trainer: training complete at 2024-03-02 11:55:54.389485.

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: 0.9412361055794923
+      value: 0.9266721210881571
 ---
 
 <!-- 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: 0.2837
-- B: {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}
-- I: {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}
-- O: {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0}
-- Accuracy: 0.9412
-- Macro avg: {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0}
-- Weighted avg: {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0}
+- Loss: 0.4247
+- B: {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}
+- I: {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}
+- O: {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0}
+- Accuracy: 0.9267
+- Macro avg: {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0}
+- Weighted avg: {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0}
 
 ## Model description
 
@@ -63,27 +63,28 @@ 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: 15
+- num_epochs: 16
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
 |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2927          | {'precision': 0.8069852941176471, 'recall': 0.42090124640460214, 'f1-score': 0.5532451165721487, 'support': 1043.0} | {'precision': 0.8852390417407678, 'recall': 0.9754466858789625, 'f1-score': 0.9281561917297357, 'support': 17350.0} | {'precision': 0.9349000879728541, 'recall': 0.8063082592672881, 'f1-score': 0.8658557876971424, 'support': 9226.0} | 0.8980   | {'precision': 0.8757081412770896, 'recall': 0.7342187305169509, 'f1-score': 0.7824190319996757, 'support': 27619.0} | {'precision': 0.8988729225389979, 'recall': 0.8980049965603389, 'f1-score': 0.8931869394398603, 'support': 27619.0} |
-| No log        | 2.0   | 82   | 0.1958          | {'precision': 0.7986171132238548, 'recall': 0.8859060402684564, 'f1-score': 0.84, 'support': 1043.0}                | {'precision': 0.9361619307123394, 'recall': 0.9703170028818444, 'f1-score': 0.9529335182407381, 'support': 17350.0} | {'precision': 0.9455124425050124, 'recall': 0.8689572946022112, 'f1-score': 0.905619881389438, 'support': 9226.0}  | 0.9333   | {'precision': 0.8934304954804023, 'recall': 0.908393445917504, 'f1-score': 0.8995177998767253, 'support': 27619.0}  | {'precision': 0.9340912032116592, 'recall': 0.933270574604439, 'f1-score': 0.932863809956036, 'support': 27619.0}   |
-| No log        | 3.0   | 123  | 0.1754          | {'precision': 0.8552631578947368, 'recall': 0.87248322147651, 'f1-score': 0.8637873754152824, 'support': 1043.0}    | {'precision': 0.966759166322253, 'recall': 0.9437463976945245, 'f1-score': 0.9551141832181294, 'support': 17350.0}  | {'precision': 0.8988355167394468, 'recall': 0.9370257966616085, 'f1-score': 0.9175334323922734, 'support': 9226.0} | 0.9388   | {'precision': 0.9069526136521455, 'recall': 0.9177518052775477, 'f1-score': 0.9121449970085617, 'support': 27619.0} | {'precision': 0.9398590639347346, 'recall': 0.9388102393279989, 'f1-score': 0.9391116535227126, 'support': 27619.0} |
-| No log        | 4.0   | 164  | 0.1844          | {'precision': 0.861003861003861, 'recall': 0.8552253116011506, 'f1-score': 0.8581048581048581, 'support': 1043.0}   | {'precision': 0.9428187016481668, 'recall': 0.9693371757925072, 'f1-score': 0.9558940547914061, 'support': 17350.0} | {'precision': 0.9376786735277302, 'recall': 0.8887925428137872, 'f1-score': 0.912581381114017, 'support': 9226.0}  | 0.9381   | {'precision': 0.9138337453932527, 'recall': 0.904451676735815, 'f1-score': 0.908860098003427, 'support': 27619.0}   | {'precision': 0.9380120548386821, 'recall': 0.9381223071074261, 'f1-score': 0.937732757876541, 'support': 27619.0}  |
-| No log        | 5.0   | 205  | 0.2030          | {'precision': 0.8463611859838275, 'recall': 0.9031639501438159, 'f1-score': 0.8738404452690166, 'support': 1043.0}  | {'precision': 0.9367116741679169, 'recall': 0.9716426512968299, 'f1-score': 0.9538574702237813, 'support': 17350.0} | {'precision': 0.9452344576330943, 'recall': 0.8717754172989378, 'f1-score': 0.9070200169157033, 'support': 9226.0} | 0.9357   | {'precision': 0.9094357725949461, 'recall': 0.9155273395798611, 'f1-score': 0.9115726441361671, 'support': 27619.0} | {'precision': 0.9361466877844027, 'recall': 0.9356964408559325, 'f1-score': 0.9351898826482664, 'support': 27619.0} |
-| No log        | 6.0   | 246  | 0.1880          | {'precision': 0.8593012275731823, 'recall': 0.87248322147651, 'f1-score': 0.8658420551855375, 'support': 1043.0}    | {'precision': 0.9416148372275452, 'recall': 0.9685878962536023, 'f1-score': 0.954910929908799, 'support': 17350.0}  | {'precision': 0.9369907035464249, 'recall': 0.8848905267721656, 'f1-score': 0.9101956630804393, 'support': 9226.0} | 0.9370   | {'precision': 0.9126355894490508, 'recall': 0.9086538815007593, 'f1-score': 0.9103162160582586, 'support': 27619.0} | {'precision': 0.9369616871420418, 'recall': 0.9369998913791231, 'f1-score': 0.9366104162010322, 'support': 27619.0} |
-| No log        | 7.0   | 287  | 0.1950          | {'precision': 0.8525345622119815, 'recall': 0.8868648130393096, 'f1-score': 0.8693609022556391, 'support': 1043.0}  | {'precision': 0.9470030477480528, 'recall': 0.9670893371757925, 'f1-score': 0.9569408007300102, 'support': 17350.0} | {'precision': 0.9362522686025408, 'recall': 0.8946455668762194, 'f1-score': 0.9149761667220929, 'support': 9226.0} | 0.9399   | {'precision': 0.9119299595208584, 'recall': 0.9161999056971072, 'f1-score': 0.9137592899025807, 'support': 27619.0} | {'precision': 0.9398443048967325, 'recall': 0.9398602411383468, 'f1-score': 0.939615352760648, 'support': 27619.0}  |
-| No log        | 8.0   | 328  | 0.2260          | {'precision': 0.8517495395948435, 'recall': 0.8868648130393096, 'f1-score': 0.868952559887271, 'support': 1043.0}   | {'precision': 0.933457985041795, 'recall': 0.978328530259366, 'f1-score': 0.955366691056453, 'support': 17350.0}    | {'precision': 0.9556833153671098, 'recall': 0.8648384998916107, 'f1-score': 0.9079943100995733, 'support': 9226.0} | 0.9370   | {'precision': 0.9136302800012494, 'recall': 0.9100106143967621, 'f1-score': 0.9107711870144325, 'support': 27619.0} | {'precision': 0.9377966283301177, 'recall': 0.9369636844201455, 'f1-score': 0.9362788339465783, 'support': 27619.0} |
-| No log        | 9.0   | 369  | 0.2217          | {'precision': 0.8499079189686924, 'recall': 0.8849472674976031, 'f1-score': 0.8670737435415689, 'support': 1043.0}  | {'precision': 0.9531535648994516, 'recall': 0.9616138328530259, 'f1-score': 0.9573650083204224, 'support': 17350.0} | {'precision': 0.927455975191051, 'recall': 0.9076522870149577, 'f1-score': 0.9174472747192549, 'support': 9226.0}  | 0.9407   | {'precision': 0.910172486353065, 'recall': 0.9180711291218623, 'f1-score': 0.9139620088604153, 'support': 27619.0}  | {'precision': 0.9406704492415535, 'recall': 0.9406930011948297, 'f1-score': 0.9406209263707241, 'support': 27619.0} |
-| No log        | 10.0  | 410  | 0.2663          | {'precision': 0.8574091332712023, 'recall': 0.8820709491850431, 'f1-score': 0.8695652173913044, 'support': 1043.0}  | {'precision': 0.9361054205193511, 'recall': 0.9744668587896254, 'f1-score': 0.9549010194572307, 'support': 17350.0} | {'precision': 0.9483794932233353, 'recall': 0.8722089746368957, 'f1-score': 0.9087008074078257, 'support': 9226.0} | 0.9368   | {'precision': 0.9139646823379629, 'recall': 0.9095822608705214, 'f1-score': 0.9110556814187869, 'support': 27619.0} | {'precision': 0.937233642655096, 'recall': 0.9368188565842355, 'f1-score': 0.9362454418504176, 'support': 27619.0}  |
-| No log        | 11.0  | 451  | 0.2752          | {'precision': 0.8570110701107011, 'recall': 0.8906999041227229, 'f1-score': 0.8735307945463094, 'support': 1043.0}  | {'precision': 0.9348246340789838, 'recall': 0.9755043227665706, 'f1-score': 0.954731349598082, 'support': 17350.0}  | {'precision': 0.9505338078291815, 'recall': 0.8685237372642532, 'f1-score': 0.9076801087449027, 'support': 9226.0} | 0.9366   | {'precision': 0.9141231706729555, 'recall': 0.9115759880511822, 'f1-score': 0.911980750963098, 'support': 27619.0}  | {'precision': 0.9371336709666482, 'recall': 0.9365654078713929, 'f1-score': 0.9359476526130199, 'support': 27619.0} |
-| No log        | 12.0  | 492  | 0.2662          | {'precision': 0.8555657773689053, 'recall': 0.8916586768935763, 'f1-score': 0.8732394366197183, 'support': 1043.0}  | {'precision': 0.9461304151624549, 'recall': 0.9667435158501441, 'f1-score': 0.9563259022749302, 'support': 17350.0} | {'precision': 0.9358246251703771, 'recall': 0.8930197268588771, 'f1-score': 0.9139212423738213, 'support': 9226.0} | 0.9393   | {'precision': 0.9125069392339125, 'recall': 0.9171406398675325, 'f1-score': 0.9144955270894899, 'support': 27619.0} | {'precision': 0.9392677432450942, 'recall': 0.9392809297947066, 'f1-score': 0.9390231550383894, 'support': 27619.0} |
-| 0.1232        | 13.0  | 533  | 0.2681          | {'precision': 0.8646895273401297, 'recall': 0.8945349952061361, 'f1-score': 0.8793590951932139, 'support': 1043.0}  | {'precision': 0.9548364966841985, 'recall': 0.9626512968299712, 'f1-score': 0.9587279719878308, 'support': 17350.0} | {'precision': 0.9293766578249337, 'recall': 0.9114459137220897, 'f1-score': 0.9203239575352961, 'support': 9226.0} | 0.9430   | {'precision': 0.9163008939497539, 'recall': 0.9228774019193989, 'f1-score': 0.9194703415721136, 'support': 27619.0} | {'precision': 0.9429274571700438, 'recall': 0.9429740396104132, 'f1-score': 0.9429020124731535, 'support': 27619.0} |
-| 0.1232        | 14.0  | 574  | 0.2835          | {'precision': 0.8643592142188962, 'recall': 0.8859060402684564, 'f1-score': 0.875, 'support': 1043.0}               | {'precision': 0.9461283248045886, 'recall': 0.9697406340057637, 'f1-score': 0.9577889733299177, 'support': 17350.0} | {'precision': 0.9405726018022128, 'recall': 0.8937784522003035, 'f1-score': 0.9165786694825766, 'support': 9226.0} | 0.9412   | {'precision': 0.9170200469418992, 'recall': 0.9164750421581745, 'f1-score': 0.9164558809374981, 'support': 27619.0} | {'precision': 0.9411845439739722, 'recall': 0.9411998986205149, 'f1-score': 0.9408964297013043, 'support': 27619.0} |
-| 0.1232        | 15.0  | 615  | 0.2837          | {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}   | {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}  | {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0} | 0.9412   | {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0} | {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0} |
+| No log        | 1.0   | 41   | 0.3607          | {'precision': 0.8202247191011236, 'recall': 0.19329214474845544, 'f1-score': 0.3128571428571429, 'support': 1133.0} | {'precision': 0.8412101850981866, 'recall': 0.9767086674303169, 'f1-score': 0.9039097402761301, 'support': 18333.0} | {'precision': 0.9249453797712376, 'recall': 0.7293271179570329, 'f1-score': 0.8155702872684006, 'support': 9868.0} | 0.8632   | {'precision': 0.8621267613235158, 'recall': 0.6331093100452684, 'f1-score': 0.6774457234672245, 'support': 29334.0} | {'precision': 0.8685682804162133, 'recall': 0.8632303811277017, 'f1-score': 0.8513633328596172, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.2532          | {'precision': 0.7996755879967559, 'recall': 0.8702559576345984, 'f1-score': 0.8334742180896026, 'support': 1133.0}  | {'precision': 0.9331675137882557, 'recall': 0.9413625702285496, 'f1-score': 0.937247128465528, 'support': 18333.0}  | {'precision': 0.8902883314250026, 'recall': 0.8667409809485205, 'f1-score': 0.878356867779204, 'support': 9868.0}  | 0.9135   | {'precision': 0.874377144403338, 'recall': 0.892786502937223, 'f1-score': 0.8830260714447782, 'support': 29334.0}   | {'precision': 0.9135868864110707, 'recall': 0.9135133292425173, 'f1-score': 0.9134282220801536, 'support': 29334.0} |
+| No log        | 3.0   | 123  | 0.2280          | {'precision': 0.8041074249605056, 'recall': 0.8984995586937334, 'f1-score': 0.8486869528970404, 'support': 1133.0}  | {'precision': 0.9231209660628774, 'recall': 0.9673812251131839, 'f1-score': 0.9447329870821681, 'support': 18333.0} | {'precision': 0.9356368563685636, 'recall': 0.8396838265099311, 'f1-score': 0.8850672933133945, 'support': 9868.0} | 0.9218   | {'precision': 0.8876217491306488, 'recall': 0.9018548701056162, 'f1-score': 0.8928290777642011, 'support': 29334.0} | {'precision': 0.9227345360999514, 'recall': 0.9217631417467785, 'f1-score': 0.9209516676970857, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.2643          | {'precision': 0.8111111111111111, 'recall': 0.9020300088261254, 'f1-score': 0.854157960718763, 'support': 1133.0}   | {'precision': 0.9180539091893006, 'recall': 0.9716358479245077, 'f1-score': 0.9440852236591055, 'support': 18333.0} | {'precision': 0.9426825049013955, 'recall': 0.8283340089177138, 'f1-score': 0.8818167107179459, 'support': 9868.0} | 0.9207   | {'precision': 0.8906158417339357, 'recall': 0.9006666218894489, 'f1-score': 0.8933532983652714, 'support': 29334.0} | {'precision': 0.9222084326864153, 'recall': 0.9207404377173246, 'f1-score': 0.9196646443104053, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.2475          | {'precision': 0.8158526821457166, 'recall': 0.8993821712268314, 'f1-score': 0.8555835432409741, 'support': 1133.0}  | {'precision': 0.9278074866310161, 'recall': 0.965308460153821, 'f1-score': 0.9461865426257119, 'support': 18333.0}  | {'precision': 0.9316391077571856, 'recall': 0.8507296311309283, 'f1-score': 0.8893479527517347, 'support': 9868.0} | 0.9242   | {'precision': 0.8917664255113061, 'recall': 0.9051400875038601, 'f1-score': 0.8970393462061402, 'support': 29334.0} | {'precision': 0.924772293469197, 'recall': 0.9242176314174678, 'f1-score': 0.9235664975183513, 'support': 29334.0}  |
+| No log        | 6.0   | 246  | 0.2554          | {'precision': 0.8058176100628931, 'recall': 0.9046778464254193, 'f1-score': 0.8523908523908524, 'support': 1133.0}  | {'precision': 0.9404408990459764, 'recall': 0.951726395025364, 'f1-score': 0.946049991866833, 'support': 18333.0}   | {'precision': 0.9114523083394679, 'recall': 0.8782934738548844, 'f1-score': 0.894565722248026, 'support': 9868.0}  | 0.9252   | {'precision': 0.8859036058161124, 'recall': 0.9115659051018893, 'f1-score': 0.8976688555019038, 'support': 29334.0} | {'precision': 0.9254893888697421, 'recall': 0.9252062453126065, 'f1-score': 0.9251131071042821, 'support': 29334.0} |
+| No log        | 7.0   | 287  | 0.2822          | {'precision': 0.8323746918652424, 'recall': 0.8940864960282436, 'f1-score': 0.8621276595744681, 'support': 1133.0}  | {'precision': 0.9353455123113582, 'recall': 0.9635084274259532, 'f1-score': 0.9492181202643882, 'support': 18333.0} | {'precision': 0.9287261698440208, 'recall': 0.8688690717470612, 'f1-score': 0.8978010471204189, 'support': 9868.0} | 0.9290   | {'precision': 0.8988154580068738, 'recall': 0.9088213317337527, 'f1-score': 0.9030489423197584, 'support': 29334.0} | {'precision': 0.9291415983878176, 'recall': 0.9289902502215859, 'f1-score': 0.9285575499450874, 'support': 29334.0} |
+| No log        | 8.0   | 328  | 0.3068          | {'precision': 0.8181089743589743, 'recall': 0.9011473962930273, 'f1-score': 0.8576228475430492, 'support': 1133.0}  | {'precision': 0.933372111469515, 'recall': 0.9627993236240658, 'f1-score': 0.9478573729996776, 'support': 18333.0}  | {'precision': 0.9279564032697548, 'recall': 0.8627888123226591, 'f1-score': 0.8941868403087749, 'support': 9868.0} | 0.9268   | {'precision': 0.8931458296994147, 'recall': 0.9089118440799174, 'f1-score': 0.899889020283834, 'support': 29334.0}  | {'precision': 0.9270983219126366, 'recall': 0.9267743914911025, 'f1-score': 0.926317298889901, 'support': 29334.0}  |
+| No log        | 9.0   | 369  | 0.3574          | {'precision': 0.8315441783649876, 'recall': 0.8887908208296558, 'f1-score': 0.8592150170648465, 'support': 1133.0}  | {'precision': 0.9180683108038387, 'recall': 0.9705994654448262, 'f1-score': 0.9436033408458174, 'support': 18333.0} | {'precision': 0.9387941883079739, 'recall': 0.8315768139440616, 'f1-score': 0.8819388467945618, 'support': 9868.0} | 0.9207   | {'precision': 0.8961355591589334, 'recall': 0.8969890334061811, 'f1-score': 0.8949190682350753, 'support': 29334.0} | {'precision': 0.9216986072911089, 'recall': 0.9206722574486943, 'f1-score': 0.9195998909875767, 'support': 29334.0} |
+| No log        | 10.0  | 410  | 0.3228          | {'precision': 0.8491048593350383, 'recall': 0.8790820829655781, 'f1-score': 0.8638334778837814, 'support': 1133.0}  | {'precision': 0.9479900314226893, 'recall': 0.9544537173403153, 'f1-score': 0.9512108939686336, 'support': 18333.0} | {'precision': 0.9123982273523652, 'recall': 0.897142278070531, 'f1-score': 0.9047059424658934, 'support': 9868.0}  | 0.9323   | {'precision': 0.9031643727033641, 'recall': 0.9102260261254749, 'f1-score': 0.9065834381061029, 'support': 29334.0} | {'precision': 0.9321975441198576, 'recall': 0.9322629031158383, 'f1-score': 0.9321916850692957, 'support': 29334.0} |
+| No log        | 11.0  | 451  | 0.3397          | {'precision': 0.8524871355060034, 'recall': 0.8773168578993822, 'f1-score': 0.8647237929534581, 'support': 1133.0}  | {'precision': 0.941372096765542, 'recall': 0.9572901325478645, 'f1-score': 0.9492643877109477, 'support': 18333.0}  | {'precision': 0.9164304461942258, 'recall': 0.8845764085934333, 'f1-score': 0.9002217294900223, 'support': 9868.0} | 0.9297   | {'precision': 0.9034298928219237, 'recall': 0.9063944663468934, 'f1-score': 0.9047366367181428, 'support': 29334.0} | {'precision': 0.9295485858585807, 'recall': 0.9297402331765187, 'f1-score': 0.9295010603371041, 'support': 29334.0} |
+| No log        | 12.0  | 492  | 0.3769          | {'precision': 0.8406040268456376, 'recall': 0.884377758164166, 'f1-score': 0.8619354838709679, 'support': 1133.0}   | {'precision': 0.9364758459246648, 'recall': 0.9601265477554137, 'f1-score': 0.9481537342777884, 'support': 18333.0} | {'precision': 0.9215707254440403, 'recall': 0.8728212403729225, 'f1-score': 0.8965337774539398, 'support': 9868.0} | 0.9278   | {'precision': 0.8995501994047809, 'recall': 0.9057751820975007, 'f1-score': 0.9022076652008987, 'support': 29334.0} | {'precision': 0.9277587769971628, 'recall': 0.9278311856548714, 'f1-score': 0.927458601951864, 'support': 29334.0}  |
+| 0.1199        | 13.0  | 533  | 0.4395          | {'precision': 0.8141945773524721, 'recall': 0.9011473962930273, 'f1-score': 0.8554671135316297, 'support': 1133.0}  | {'precision': 0.9200891931134619, 'recall': 0.967817596683576, 'f1-score': 0.9433500810803624, 'support': 18333.0}  | {'precision': 0.9357662573897226, 'recall': 0.8341102553708958, 'f1-score': 0.8820188598371196, 'support': 9868.0} | 0.9203   | {'precision': 0.8900166759518856, 'recall': 0.9010250827824997, 'f1-score': 0.8936120181497039, 'support': 29334.0} | {'precision': 0.9212728936187097, 'recall': 0.9202631758369128, 'f1-score': 0.9193237671285989, 'support': 29334.0} |
+| 0.1199        | 14.0  | 574  | 0.4362          | {'precision': 0.8338842975206612, 'recall': 0.8905560458958517, 'f1-score': 0.861288945795988, 'support': 1133.0}   | {'precision': 0.9288525106249016, 'recall': 0.9656357388316151, 'f1-score': 0.9468870346598203, 'support': 18333.0} | {'precision': 0.9307225592939878, 'recall': 0.8549858127280098, 'f1-score': 0.8912480853536153, 'support': 9868.0} | 0.9255   | {'precision': 0.8978197891465168, 'recall': 0.9037258658184922, 'f1-score': 0.8998080219364746, 'support': 29334.0} | {'precision': 0.9258135338341278, 'recall': 0.9255130565214427, 'f1-score': 0.9248638606488995, 'support': 29334.0} |
+| 0.1199        | 15.0  | 615  | 0.4385          | {'precision': 0.8272208638956805, 'recall': 0.8958517210944396, 'f1-score': 0.8601694915254238, 'support': 1133.0}  | {'precision': 0.9282784730255548, 'recall': 0.9629629629629629, 'f1-score': 0.9453026692725763, 'support': 18333.0} | {'precision': 0.9263945428539994, 'recall': 0.8532630725577625, 'f1-score': 0.8883262119533682, 'support': 9868.0} | 0.9235   | {'precision': 0.8939646265917448, 'recall': 0.9040259188717217, 'f1-score': 0.8979327909171229, 'support': 29334.0} | {'precision': 0.923741454750616, 'recall': 0.9234676484625349, 'f1-score': 0.9228475124165911, 'support': 29334.0}  |
+| 0.1199        | 16.0  | 656  | 0.4247          | {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}  | {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}  | {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0} | 0.9267   | {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0} | {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0}  |
 
 
 ### Framework versions

meta_data/README_s42_e16.md

@@ -0,0 +1,95 @@
+---
+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: 0.9266721210881571
+---
+
+<!-- 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: 0.4247
+- B: {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}
+- I: {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}
+- O: {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0}
+- Accuracy: 0.9267
+- Macro avg: {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0}
+- Weighted avg: {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, '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: 16
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.3607          | {'precision': 0.8202247191011236, 'recall': 0.19329214474845544, 'f1-score': 0.3128571428571429, 'support': 1133.0} | {'precision': 0.8412101850981866, 'recall': 0.9767086674303169, 'f1-score': 0.9039097402761301, 'support': 18333.0} | {'precision': 0.9249453797712376, 'recall': 0.7293271179570329, 'f1-score': 0.8155702872684006, 'support': 9868.0} | 0.8632   | {'precision': 0.8621267613235158, 'recall': 0.6331093100452684, 'f1-score': 0.6774457234672245, 'support': 29334.0} | {'precision': 0.8685682804162133, 'recall': 0.8632303811277017, 'f1-score': 0.8513633328596172, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.2532          | {'precision': 0.7996755879967559, 'recall': 0.8702559576345984, 'f1-score': 0.8334742180896026, 'support': 1133.0}  | {'precision': 0.9331675137882557, 'recall': 0.9413625702285496, 'f1-score': 0.937247128465528, 'support': 18333.0}  | {'precision': 0.8902883314250026, 'recall': 0.8667409809485205, 'f1-score': 0.878356867779204, 'support': 9868.0}  | 0.9135   | {'precision': 0.874377144403338, 'recall': 0.892786502937223, 'f1-score': 0.8830260714447782, 'support': 29334.0}   | {'precision': 0.9135868864110707, 'recall': 0.9135133292425173, 'f1-score': 0.9134282220801536, 'support': 29334.0} |
+| No log        | 3.0   | 123  | 0.2280          | {'precision': 0.8041074249605056, 'recall': 0.8984995586937334, 'f1-score': 0.8486869528970404, 'support': 1133.0}  | {'precision': 0.9231209660628774, 'recall': 0.9673812251131839, 'f1-score': 0.9447329870821681, 'support': 18333.0} | {'precision': 0.9356368563685636, 'recall': 0.8396838265099311, 'f1-score': 0.8850672933133945, 'support': 9868.0} | 0.9218   | {'precision': 0.8876217491306488, 'recall': 0.9018548701056162, 'f1-score': 0.8928290777642011, 'support': 29334.0} | {'precision': 0.9227345360999514, 'recall': 0.9217631417467785, 'f1-score': 0.9209516676970857, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.2643          | {'precision': 0.8111111111111111, 'recall': 0.9020300088261254, 'f1-score': 0.854157960718763, 'support': 1133.0}   | {'precision': 0.9180539091893006, 'recall': 0.9716358479245077, 'f1-score': 0.9440852236591055, 'support': 18333.0} | {'precision': 0.9426825049013955, 'recall': 0.8283340089177138, 'f1-score': 0.8818167107179459, 'support': 9868.0} | 0.9207   | {'precision': 0.8906158417339357, 'recall': 0.9006666218894489, 'f1-score': 0.8933532983652714, 'support': 29334.0} | {'precision': 0.9222084326864153, 'recall': 0.9207404377173246, 'f1-score': 0.9196646443104053, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.2475          | {'precision': 0.8158526821457166, 'recall': 0.8993821712268314, 'f1-score': 0.8555835432409741, 'support': 1133.0}  | {'precision': 0.9278074866310161, 'recall': 0.965308460153821, 'f1-score': 0.9461865426257119, 'support': 18333.0}  | {'precision': 0.9316391077571856, 'recall': 0.8507296311309283, 'f1-score': 0.8893479527517347, 'support': 9868.0} | 0.9242   | {'precision': 0.8917664255113061, 'recall': 0.9051400875038601, 'f1-score': 0.8970393462061402, 'support': 29334.0} | {'precision': 0.924772293469197, 'recall': 0.9242176314174678, 'f1-score': 0.9235664975183513, 'support': 29334.0}  |
+| No log        | 6.0   | 246  | 0.2554          | {'precision': 0.8058176100628931, 'recall': 0.9046778464254193, 'f1-score': 0.8523908523908524, 'support': 1133.0}  | {'precision': 0.9404408990459764, 'recall': 0.951726395025364, 'f1-score': 0.946049991866833, 'support': 18333.0}   | {'precision': 0.9114523083394679, 'recall': 0.8782934738548844, 'f1-score': 0.894565722248026, 'support': 9868.0}  | 0.9252   | {'precision': 0.8859036058161124, 'recall': 0.9115659051018893, 'f1-score': 0.8976688555019038, 'support': 29334.0} | {'precision': 0.9254893888697421, 'recall': 0.9252062453126065, 'f1-score': 0.9251131071042821, 'support': 29334.0} |
+| No log        | 7.0   | 287  | 0.2822          | {'precision': 0.8323746918652424, 'recall': 0.8940864960282436, 'f1-score': 0.8621276595744681, 'support': 1133.0}  | {'precision': 0.9353455123113582, 'recall': 0.9635084274259532, 'f1-score': 0.9492181202643882, 'support': 18333.0} | {'precision': 0.9287261698440208, 'recall': 0.8688690717470612, 'f1-score': 0.8978010471204189, 'support': 9868.0} | 0.9290   | {'precision': 0.8988154580068738, 'recall': 0.9088213317337527, 'f1-score': 0.9030489423197584, 'support': 29334.0} | {'precision': 0.9291415983878176, 'recall': 0.9289902502215859, 'f1-score': 0.9285575499450874, 'support': 29334.0} |
+| No log        | 8.0   | 328  | 0.3068          | {'precision': 0.8181089743589743, 'recall': 0.9011473962930273, 'f1-score': 0.8576228475430492, 'support': 1133.0}  | {'precision': 0.933372111469515, 'recall': 0.9627993236240658, 'f1-score': 0.9478573729996776, 'support': 18333.0}  | {'precision': 0.9279564032697548, 'recall': 0.8627888123226591, 'f1-score': 0.8941868403087749, 'support': 9868.0} | 0.9268   | {'precision': 0.8931458296994147, 'recall': 0.9089118440799174, 'f1-score': 0.899889020283834, 'support': 29334.0}  | {'precision': 0.9270983219126366, 'recall': 0.9267743914911025, 'f1-score': 0.926317298889901, 'support': 29334.0}  |
+| No log        | 9.0   | 369  | 0.3574          | {'precision': 0.8315441783649876, 'recall': 0.8887908208296558, 'f1-score': 0.8592150170648465, 'support': 1133.0}  | {'precision': 0.9180683108038387, 'recall': 0.9705994654448262, 'f1-score': 0.9436033408458174, 'support': 18333.0} | {'precision': 0.9387941883079739, 'recall': 0.8315768139440616, 'f1-score': 0.8819388467945618, 'support': 9868.0} | 0.9207   | {'precision': 0.8961355591589334, 'recall': 0.8969890334061811, 'f1-score': 0.8949190682350753, 'support': 29334.0} | {'precision': 0.9216986072911089, 'recall': 0.9206722574486943, 'f1-score': 0.9195998909875767, 'support': 29334.0} |
+| No log        | 10.0  | 410  | 0.3228          | {'precision': 0.8491048593350383, 'recall': 0.8790820829655781, 'f1-score': 0.8638334778837814, 'support': 1133.0}  | {'precision': 0.9479900314226893, 'recall': 0.9544537173403153, 'f1-score': 0.9512108939686336, 'support': 18333.0} | {'precision': 0.9123982273523652, 'recall': 0.897142278070531, 'f1-score': 0.9047059424658934, 'support': 9868.0}  | 0.9323   | {'precision': 0.9031643727033641, 'recall': 0.9102260261254749, 'f1-score': 0.9065834381061029, 'support': 29334.0} | {'precision': 0.9321975441198576, 'recall': 0.9322629031158383, 'f1-score': 0.9321916850692957, 'support': 29334.0} |
+| No log        | 11.0  | 451  | 0.3397          | {'precision': 0.8524871355060034, 'recall': 0.8773168578993822, 'f1-score': 0.8647237929534581, 'support': 1133.0}  | {'precision': 0.941372096765542, 'recall': 0.9572901325478645, 'f1-score': 0.9492643877109477, 'support': 18333.0}  | {'precision': 0.9164304461942258, 'recall': 0.8845764085934333, 'f1-score': 0.9002217294900223, 'support': 9868.0} | 0.9297   | {'precision': 0.9034298928219237, 'recall': 0.9063944663468934, 'f1-score': 0.9047366367181428, 'support': 29334.0} | {'precision': 0.9295485858585807, 'recall': 0.9297402331765187, 'f1-score': 0.9295010603371041, 'support': 29334.0} |
+| No log        | 12.0  | 492  | 0.3769          | {'precision': 0.8406040268456376, 'recall': 0.884377758164166, 'f1-score': 0.8619354838709679, 'support': 1133.0}   | {'precision': 0.9364758459246648, 'recall': 0.9601265477554137, 'f1-score': 0.9481537342777884, 'support': 18333.0} | {'precision': 0.9215707254440403, 'recall': 0.8728212403729225, 'f1-score': 0.8965337774539398, 'support': 9868.0} | 0.9278   | {'precision': 0.8995501994047809, 'recall': 0.9057751820975007, 'f1-score': 0.9022076652008987, 'support': 29334.0} | {'precision': 0.9277587769971628, 'recall': 0.9278311856548714, 'f1-score': 0.927458601951864, 'support': 29334.0}  |
+| 0.1199        | 13.0  | 533  | 0.4395          | {'precision': 0.8141945773524721, 'recall': 0.9011473962930273, 'f1-score': 0.8554671135316297, 'support': 1133.0}  | {'precision': 0.9200891931134619, 'recall': 0.967817596683576, 'f1-score': 0.9433500810803624, 'support': 18333.0}  | {'precision': 0.9357662573897226, 'recall': 0.8341102553708958, 'f1-score': 0.8820188598371196, 'support': 9868.0} | 0.9203   | {'precision': 0.8900166759518856, 'recall': 0.9010250827824997, 'f1-score': 0.8936120181497039, 'support': 29334.0} | {'precision': 0.9212728936187097, 'recall': 0.9202631758369128, 'f1-score': 0.9193237671285989, 'support': 29334.0} |
+| 0.1199        | 14.0  | 574  | 0.4362          | {'precision': 0.8338842975206612, 'recall': 0.8905560458958517, 'f1-score': 0.861288945795988, 'support': 1133.0}   | {'precision': 0.9288525106249016, 'recall': 0.9656357388316151, 'f1-score': 0.9468870346598203, 'support': 18333.0} | {'precision': 0.9307225592939878, 'recall': 0.8549858127280098, 'f1-score': 0.8912480853536153, 'support': 9868.0} | 0.9255   | {'precision': 0.8978197891465168, 'recall': 0.9037258658184922, 'f1-score': 0.8998080219364746, 'support': 29334.0} | {'precision': 0.9258135338341278, 'recall': 0.9255130565214427, 'f1-score': 0.9248638606488995, 'support': 29334.0} |
+| 0.1199        | 15.0  | 615  | 0.4385          | {'precision': 0.8272208638956805, 'recall': 0.8958517210944396, 'f1-score': 0.8601694915254238, 'support': 1133.0}  | {'precision': 0.9282784730255548, 'recall': 0.9629629629629629, 'f1-score': 0.9453026692725763, 'support': 18333.0} | {'precision': 0.9263945428539994, 'recall': 0.8532630725577625, 'f1-score': 0.8883262119533682, 'support': 9868.0} | 0.9235   | {'precision': 0.8939646265917448, 'recall': 0.9040259188717217, 'f1-score': 0.8979327909171229, 'support': 29334.0} | {'precision': 0.923741454750616, 'recall': 0.9234676484625349, 'f1-score': 0.9228475124165911, 'support': 29334.0}  |
+| 0.1199        | 16.0  | 656  | 0.4247          | {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}  | {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}  | {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0} | 0.9267   | {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0} | {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0}  |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/meta_s42_e16_cvi0.json

@@ -1 +1 @@
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+{"B": {"precision": 0.8405063291139241, "recall": 0.8790820829655781, "f1-score": 0.8593615185504745, "support": 1133.0}, "I": {"precision": 0.9346315063405316, "recall": 0.960835651557301, "f1-score": 0.9475524475524475, "support": 18333.0}, "O": {"precision": 0.9215222532788647, "recall": 0.8686663964329144, "f1-score": 0.8943140323422013, "support": 9868.0}, "accuracy": 0.9266721210881571, "macro avg": {"precision": 0.8988866962444403, "recall": 0.9028613769852646, "f1-score": 0.9004093328150411, "support": 29334.0}, "weighted avg": {"precision": 0.9265860323168638, "recall": 0.9266721210881571, "f1-score": 0.926236670506905, "support": 29334.0}}

model.safetensors

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