8625732658054971, 'recall': 0.9533932951757972, 'f1-score': 0.9057122370936902, 'support': 15899.0} | 0.9018   | {'precision': 0.8694789013496315, 'recall': 0.832512755291234, 'f1-score': 0.8447617449625392, 'support': 36380.0}  | {'precision': 0.8962210519664664, 'recall': 0.9018141836173722, 'f1-score': 0.8949452398328692, 'support': 36380.0} |
-| No log        | 8.0   | 328  | 0.3121          | {'precision': 0.7013651877133106, 'recall': 0.665587044534413, 'f1-score': 0.6830078936435396, 'support': 4940.0}   | {'precision': 0.9269796111901375, 'recall': 0.893510054844607, 'f1-score': 0.9099371654642774, 'support': 2188.0}  | {'precision': 0.9998493181646952, 'recall': 0.9938590578896128, 'f1-score': 0.9968451889130925, 'support': 13353.0} | {'precision': 0.9029429797670141, 'recall': 0.9262846719919492, 'f1-score': 0.9144649011145954, 'support': 15899.0} | 0.9137   | {'precision': 0.8827842742087894, 'recall': 0.8698102073151455, 'f1-score': 0.8760637872838762, 'support': 36380.0} | {'precision': 0.9125853160350936, 'recall': 0.9137163276525564, 'f1-score': 0.9130003513747224, 'support': 36380.0} |
-| No log        | 9.0   | 369  | 0.3160          | {'precision': 0.6483785822021116, 'recall': 0.6961538461538461, 'f1-score': 0.6714174150722373, 'support': 4940.0}  | {'precision': 0.8652871621621622, 'recall': 0.9364716636197441, 'f1-score': 0.8994732221246707, 'support': 2188.0} | {'precision': 0.9996248217903504, 'recall': 0.9976784243241219, 'f1-score': 0.9986506746626687, 'support': 13353.0} | {'precision': 0.9191860087120474, 'recall': 0.8892383168752752, 'f1-score': 0.9039641943734017, 'support': 15899.0} | 0.9057   | {'precision': 0.8581191437166679, 'recall': 0.8798855627432468, 'f1-score': 0.8683763765582445, 'support': 36380.0} | {'precision': 0.9086961820991927, 'recall': 0.9056624518966465, 'f1-score': 0.9068707703567609, 'support': 36380.0} |
-| No log        | 10.0  | 410  | 0.3451          | {'precision': 0.7022722446379274, 'recall': 0.6694331983805668, 'f1-score': 0.6854596331226034, 'support': 4940.0}  | {'precision': 0.924015009380863, 'recall': 0.9003656307129799, 'f1-score': 0.912037037037037, 'support': 2188.0}   | {'precision': 0.999474435017644, 'recall': 0.9969295289448065, 'f1-score': 0.9982003599280145, 'support': 13353.0}  | {'precision': 0.9051787916152898, 'recall': 0.9234543053022203, 'f1-score': 0.9142252249447368, 'support': 15899.0} | 0.9145   | {'precision': 0.882735120162931, 'recall': 0.8725456658351434, 'f1-score': 0.8774805637580978, 'support': 36380.0}  | {'precision': 0.9133696939999666, 'recall': 0.9145409565695437, 'f1-score': 0.9138522232594241, 'support': 36380.0} |
-| No log        | 11.0  | 451  | 0.3666          | {'precision': 0.6846517626827171, 'recall': 0.6447368421052632, 'f1-score': 0.6640950792326938, 'support': 4940.0}  | {'precision': 0.9058558558558558, 'recall': 0.9191042047531993, 'f1-score': 0.9124319419237751, 'support': 2188.0} | {'precision': 0.9991744220954668, 'recall': 0.9970044184827379, 'f1-score': 0.9980882408066872, 'support': 13353.0} | {'precision': 0.9018783984181908, 'recall': 0.9180451600729606, 'f1-score': 0.9098899728828351, 'support': 15899.0} | 0.9100   | {'precision': 0.8728901097630577, 'recall': 0.8697226563535402, 'f1-score': 0.8711263087114978, 'support': 36380.0} | {'precision': 0.9083324088773178, 'recall': 0.909978009895547, 'f1-score': 0.9090391352032605, 'support': 36380.0}  |
-| No log        | 12.0  | 492  | 0.4192          | {'precision': 0.7116060961313013, 'recall': 0.6143724696356275, 'f1-score': 0.6594242259641498, 'support': 4940.0}  | {'precision': 0.8527354997943233, 'recall': 0.9474405850091407, 'f1-score': 0.897596882442087, 'support': 2188.0}  | {'precision': 0.9988748030905409, 'recall': 0.9972290870965326, 'f1-score': 0.9980512666766601, 'support': 13353.0} | {'precision': 0.9013636641594814, 'recall': 0.9271023334800931, 'f1-score': 0.9140518417462483, 'support': 15899.0} | 0.9116   | {'precision': 0.8661450157939118, 'recall': 0.8715361188053484, 'f1-score': 0.8672810542072864, 'support': 36380.0} | {'precision': 0.9084627688449202, 'recall': 0.9115997800989555, 'f1-score': 0.9093179343293907, 'support': 36380.0} |
-| 0.1714        | 13.0  | 533  | 0.4313          | {'precision': 0.6997058157954288, 'recall': 0.625910931174089, 'f1-score': 0.6607543540976599, 'support': 4940.0}   | {'precision': 0.8979409131602507, 'recall': 0.916819012797075, 'f1-score': 0.9072817729534148, 'support': 2188.0}  | {'precision': 0.9991729945116908, 'recall': 0.9952819591103123, 'f1-score': 0.9972236812485931, 'support': 13353.0} | {'precision': 0.8959576281504931, 'recall': 0.9256557016164538, 'f1-score': 0.9105645784996134, 'support': 15899.0} | 0.9100   | {'precision': 0.8731943379044659, 'recall': 0.8659169011744825, 'f1-score': 0.8689560966998203, 'support': 36380.0} | {'precision': 0.9073125006515488, 'recall': 0.909978009895547, 'f1-score': 0.9082532456773593, 'support': 36380.0}  |
-| 0.1714        | 14.0  | 574  | 0.4222          | {'precision': 0.7030596522121946, 'recall': 0.6465587044534413, 'f1-score': 0.6736264895075399, 'support': 4940.0}  | {'precision': 0.8805194805194805, 'recall': 0.9296160877513712, 'f1-score': 0.9044019564250778, 'support': 2188.0} | {'precision': 0.9993238674780257, 'recall': 0.9961806335654909, 'f1-score': 0.9977497749774977, 'support': 13353.0} | {'precision': 0.9044770596941293, 'recall': 0.9225108497389773, 'f1-score': 0.9134049509574966, 'support': 15899.0} | 0.9125   | {'precision': 0.8718450149759575, 'recall': 0.8737165688773202, 'f1-score': 0.8722957929669031, 'support': 36380.0} | {'precision': 0.9104987267678919, 'recall': 0.9125068719076416, 'f1-score': 0.911262352923944, 'support': 36380.0}  |
-| 0.1714        | 15.0  | 615  | 0.4086          | {'precision': 0.6842319430315361, 'recall': 0.6807692307692308, 'f1-score': 0.6824961948249619, 'support': 4940.0}  | {'precision': 0.8801384681955863, 'recall': 0.9296160877513712, 'f1-score': 0.9042009335407869, 'support': 2188.0} | {'precision': 0.9993994895661312, 'recall': 0.9970793080206695, 'f1-score': 0.9982380506091845, 'support': 13353.0} | {'precision': 0.9139717028802425, 'recall': 0.9101201333417196, 'f1-score': 0.9120418518168354, 'support': 15899.0} | 0.9121   | {'precision': 0.869435400918374, 'recall': 0.8793961899707478, 'f1-score': 0.8742442576979421, 'support': 36380.0}  | {'precision': 0.9120963786491826, 'recall': 0.9120670698185817, 'f1-score': 0.9120381785828164, 'support': 36380.0} |
-| 0.1714        | 16.0  | 656  | 0.4143          | {'precision': 0.6836630634305527, 'recall': 0.6785425101214575, 'f1-score': 0.6810931626536625, 'support': 4940.0}  | {'precision': 0.8679727427597955, 'recall': 0.9314442413162706, 'f1-score': 0.8985890652557319, 'support': 2188.0} | {'precision': 0.999474513925381, 'recall': 0.9970793080206695, 'f1-score': 0.9982754742445827, 'support': 13353.0}  | {'precision': 0.9151062753036437, 'recall': 0.9098685451915215, 'f1-score': 0.9124798940297096, 'support': 15899.0} | 0.9118   | {'precision': 0.8665541488548433, 'recall': 0.8792336511624799, 'f1-score': 0.8726093990459217, 'support': 36380.0} | {'precision': 0.9118108232546347, 'recall': 0.9117647058823529, 'f1-score': 0.9117153199850166, 'support': 36380.0} |
 ### Framework versions

       name: essays_su_g
       type: essays_su_g
       config: sep_tok
+      split: train[80%:100%]
       args: sep_tok
     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.9037199124726477
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 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: 0.4627
+- Claim: {'precision': 0.6641901931649331, 'recall': 0.6434740882917467, 'f1-score': 0.6536680477699245, 'support': 4168.0}
+- Majorclaim: {'precision': 0.9209900047596382, 'recall': 0.8991635687732342, 'f1-score': 0.909945920526687, 'support': 2152.0}
+- O: {'precision': 1.0, 'recall': 0.9999115983026874, 'f1-score': 0.9999557971975424, 'support': 11312.0}
+- Premise: {'precision': 0.8908200734394125, 'recall': 0.9042491509980949, 'f1-score': 0.8974843801381124, 'support': 12073.0}
+- Accuracy: 0.9037
+- Macro avg: {'precision': 0.8690000678409959, 'recall': 0.8616996015914409, 'f1-score': 0.8652635364080665, 'support': 29705.0}
+- Weighted avg: {'precision': 0.9027835705096182, 'recall': 0.9037199124726477, 'f1-score': 0.9031988198412557, 'support': 29705.0}
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                                         | O                                                                                                                   | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.3480          | {'precision': 0.45800144822592326, 'recall': 0.30350287907869483, 'f1-score': 0.3650793650793651, 'support': 4168.0} | {'precision': 0.6792168674698795, 'recall': 0.6287174721189591, 'f1-score': 0.652992277992278, 'support': 2152.0}  | {'precision': 0.9994626063591581, 'recall': 0.986474540311174, 'f1-score': 0.9929261022378432, 'support': 11312.0}  | {'precision': 0.8190918322936313, 'recall': 0.9353101963058064, 'f1-score': 0.873351637727677, 'support': 12073.0}  | 0.8439   | {'precision': 0.7389431885871479, 'recall': 0.7135012719536586, 'f1-score': 0.7210873457592908, 'support': 29705.0} | {'precision': 0.8269800178224755, 'recall': 0.8439319979801381, 'f1-score': 0.8316056073620907, 'support': 29705.0} |
+| No log        | 2.0   | 82   | 0.2758          | {'precision': 0.6302521008403361, 'recall': 0.32389635316698656, 'f1-score': 0.427892234548336, 'support': 4168.0}   | {'precision': 0.7593291404612159, 'recall': 0.841542750929368, 'f1-score': 0.7983248842847697, 'support': 2152.0}  | {'precision': 0.9997344192634561, 'recall': 0.9983203677510608, 'f1-score': 0.9990268931351733, 'support': 11312.0} | {'precision': 0.833381357153148, 'recall': 0.9582539551064359, 'f1-score': 0.8914659988441533, 'support': 12073.0}  | 0.8760   | {'precision': 0.805674254429539, 'recall': 0.7805033567384627, 'f1-score': 0.779177502703108, 'support': 29705.0}   | {'precision': 0.8628640276786139, 'recall': 0.876047803400101, 'f1-score': 0.8606332672536217, 'support': 29705.0}  |
+| No log        | 3.0   | 123  | 0.2410          | {'precision': 0.620671283963772, 'recall': 0.559021113243762, 'f1-score': 0.5882352941176471, 'support': 4168.0}     | {'precision': 0.8549924736578023, 'recall': 0.79182156133829, 'f1-score': 0.822195416164053, 'support': 2152.0}    | {'precision': 0.9999115357395613, 'recall': 0.9992043847241867, 'f1-score': 0.999557835160948, 'support': 11312.0}  | {'precision': 0.8765607712976135, 'recall': 0.918744305475027, 'f1-score': 0.897156953936992, 'support': 12073.0}   | 0.8897   | {'precision': 0.8380340161646872, 'recall': 0.8171978411953165, 'f1-score': 0.8267863748449099, 'support': 29705.0} | {'precision': 0.8860669651248813, 'recall': 0.8897155361050328, 'f1-score': 0.8873759763571568, 'support': 29705.0} |
+| No log        | 4.0   | 164  | 0.2487          | {'precision': 0.6344057431534167, 'recall': 0.5724568138195777, 'f1-score': 0.6018413419094463, 'support': 4168.0}   | {'precision': 0.8230162027420025, 'recall': 0.9205390334572491, 'f1-score': 0.8690502303136654, 'support': 2152.0} | {'precision': 0.9998231027772864, 'recall': 0.9992927864214993, 'f1-score': 0.9995578742594394, 'support': 11312.0} | {'precision': 0.8858637887335459, 'recall': 0.8974571357574753, 'f1-score': 0.8916227781435153, 'support': 12073.0} | 0.8923   | {'precision': 0.8357772093515627, 'recall': 0.8474364423639503, 'f1-score': 0.8405180561565165, 'support': 29705.0} | {'precision': 0.8894248936462209, 'recall': 0.8923076923076924, 'f1-score': 0.8904302737876794, 'support': 29705.0} |
+| No log        | 5.0   | 205  | 0.2594          | {'precision': 0.6126252038201724, 'recall': 0.6309980806142035, 'f1-score': 0.6216759248315801, 'support': 4168.0}   | {'precision': 0.8722222222222222, 'recall': 0.8754646840148699, 'f1-score': 0.8738404452690167, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9992927864214993, 'f1-score': 0.9996462681287585, 'support': 11312.0}                | {'precision': 0.8930364914630063, 'recall': 0.8837902758220824, 'f1-score': 0.8883893260064111, 'support': 12073.0} | 0.8917   | {'precision': 0.8444709793763502, 'recall': 0.8473864567181638, 'f1-score': 0.8458879910589416, 'support': 29705.0} | {'precision': 0.8929161297147813, 'recall': 0.8917017337148628, 'f1-score': 0.8922798455096741, 'support': 29705.0} |
+| No log        | 6.0   | 246  | 0.2812          | {'precision': 0.5880121396054628, 'recall': 0.7437619961612284, 'f1-score': 0.6567796610169492, 'support': 4168.0}   | {'precision': 0.8901355773726041, 'recall': 0.8847583643122676, 'f1-score': 0.8874388254486133, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.999557991513437, 'f1-score': 0.9997789469030461, 'support': 11312.0}                 | {'precision': 0.9225448257031037, 'recall': 0.8395593473039012, 'f1-score': 0.8790980052038161, 'support': 12073.0} | 0.8903   | {'precision': 0.8501731356702926, 'recall': 0.8669094248227085, 'f1-score': 0.8557738596431061, 'support': 29705.0} | {'precision': 0.9027534099005212, 'recall': 0.8903214946978623, 'f1-score': 0.8944647582453118, 'support': 29705.0} |
+| No log        | 7.0   | 287  | 0.3027          | {'precision': 0.6093205574912892, 'recall': 0.6713051823416507, 'f1-score': 0.6388127853881279, 'support': 4168.0}   | {'precision': 0.905252822778596, 'recall': 0.8568773234200744, 'f1-score': 0.8804010503700167, 'support': 2152.0}  | {'precision': 1.0, 'recall': 0.9998231966053748, 'f1-score': 0.9999115904871364, 'support': 11312.0}                | {'precision': 0.8979262281149074, 'recall': 0.8750931831359231, 'f1-score': 0.886362682998448, 'support': 12073.0}  | 0.8927   | {'precision': 0.8531249020961982, 'recall': 0.8507747213757557, 'f1-score': 0.8513720273109323, 'support': 29705.0} | {'precision': 0.8968327052777144, 'recall': 0.8926780003366437, 'f1-score': 0.894437008359695, 'support': 29705.0}  |
+| No log        | 8.0   | 328  | 0.3308          | {'precision': 0.6094457623463446, 'recall': 0.6780230326295585, 'f1-score': 0.64190800681431, 'support': 4168.0}     | {'precision': 0.8877551020408163, 'recall': 0.8489776951672863, 'f1-score': 0.8679334916864608, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9993811881188119, 'f1-score': 0.9996904982977407, 'support': 11312.0}                | {'precision': 0.9026911576249466, 'recall': 0.875176012590077, 'f1-score': 0.8887206661619985, 'support': 12073.0}  | 0.8929   | {'precision': 0.8499730055030268, 'recall': 0.8503894821264335, 'f1-score': 0.8495631657401276, 'support': 29705.0} | {'precision': 0.8975192480409824, 'recall': 0.8929136509005218, 'f1-score': 0.8948422476293271, 'support': 29705.0} |
+| No log        | 9.0   | 369  | 0.3408          | {'precision': 0.651685393258427, 'recall': 0.6261996161228407, 'f1-score': 0.63868836412578, 'support': 4168.0}      | {'precision': 0.9157330735509012, 'recall': 0.8736059479553904, 'f1-score': 0.8941736028537456, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9993811881188119, 'f1-score': 0.9996904982977407, 'support': 11312.0}                | {'precision': 0.8864851725814292, 'recall': 0.9062370578977884, 'f1-score': 0.8962523039115298, 'support': 12073.0} | 0.9001   | {'precision': 0.8634759098476893, 'recall': 0.8513559525237079, 'f1-score': 0.857201192297199, 'support': 29705.0}  | {'precision': 0.8988863080948749, 'recall': 0.9000504965494025, 'f1-score': 0.8993525560304815, 'support': 29705.0} |
+| No log        | 10.0  | 410  | 0.4050          | {'precision': 0.6122782446311859, 'recall': 0.6293186180422264, 'f1-score': 0.6206814955040227, 'support': 4168.0}   | {'precision': 0.8178170144462279, 'recall': 0.9470260223048327, 'f1-score': 0.8776916451335055, 'support': 2152.0} | {'precision': 0.9999116061168567, 'recall': 1.0, 'f1-score': 0.9999558011049724, 'support': 11312.0}                | {'precision': 0.9038395316804407, 'recall': 0.869626439161766, 'f1-score': 0.8864029718434716, 'support': 12073.0}  | 0.8912   | {'precision': 0.8334615992186778, 'recall': 0.8614927698772062, 'f1-score': 0.8461829783964931, 'support': 29705.0} | {'precision': 0.8932830396594146, 'recall': 0.89116310385457, 'f1-score': 0.8917298769484514, 'support': 29705.0}   |
+| No log        | 11.0  | 451  | 0.4124          | {'precision': 0.5987719669701461, 'recall': 0.6785028790786948, 'f1-score': 0.6361489146327746, 'support': 4168.0}   | {'precision': 0.9018375241779497, 'recall': 0.866635687732342, 'f1-score': 0.8838862559241706, 'support': 2152.0}  | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11312.0}                                              | {'precision': 0.90087915876573, 'recall': 0.8657334548165327, 'f1-score': 0.8829567053854277, 'support': 12073.0}   | 0.8907   | {'precision': 0.8503721624784565, 'recall': 0.8527180054068924, 'f1-score': 0.8507479689855932, 'support': 29705.0} | {'precision': 0.8963053356048198, 'recall': 0.8906581383605454, 'f1-score': 0.8929650968879477, 'support': 29705.0} |
+| No log        | 12.0  | 492  | 0.4421          | {'precision': 0.6705202312138728, 'recall': 0.5566218809980806, 'f1-score': 0.6082852648138437, 'support': 4168.0}   | {'precision': 0.9058880308880309, 'recall': 0.8722118959107806, 'f1-score': 0.8887310606060606, 'support': 2152.0} | {'precision': 0.9999115513886432, 'recall': 0.9993811881188119, 'f1-score': 0.9996462994075515, 'support': 11312.0} | {'precision': 0.8699774617237895, 'recall': 0.9271929097987244, 'f1-score': 0.8976744186046511, 'support': 12073.0} | 0.8987   | {'precision': 0.861574318803584, 'recall': 0.8388519687065994, 'f1-score': 0.8485842608580267, 'support': 29705.0}  | {'precision': 0.8940729416216161, 'recall': 0.8987039218986702, 'f1-score': 0.8952534731823099, 'support': 29705.0} |
+| 0.1687        | 13.0  | 533  | 0.4406          | {'precision': 0.6625574087503021, 'recall': 0.6576295585412668, 'f1-score': 0.6600842865743528, 'support': 4168.0}   | {'precision': 0.8832599118942731, 'recall': 0.9316914498141264, 'f1-score': 0.9068294889190412, 'support': 2152.0} | {'precision': 0.9999115826702034, 'recall': 0.9997347949080623, 'f1-score': 0.9998231809742728, 'support': 11312.0} | {'precision': 0.9014848181514848, 'recall': 0.8951379110411662, 'f1-score': 0.8983001537758197, 'support': 12073.0} | 0.9043   | {'precision': 0.8618034303665659, 'recall': 0.8710484285761554, 'f1-score': 0.8662592775608715, 'support': 29705.0} | {'precision': 0.9041218866445364, 'recall': 0.9042922066992088, 'f1-score': 0.9041543829763381, 'support': 29705.0} |
+| 0.1687        | 14.0  | 574  | 0.4457          | {'precision': 0.6631526104417671, 'recall': 0.6338771593090211, 'f1-score': 0.6481844946025515, 'support': 4168.0}   | {'precision': 0.9062062529164723, 'recall': 0.9024163568773235, 'f1-score': 0.9043073341094298, 'support': 2152.0} | {'precision': 0.9999115748518879, 'recall': 0.9996463932107497, 'f1-score': 0.9997789664471067, 'support': 11312.0} | {'precision': 0.8905371260901459, 'recall': 0.90499461608548, 'f1-score': 0.897707665762879, 'support': 12073.0}    | 0.9028   | {'precision': 0.8649518910750683, 'recall': 0.8602336313706436, 'f1-score': 0.8624946152304918, 'support': 29705.0} | {'precision': 0.9014182930351262, 'recall': 0.9028109745834034, 'f1-score': 0.902044324986091, 'support': 29705.0}  |
+| 0.1687        | 15.0  | 615  | 0.4688          | {'precision': 0.6694429984383133, 'recall': 0.6170825335892515, 'f1-score': 0.6421972534332085, 'support': 4168.0}   | {'precision': 0.925692083535697, 'recall': 0.8856877323420075, 'f1-score': 0.905248159582047, 'support': 2152.0}   | {'precision': 0.9999115826702034, 'recall': 0.9997347949080623, 'f1-score': 0.9998231809742728, 'support': 11312.0} | {'precision': 0.882983832239475, 'recall': 0.9137745382257931, 'f1-score': 0.8981153580005699, 'support': 12073.0}  | 0.9028   | {'precision': 0.8695076242209221, 'recall': 0.8540698997662786, 'f1-score': 0.8613459879975246, 'support': 29705.0} | {'precision': 0.9006427002542411, 'recall': 0.9028446389496718, 'f1-score': 0.9014549312254513, 'support': 29705.0} |
+| 0.1687        | 16.0  | 656  | 0.4627          | {'precision': 0.6641901931649331, 'recall': 0.6434740882917467, 'f1-score': 0.6536680477699245, 'support': 4168.0}   | {'precision': 0.9209900047596382, 'recall': 0.8991635687732342, 'f1-score': 0.909945920526687, 'support': 2152.0}  | {'precision': 1.0, 'recall': 0.9999115983026874, 'f1-score': 0.9999557971975424, 'support': 11312.0}                | {'precision': 0.8908200734394125, 'recall': 0.9042491509980949, 'f1-score': 0.8974843801381124, 'support': 12073.0} | 0.9037   | {'precision': 0.8690000678409959, 'recall': 0.8616996015914409, 'f1-score': 0.8652635364080665, 'support': 29705.0} | {'precision': 0.9027835705096182, 'recall': 0.9037199124726477, 'f1-score': 0.9031988198412557, 'support': 29705.0} |
 ### Framework versions

meta_data/README_s42_e16.md CHANGED Viewed

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: sep_tok
-      split: train[60%:80%]
       args: sep_tok
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9117647058823529
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,14 +32,14 @@ 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: 0.4143
-- Claim: {'precision': 0.6836630634305527, 'recall': 0.6785425101214575, 'f1-score': 0.6810931626536625, 'support': 4940.0}
-- Majorclaim: {'precision': 0.8679727427597955, 'recall': 0.9314442413162706, 'f1-score': 0.8985890652557319, 'support': 2188.0}
-- O: {'precision': 0.999474513925381, 'recall': 0.9970793080206695, 'f1-score': 0.9982754742445827, 'support': 13353.0}
-- Premise: {'precision': 0.9151062753036437, 'recall': 0.9098685451915215, 'f1-score': 0.9124798940297096, 'support': 15899.0}
-- Accuracy: 0.9118
-- Macro avg: {'precision': 0.8665541488548433, 'recall': 0.8792336511624799, 'f1-score': 0.8726093990459217, 'support': 36380.0}
-- Weighted avg: {'precision': 0.9118108232546347, 'recall': 0.9117647058823529, 'f1-score': 0.9117153199850166, 'support': 36380.0}
 ## Model description
@@ -68,24 +68,24 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                   | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.3470          | {'precision': 0.564843099139128, 'recall': 0.4117408906882591, 'f1-score': 0.476290832455216, 'support': 4940.0}    | {'precision': 0.5601374570446735, 'recall': 0.8939670932358318, 'f1-score': 0.6887323943661973, 'support': 2188.0} | {'precision': 0.9936889556724268, 'recall': 0.9904890286826931, 'f1-score': 0.9920864118816337, 'support': 13353.0} | {'precision': 0.8845214996557551, 'recall': 0.888860934649978, 'f1-score': 0.8866859078930858, 'support': 15899.0}  | 0.8617   | {'precision': 0.7507977528779959, 'recall': 0.7962644868141905, 'f1-score': 0.7609488866490333, 'support': 36380.0} | {'precision': 0.8616723918054371, 'recall': 0.8616822429906542, 'f1-score': 0.8577397553229695, 'support': 36380.0} |
-| No log        | 2.0   | 82   | 0.2482          | {'precision': 0.609009009009009, 'recall': 0.6842105263157895, 'f1-score': 0.6444232602478551, 'support': 4940.0}   | {'precision': 0.8622502628811777, 'recall': 0.7495429616087751, 'f1-score': 0.8019559902200488, 'support': 2188.0} | {'precision': 0.9991750412479377, 'recall': 0.9977533138620535, 'f1-score': 0.998463671450519, 'support': 13353.0}  | {'precision': 0.9146466589713993, 'recall': 0.8971004465689666, 'f1-score': 0.9057885879401772, 'support': 15899.0} | 0.8963   | {'precision': 0.846270243027381, 'recall': 0.8321518120888962, 'f1-score': 0.83765787746465, 'support': 36380.0}    | {'precision': 0.901018681595891, 'recall': 0.8962616822429906, 'f1-score': 0.898068960328904, 'support': 36380.0}   |
-| No log        | 3.0   | 123  | 0.2560          | {'precision': 0.6481652790625964, 'recall': 0.42550607287449393, 'f1-score': 0.5137480141757302, 'support': 4940.0} | {'precision': 0.9458357600465929, 'recall': 0.7422303473491774, 'f1-score': 0.8317541613316262, 'support': 2188.0} | {'precision': 0.9993245271690183, 'recall': 0.997154197558601, 'f1-score': 0.9982381827042021, 'support': 13353.0}  | {'precision': 0.8452144120247569, 'recall': 0.962010189320083, 'f1-score': 0.8998382115016914, 'support': 15899.0}  | 0.8888   | {'precision': 0.8596349945757411, 'recall': 0.7817252017755888, 'f1-score': 0.8108946424283126, 'support': 36380.0} | {'precision': 0.8810739271473523, 'recall': 0.8888400219901045, 'f1-score': 0.8794336303830761, 'support': 36380.0} |
-| No log        | 4.0   | 164  | 0.2400          | {'precision': 0.7123867069486405, 'recall': 0.4773279352226721, 'f1-score': 0.5716363636363636, 'support': 4940.0}  | {'precision': 0.9357889497262319, 'recall': 0.8592321755027422, 'f1-score': 0.8958780081010246, 'support': 2188.0} | {'precision': 0.999699270731524, 'recall': 0.9958061858758331, 'f1-score': 0.9977489307421025, 'support': 13353.0}  | {'precision': 0.8584459459459459, 'recall': 0.958928234480156, 'f1-score': 0.9059092664666211, 'support': 15899.0}  | 0.9011   | {'precision': 0.8765802183380855, 'recall': 0.8228236327703509, 'f1-score': 0.8427931422365279, 'support': 36380.0} | {'precision': 0.8951103081638239, 'recall': 0.9010720175920836, 'f1-score': 0.8936244534865525, 'support': 36380.0} |
-| No log        | 5.0   | 205  | 0.2714          | {'precision': 0.7058679106309845, 'recall': 0.5819838056680162, 'f1-score': 0.6379673804504604, 'support': 4940.0}  | {'precision': 0.9069548872180451, 'recall': 0.8820840950639853, 'f1-score': 0.8943466172381834, 'support': 2188.0} | {'precision': 0.999171686746988, 'recall': 0.9937092788137497, 'f1-score': 0.9964329966582812, 'support': 13353.0}  | {'precision': 0.8842535061246227, 'recall': 0.939870432102648, 'f1-score': 0.9112140984206354, 'support': 15899.0}  | 0.9076   | {'precision': 0.8740619976801601, 'recall': 0.8494119029120998, 'f1-score': 0.8599902731918901, 'support': 36380.0} | {'precision': 0.9035758878163292, 'recall': 0.9075590984057175, 'f1-score': 0.9043747117402454, 'support': 36380.0} |
-| No log        | 6.0   | 246  | 0.2659          | {'precision': 0.6969630281690141, 'recall': 0.6410931174089068, 'f1-score': 0.6678616617460986, 'support': 4940.0}  | {'precision': 0.8673602080624188, 'recall': 0.9145338208409507, 'f1-score': 0.8903225806451615, 'support': 2188.0} | {'precision': 1.0, 'recall': 0.9901145809930353, 'f1-score': 0.9950327387672161, 'support': 13353.0}                | {'precision': 0.8999877360804514, 'recall': 0.9231398201144726, 'f1-score': 0.9114167727512653, 'support': 15899.0} | 0.9089   | {'precision': 0.8660777430779711, 'recall': 0.8672203348393414, 'f1-score': 0.8661584384774353, 'support': 36380.0} | {'precision': 0.9071656544897909, 'recall': 0.9089059923034635, 'f1-score': 0.9077666257891245, 'support': 36380.0} |
-| No log        | 7.0   | 287  | 0.3056          | {'precision': 0.7278708133971292, 'recall': 0.49271255060728747, 'f1-score': 0.5876388218252052, 'support': 4940.0} | {'precision': 0.8874715261958998, 'recall': 0.8903107861060329, 'f1-score': 0.888888888888889, 'support': 2188.0}  | {'precision': 1.0, 'recall': 0.9936343892758182, 'f1-score': 0.9968070320423725, 'support': 13353.0}                | {'precision': 0.8625732658054971, 'recall': 0.9533932951757972, 'f1-score': 0.9057122370936902, 'support': 15899.0} | 0.9018   | {'precision': 0.8694789013496315, 'recall': 0.832512755291234, 'f1-score': 0.8447617449625392, 'support': 36380.0}  | {'precision': 0.8962210519664664, 'recall': 0.9018141836173722, 'f1-score': 0.8949452398328692, 'support': 36380.0} |
-| No log        | 8.0   | 328  | 0.3121          | {'precision': 0.7013651877133106, 'recall': 0.665587044534413, 'f1-score': 0.6830078936435396, 'support': 4940.0}   | {'precision': 0.9269796111901375, 'recall': 0.893510054844607, 'f1-score': 0.9099371654642774, 'support': 2188.0}  | {'precision': 0.9998493181646952, 'recall': 0.9938590578896128, 'f1-score': 0.9968451889130925, 'support': 13353.0} | {'precision': 0.9029429797670141, 'recall': 0.9262846719919492, 'f1-score': 0.9144649011145954, 'support': 15899.0} | 0.9137   | {'precision': 0.8827842742087894, 'recall': 0.8698102073151455, 'f1-score': 0.8760637872838762, 'support': 36380.0} | {'precision': 0.9125853160350936, 'recall': 0.9137163276525564, 'f1-score': 0.9130003513747224, 'support': 36380.0} |
-| No log        | 9.0   | 369  | 0.3160          | {'precision': 0.6483785822021116, 'recall': 0.6961538461538461, 'f1-score': 0.6714174150722373, 'support': 4940.0}  | {'precision': 0.8652871621621622, 'recall': 0.9364716636197441, 'f1-score': 0.8994732221246707, 'support': 2188.0} | {'precision': 0.9996248217903504, 'recall': 0.9976784243241219, 'f1-score': 0.9986506746626687, 'support': 13353.0} | {'precision': 0.9191860087120474, 'recall': 0.8892383168752752, 'f1-score': 0.9039641943734017, 'support': 15899.0} | 0.9057   | {'precision': 0.8581191437166679, 'recall': 0.8798855627432468, 'f1-score': 0.8683763765582445, 'support': 36380.0} | {'precision': 0.9086961820991927, 'recall': 0.9056624518966465, 'f1-score': 0.9068707703567609, 'support': 36380.0} |
-| No log        | 10.0  | 410  | 0.3451          | {'precision': 0.7022722446379274, 'recall': 0.6694331983805668, 'f1-score': 0.6854596331226034, 'support': 4940.0}  | {'precision': 0.924015009380863, 'recall': 0.9003656307129799, 'f1-score': 0.912037037037037, 'support': 2188.0}   | {'precision': 0.999474435017644, 'recall': 0.9969295289448065, 'f1-score': 0.9982003599280145, 'support': 13353.0}  | {'precision': 0.9051787916152898, 'recall': 0.9234543053022203, 'f1-score': 0.9142252249447368, 'support': 15899.0} | 0.9145   | {'precision': 0.882735120162931, 'recall': 0.8725456658351434, 'f1-score': 0.8774805637580978, 'support': 36380.0}  | {'precision': 0.9133696939999666, 'recall': 0.9145409565695437, 'f1-score': 0.9138522232594241, 'support': 36380.0} |
-| No log        | 11.0  | 451  | 0.3666          | {'precision': 0.6846517626827171, 'recall': 0.6447368421052632, 'f1-score': 0.6640950792326938, 'support': 4940.0}  | {'precision': 0.9058558558558558, 'recall': 0.9191042047531993, 'f1-score': 0.9124319419237751, 'support': 2188.0} | {'precision': 0.9991744220954668, 'recall': 0.9970044184827379, 'f1-score': 0.9980882408066872, 'support': 13353.0} | {'precision': 0.9018783984181908, 'recall': 0.9180451600729606, 'f1-score': 0.9098899728828351, 'support': 15899.0} | 0.9100   | {'precision': 0.8728901097630577, 'recall': 0.8697226563535402, 'f1-score': 0.8711263087114978, 'support': 36380.0} | {'precision': 0.9083324088773178, 'recall': 0.909978009895547, 'f1-score': 0.9090391352032605, 'support': 36380.0}  |
-| No log        | 12.0  | 492  | 0.4192          | {'precision': 0.7116060961313013, 'recall': 0.6143724696356275, 'f1-score': 0.6594242259641498, 'support': 4940.0}  | {'precision': 0.8527354997943233, 'recall': 0.9474405850091407, 'f1-score': 0.897596882442087, 'support': 2188.0}  | {'precision': 0.9988748030905409, 'recall': 0.9972290870965326, 'f1-score': 0.9980512666766601, 'support': 13353.0} | {'precision': 0.9013636641594814, 'recall': 0.9271023334800931, 'f1-score': 0.9140518417462483, 'support': 15899.0} | 0.9116   | {'precision': 0.8661450157939118, 'recall': 0.8715361188053484, 'f1-score': 0.8672810542072864, 'support': 36380.0} | {'precision': 0.9084627688449202, 'recall': 0.9115997800989555, 'f1-score': 0.9093179343293907, 'support': 36380.0} |
-| 0.1714        | 13.0  | 533  | 0.4313          | {'precision': 0.6997058157954288, 'recall': 0.625910931174089, 'f1-score': 0.6607543540976599, 'support': 4940.0}   | {'precision': 0.8979409131602507, 'recall': 0.916819012797075, 'f1-score': 0.9072817729534148, 'support': 2188.0}  | {'precision': 0.9991729945116908, 'recall': 0.9952819591103123, 'f1-score': 0.9972236812485931, 'support': 13353.0} | {'precision': 0.8959576281504931, 'recall': 0.9256557016164538, 'f1-score': 0.9105645784996134, 'support': 15899.0} | 0.9100   | {'precision': 0.8731943379044659, 'recall': 0.8659169011744825, 'f1-score': 0.8689560966998203, 'support': 36380.0} | {'precision': 0.9073125006515488, 'recall': 0.909978009895547, 'f1-score': 0.9082532456773593, 'support': 36380.0}  |
-| 0.1714        | 14.0  | 574  | 0.4222          | {'precision': 0.7030596522121946, 'recall': 0.6465587044534413, 'f1-score': 0.6736264895075399, 'support': 4940.0}  | {'precision': 0.8805194805194805, 'recall': 0.9296160877513712, 'f1-score': 0.9044019564250778, 'support': 2188.0} | {'precision': 0.9993238674780257, 'recall': 0.9961806335654909, 'f1-score': 0.9977497749774977, 'support': 13353.0} | {'precision': 0.9044770596941293, 'recall': 0.9225108497389773, 'f1-score': 0.9134049509574966, 'support': 15899.0} | 0.9125   | {'precision': 0.8718450149759575, 'recall': 0.8737165688773202, 'f1-score': 0.8722957929669031, 'support': 36380.0} | {'precision': 0.9104987267678919, 'recall': 0.9125068719076416, 'f1-score': 0.911262352923944, 'support': 36380.0}  |
-| 0.1714        | 15.0  | 615  | 0.4086          | {'precision': 0.6842319430315361, 'recall': 0.6807692307692308, 'f1-score': 0.6824961948249619, 'support': 4940.0}  | {'precision': 0.8801384681955863, 'recall': 0.9296160877513712, 'f1-score': 0.9042009335407869, 'support': 2188.0} | {'precision': 0.9993994895661312, 'recall': 0.9970793080206695, 'f1-score': 0.9982380506091845, 'support': 13353.0} | {'precision': 0.9139717028802425, 'recall': 0.9101201333417196, 'f1-score': 0.9120418518168354, 'support': 15899.0} | 0.9121   | {'precision': 0.869435400918374, 'recall': 0.8793961899707478, 'f1-score': 0.8742442576979421, 'support': 36380.0}  | {'precision': 0.9120963786491826, 'recall': 0.9120670698185817, 'f1-score': 0.9120381785828164, 'support': 36380.0} |
-| 0.1714        | 16.0  | 656  | 0.4143          | {'precision': 0.6836630634305527, 'recall': 0.6785425101214575, 'f1-score': 0.6810931626536625, 'support': 4940.0}  | {'precision': 0.8679727427597955, 'recall': 0.9314442413162706, 'f1-score': 0.8985890652557319, 'support': 2188.0} | {'precision': 0.999474513925381, 'recall': 0.9970793080206695, 'f1-score': 0.9982754742445827, 'support': 13353.0}  | {'precision': 0.9151062753036437, 'recall': 0.9098685451915215, 'f1-score': 0.9124798940297096, 'support': 15899.0} | 0.9118   | {'precision': 0.8665541488548433, 'recall': 0.8792336511624799, 'f1-score': 0.8726093990459217, 'support': 36380.0} | {'precision': 0.9118108232546347, 'recall': 0.9117647058823529, 'f1-score': 0.9117153199850166, 'support': 36380.0} |
 ### Framework versions

       name: essays_su_g
       type: essays_su_g
       config: sep_tok
+      split: train[80%:100%]
       args: sep_tok
     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.9037199124726477
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 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: 0.4627
+- Claim: {'precision': 0.6641901931649331, 'recall': 0.6434740882917467, 'f1-score': 0.6536680477699245, 'support': 4168.0}
+- Majorclaim: {'precision': 0.9209900047596382, 'recall': 0.8991635687732342, 'f1-score': 0.909945920526687, 'support': 2152.0}
+- O: {'precision': 1.0, 'recall': 0.9999115983026874, 'f1-score': 0.9999557971975424, 'support': 11312.0}
+- Premise: {'precision': 0.8908200734394125, 'recall': 0.9042491509980949, 'f1-score': 0.8974843801381124, 'support': 12073.0}
+- Accuracy: 0.9037
+- Macro avg: {'precision': 0.8690000678409959, 'recall': 0.8616996015914409, 'f1-score': 0.8652635364080665, 'support': 29705.0}
+- Weighted avg: {'precision': 0.9027835705096182, 'recall': 0.9037199124726477, 'f1-score': 0.9031988198412557, 'support': 29705.0}
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                                         | O                                                                                                                   | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.3480          | {'precision': 0.45800144822592326, 'recall': 0.30350287907869483, 'f1-score': 0.3650793650793651, 'support': 4168.0} | {'precision': 0.6792168674698795, 'recall': 0.6287174721189591, 'f1-score': 0.652992277992278, 'support': 2152.0}  | {'precision': 0.9994626063591581, 'recall': 0.986474540311174, 'f1-score': 0.9929261022378432, 'support': 11312.0}  | {'precision': 0.8190918322936313, 'recall': 0.9353101963058064, 'f1-score': 0.873351637727677, 'support': 12073.0}  | 0.8439   | {'precision': 0.7389431885871479, 'recall': 0.7135012719536586, 'f1-score': 0.7210873457592908, 'support': 29705.0} | {'precision': 0.8269800178224755, 'recall': 0.8439319979801381, 'f1-score': 0.8316056073620907, 'support': 29705.0} |
+| No log        | 2.0   | 82   | 0.2758          | {'precision': 0.6302521008403361, 'recall': 0.32389635316698656, 'f1-score': 0.427892234548336, 'support': 4168.0}   | {'precision': 0.7593291404612159, 'recall': 0.841542750929368, 'f1-score': 0.7983248842847697, 'support': 2152.0}  | {'precision': 0.9997344192634561, 'recall': 0.9983203677510608, 'f1-score': 0.9990268931351733, 'support': 11312.0} | {'precision': 0.833381357153148, 'recall': 0.9582539551064359, 'f1-score': 0.8914659988441533, 'support': 12073.0}  | 0.8760   | {'precision': 0.805674254429539, 'recall': 0.7805033567384627, 'f1-score': 0.779177502703108, 'support': 29705.0}   | {'precision': 0.8628640276786139, 'recall': 0.876047803400101, 'f1-score': 0.8606332672536217, 'support': 29705.0}  |
+| No log        | 3.0   | 123  | 0.2410          | {'precision': 0.620671283963772, 'recall': 0.559021113243762, 'f1-score': 0.5882352941176471, 'support': 4168.0}     | {'precision': 0.8549924736578023, 'recall': 0.79182156133829, 'f1-score': 0.822195416164053, 'support': 2152.0}    | {'precision': 0.9999115357395613, 'recall': 0.9992043847241867, 'f1-score': 0.999557835160948, 'support': 11312.0}  | {'precision': 0.8765607712976135, 'recall': 0.918744305475027, 'f1-score': 0.897156953936992, 'support': 12073.0}   | 0.8897   | {'precision': 0.8380340161646872, 'recall': 0.8171978411953165, 'f1-score': 0.8267863748449099, 'support': 29705.0} | {'precision': 0.8860669651248813, 'recall': 0.8897155361050328, 'f1-score': 0.8873759763571568, 'support': 29705.0} |
+| No log        | 4.0   | 164  | 0.2487          | {'precision': 0.6344057431534167, 'recall': 0.5724568138195777, 'f1-score': 0.6018413419094463, 'support': 4168.0}   | {'precision': 0.8230162027420025, 'recall': 0.9205390334572491, 'f1-score': 0.8690502303136654, 'support': 2152.0} | {'precision': 0.9998231027772864, 'recall': 0.9992927864214993, 'f1-score': 0.9995578742594394, 'support': 11312.0} | {'precision': 0.8858637887335459, 'recall': 0.8974571357574753, 'f1-score': 0.8916227781435153, 'support': 12073.0} | 0.8923   | {'precision': 0.8357772093515627, 'recall': 0.8474364423639503, 'f1-score': 0.8405180561565165, 'support': 29705.0} | {'precision': 0.8894248936462209, 'recall': 0.8923076923076924, 'f1-score': 0.8904302737876794, 'support': 29705.0} |
+| No log        | 5.0   | 205  | 0.2594          | {'precision': 0.6126252038201724, 'recall': 0.6309980806142035, 'f1-score': 0.6216759248315801, 'support': 4168.0}   | {'precision': 0.8722222222222222, 'recall': 0.8754646840148699, 'f1-score': 0.8738404452690167, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9992927864214993, 'f1-score': 0.9996462681287585, 'support': 11312.0}                | {'precision': 0.8930364914630063, 'recall': 0.8837902758220824, 'f1-score': 0.8883893260064111, 'support': 12073.0} | 0.8917   | {'precision': 0.8444709793763502, 'recall': 0.8473864567181638, 'f1-score': 0.8458879910589416, 'support': 29705.0} | {'precision': 0.8929161297147813, 'recall': 0.8917017337148628, 'f1-score': 0.8922798455096741, 'support': 29705.0} |
+| No log        | 6.0   | 246  | 0.2812          | {'precision': 0.5880121396054628, 'recall': 0.7437619961612284, 'f1-score': 0.6567796610169492, 'support': 4168.0}   | {'precision': 0.8901355773726041, 'recall': 0.8847583643122676, 'f1-score': 0.8874388254486133, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.999557991513437, 'f1-score': 0.9997789469030461, 'support': 11312.0}                 | {'precision': 0.9225448257031037, 'recall': 0.8395593473039012, 'f1-score': 0.8790980052038161, 'support': 12073.0} | 0.8903   | {'precision': 0.8501731356702926, 'recall': 0.8669094248227085, 'f1-score': 0.8557738596431061, 'support': 29705.0} | {'precision': 0.9027534099005212, 'recall': 0.8903214946978623, 'f1-score': 0.8944647582453118, 'support': 29705.0} |
+| No log        | 7.0   | 287  | 0.3027          | {'precision': 0.6093205574912892, 'recall': 0.6713051823416507, 'f1-score': 0.6388127853881279, 'support': 4168.0}   | {'precision': 0.905252822778596, 'recall': 0.8568773234200744, 'f1-score': 0.8804010503700167, 'support': 2152.0}  | {'precision': 1.0, 'recall': 0.9998231966053748, 'f1-score': 0.9999115904871364, 'support': 11312.0}                | {'precision': 0.8979262281149074, 'recall': 0.8750931831359231, 'f1-score': 0.886362682998448, 'support': 12073.0}  | 0.8927   | {'precision': 0.8531249020961982, 'recall': 0.8507747213757557, 'f1-score': 0.8513720273109323, 'support': 29705.0} | {'precision': 0.8968327052777144, 'recall': 0.8926780003366437, 'f1-score': 0.894437008359695, 'support': 29705.0}  |
+| No log        | 8.0   | 328  | 0.3308          | {'precision': 0.6094457623463446, 'recall': 0.6780230326295585, 'f1-score': 0.64190800681431, 'support': 4168.0}     | {'precision': 0.8877551020408163, 'recall': 0.8489776951672863, 'f1-score': 0.8679334916864608, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9993811881188119, 'f1-score': 0.9996904982977407, 'support': 11312.0}                | {'precision': 0.9026911576249466, 'recall': 0.875176012590077, 'f1-score': 0.8887206661619985, 'support': 12073.0}  | 0.8929   | {'precision': 0.8499730055030268, 'recall': 0.8503894821264335, 'f1-score': 0.8495631657401276, 'support': 29705.0} | {'precision': 0.8975192480409824, 'recall': 0.8929136509005218, 'f1-score': 0.8948422476293271, 'support': 29705.0} |
+| No log        | 9.0   | 369  | 0.3408          | {'precision': 0.651685393258427, 'recall': 0.6261996161228407, 'f1-score': 0.63868836412578, 'support': 4168.0}      | {'precision': 0.9157330735509012, 'recall': 0.8736059479553904, 'f1-score': 0.8941736028537456, 'support': 2152.0} | {'precision': 1.0, 'recall': 0.9993811881188119, 'f1-score': 0.9996904982977407, 'support': 11312.0}                | {'precision': 0.8864851725814292, 'recall': 0.9062370578977884, 'f1-score': 0.8962523039115298, 'support': 12073.0} | 0.9001   | {'precision': 0.8634759098476893, 'recall': 0.8513559525237079, 'f1-score': 0.857201192297199, 'support': 29705.0}  | {'precision': 0.8988863080948749, 'recall': 0.9000504965494025, 'f1-score': 0.8993525560304815, 'support': 29705.0} |
+| No log        | 10.0  | 410  | 0.4050          | {'precision': 0.6122782446311859, 'recall': 0.6293186180422264, 'f1-score': 0.6206814955040227, 'support': 4168.0}   | {'precision': 0.8178170144462279, 'recall': 0.9470260223048327, 'f1-score': 0.8776916451335055, 'support': 2152.0} | {'precision': 0.9999116061168567, 'recall': 1.0, 'f1-score': 0.9999558011049724, 'support': 11312.0}                | {'precision': 0.9038395316804407, 'recall': 0.869626439161766, 'f1-score': 0.8864029718434716, 'support': 12073.0}  | 0.8912   | {'precision': 0.8334615992186778, 'recall': 0.8614927698772062, 'f1-score': 0.8461829783964931, 'support': 29705.0} | {'precision': 0.8932830396594146, 'recall': 0.89116310385457, 'f1-score': 0.8917298769484514, 'support': 29705.0}   |
+| No log        | 11.0  | 451  | 0.4124          | {'precision': 0.5987719669701461, 'recall': 0.6785028790786948, 'f1-score': 0.6361489146327746, 'support': 4168.0}   | {'precision': 0.9018375241779497, 'recall': 0.866635687732342, 'f1-score': 0.8838862559241706, 'support': 2152.0}  | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11312.0}                                              | {'precision': 0.90087915876573, 'recall': 0.8657334548165327, 'f1-score': 0.8829567053854277, 'support': 12073.0}   | 0.8907   | {'precision': 0.8503721624784565, 'recall': 0.8527180054068924, 'f1-score': 0.8507479689855932, 'support': 29705.0} | {'precision': 0.8963053356048198, 'recall': 0.8906581383605454, 'f1-score': 0.8929650968879477, 'support': 29705.0} |
+| No log        | 12.0  | 492  | 0.4421          | {'precision': 0.6705202312138728, 'recall': 0.5566218809980806, 'f1-score': 0.6082852648138437, 'support': 4168.0}   | {'precision': 0.9058880308880309, 'recall': 0.8722118959107806, 'f1-score': 0.8887310606060606, 'support': 2152.0} | {'precision': 0.9999115513886432, 'recall': 0.9993811881188119, 'f1-score': 0.9996462994075515, 'support': 11312.0} | {'precision': 0.8699774617237895, 'recall': 0.9271929097987244, 'f1-score': 0.8976744186046511, 'support': 12073.0} | 0.8987   | {'precision': 0.861574318803584, 'recall': 0.8388519687065994, 'f1-score': 0.8485842608580267, 'support': 29705.0}  | {'precision': 0.8940729416216161, 'recall': 0.8987039218986702, 'f1-score': 0.8952534731823099, 'support': 29705.0} |
+| 0.1687        | 13.0  | 533  | 0.4406          | {'precision': 0.6625574087503021, 'recall': 0.6576295585412668, 'f1-score': 0.6600842865743528, 'support': 4168.0}   | {'precision': 0.8832599118942731, 'recall': 0.9316914498141264, 'f1-score': 0.9068294889190412, 'support': 2152.0} | {'precision': 0.9999115826702034, 'recall': 0.9997347949080623, 'f1-score': 0.9998231809742728, 'support': 11312.0} | {'precision': 0.9014848181514848, 'recall': 0.8951379110411662, 'f1-score': 0.8983001537758197, 'support': 12073.0} | 0.9043   | {'precision': 0.8618034303665659, 'recall': 0.8710484285761554, 'f1-score': 0.8662592775608715, 'support': 29705.0} | {'precision': 0.9041218866445364, 'recall': 0.9042922066992088, 'f1-score': 0.9041543829763381, 'support': 29705.0} |
+| 0.1687        | 14.0  | 574  | 0.4457          | {'precision': 0.6631526104417671, 'recall': 0.6338771593090211, 'f1-score': 0.6481844946025515, 'support': 4168.0}   | {'precision': 0.9062062529164723, 'recall': 0.9024163568773235, 'f1-score': 0.9043073341094298, 'support': 2152.0} | {'precision': 0.9999115748518879, 'recall': 0.9996463932107497, 'f1-score': 0.9997789664471067, 'support': 11312.0} | {'precision': 0.8905371260901459, 'recall': 0.90499461608548, 'f1-score': 0.897707665762879, 'support': 12073.0}    | 0.9028   | {'precision': 0.8649518910750683, 'recall': 0.8602336313706436, 'f1-score': 0.8624946152304918, 'support': 29705.0} | {'precision': 0.9014182930351262, 'recall': 0.9028109745834034, 'f1-score': 0.902044324986091, 'support': 29705.0}  |
+| 0.1687        | 15.0  | 615  | 0.4688          | {'precision': 0.6694429984383133, 'recall': 0.6170825335892515, 'f1-score': 0.6421972534332085, 'support': 4168.0}   | {'precision': 0.925692083535697, 'recall': 0.8856877323420075, 'f1-score': 0.905248159582047, 'support': 2152.0}   | {'precision': 0.9999115826702034, 'recall': 0.9997347949080623, 'f1-score': 0.9998231809742728, 'support': 11312.0} | {'precision': 0.882983832239475, 'recall': 0.9137745382257931, 'f1-score': 0.8981153580005699, 'support': 12073.0}  | 0.9028   | {'precision': 0.8695076242209221, 'recall': 0.8540698997662786, 'f1-score': 0.8613459879975246, 'support': 29705.0} | {'precision': 0.9006427002542411, 'recall': 0.9028446389496718, 'f1-score': 0.9014549312254513, 'support': 29705.0} |
+| 0.1687        | 16.0  | 656  | 0.4627          | {'precision': 0.6641901931649331, 'recall': 0.6434740882917467, 'f1-score': 0.6536680477699245, 'support': 4168.0}   | {'precision': 0.9209900047596382, 'recall': 0.8991635687732342, 'f1-score': 0.909945920526687, 'support': 2152.0}  | {'precision': 1.0, 'recall': 0.9999115983026874, 'f1-score': 0.9999557971975424, 'support': 11312.0}                | {'precision': 0.8908200734394125, 'recall': 0.9042491509980949, 'f1-score': 0.8974843801381124, 'support': 12073.0} | 0.9037   | {'precision': 0.8690000678409959, 'recall': 0.8616996015914409, 'f1-score': 0.8652635364080665, 'support': 29705.0} | {'precision': 0.9027835705096182, 'recall': 0.9037199124726477, 'f1-score': 0.9031988198412557, 'support': 29705.0} |
 ### Framework versions

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