trainer: training complete at 2024-02-17 19:28:20.117099.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9427066537676019
+      value: 0.9393385646207316
 ---
 
 <!-- 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 fancy_dataset dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2002
-- B: {'precision': 0.8633879781420765, 'recall': 0.8951841359773371, 'f1-score': 0.878998609179416, 'support': 1059.0}
-- I: {'precision': 0.9522312657872579, 'recall': 0.9652347083926032, 'f1-score': 0.9586888951681265, 'support': 17575.0}
-- O: {'precision': 0.9335260115606936, 'recall': 0.9054447439353099, 'f1-score': 0.91927097586339, 'support': 9275.0}
-- Accuracy: 0.9427
-- Macro avg: {'precision': 0.9163817518300093, 'recall': 0.9219545294350834, 'f1-score': 0.9189861600703108, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9426438110390537, 'recall': 0.9427066537676019, 'f1-score': 0.9425653072784325, 'support': 27909.0}
+- Loss: 0.1675
+- B: {'precision': 0.8321678321678322, 'recall': 0.898961284230406, 'f1-score': 0.864275987290059, 'support': 1059.0}
+- I: {'precision': 0.9499635384529085, 'recall': 0.9635846372688478, 'f1-score': 0.956725608722671, 'support': 17575.0}
+- O: {'precision': 0.9318639516670396, 'recall': 0.8980053908355795, 'f1-score': 0.9146214242573986, 'support': 9275.0}
+- Accuracy: 0.9393
+- Macro avg: {'precision': 0.9046651074292601, 'recall': 0.9201837707782777, 'f1-score': 0.9118743400900429, 'support': 27909.0}
+- Weighted avg: {'precision': 0.939478772950926, 'recall': 0.9393385646207316, 'f1-score': 0.9392251443558882, 'support': 27909.0}
 
 ## Model description
 
@@ -63,22 +63,17 @@ 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: 10
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
 |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2694          | {'precision': 0.7387836490528414, 'recall': 0.6997167138810199, 'f1-score': 0.7187196896217265, 'support': 1059.0} | {'precision': 0.9249066904350732, 'recall': 0.9446941678520626, 'f1-score': 0.9346957158137702, 'support': 17575.0} | {'precision': 0.8946957007258515, 'recall': 0.8638274932614555, 'f1-score': 0.8789906747120131, 'support': 9275.0} | 0.9085   | {'precision': 0.852795346737922, 'recall': 0.8360794583315126, 'f1-score': 0.84413536004917, 'support': 27909.0}    | {'precision': 0.9078042779381434, 'recall': 0.9085241320004299, 'f1-score': 0.9079880993475345, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.1833          | {'precision': 0.8053402239448751, 'recall': 0.882908404154863, 'f1-score': 0.8423423423423424, 'support': 1059.0}  | {'precision': 0.9424276169265033, 'recall': 0.9630725462304409, 'f1-score': 0.9526382439848037, 'support': 17575.0} | {'precision': 0.9319526627218935, 'recall': 0.8830188679245283, 'f1-score': 0.9068261086198306, 'support': 9275.0} | 0.9334   | {'precision': 0.8932401678644238, 'recall': 0.9096666061032774, 'f1-score': 0.9006022316489922, 'support': 27909.0} | {'precision': 0.9337447279152418, 'recall': 0.9334264932459063, 'f1-score': 0.9332283434025723, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.1823          | {'precision': 0.8270742358078602, 'recall': 0.8942398489140698, 'f1-score': 0.8593466424682397, 'support': 1059.0} | {'precision': 0.9510770975056689, 'recall': 0.9545945945945946, 'f1-score': 0.952832599744427, 'support': 17575.0}  | {'precision': 0.9167032003507234, 'recall': 0.9017789757412399, 'f1-score': 0.9091798467308007, 'support': 9275.0} | 0.9348   | {'precision': 0.8982848445547509, 'recall': 0.9168711397499681, 'f1-score': 0.9071196963144891, 'support': 27909.0} | {'precision': 0.9349483603008211, 'recall': 0.9347522304632914, 'f1-score': 0.9347781759758625, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1972          | {'precision': 0.8227739726027398, 'recall': 0.9074598677998111, 'f1-score': 0.8630444544229906, 'support': 1059.0} | {'precision': 0.9315806662312214, 'recall': 0.9738264580369843, 'f1-score': 0.9522352352073885, 'support': 17575.0} | {'precision': 0.9496953040984586, 'recall': 0.8569272237196766, 'f1-score': 0.900929494445704, 'support': 9275.0}  | 0.9325   | {'precision': 0.9013499809774732, 'recall': 0.9127378498521574, 'f1-score': 0.9054030613586944, 'support': 27909.0} | {'precision': 0.9334720624713614, 'recall': 0.9324590633845713, 'f1-score': 0.9318004728577772, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.1764          | {'precision': 0.8512544802867383, 'recall': 0.8970727101038716, 'f1-score': 0.8735632183908046, 'support': 1059.0} | {'precision': 0.9488699932870889, 'recall': 0.9651209103840683, 'f1-score': 0.9569264618769571, 'support': 17575.0} | {'precision': 0.9329370864640574, 'recall': 0.8969272237196766, 'f1-score': 0.9145778364116095, 'support': 9275.0} | 0.9399   | {'precision': 0.9110205200126282, 'recall': 0.9197069480692055, 'f1-score': 0.9150225055597904, 'support': 27909.0} | {'precision': 0.939871013063828, 'recall': 0.9398760256548067, 'f1-score': 0.939689542745389, 'support': 27909.0}   |
-| No log        | 6.0   | 246  | 0.1789          | {'precision': 0.846830985915493, 'recall': 0.9084041548630784, 'f1-score': 0.8765375854214124, 'support': 1059.0}  | {'precision': 0.9442471984910684, 'recall': 0.9684779516358464, 'f1-score': 0.9562090952501334, 'support': 17575.0} | {'precision': 0.939179147136161, 'recall': 0.8857142857142857, 'f1-score': 0.9116635223615581, 'support': 9275.0}  | 0.9387   | {'precision': 0.9100857771809073, 'recall': 0.9208654640710702, 'f1-score': 0.9148034010110346, 'support': 27909.0} | {'precision': 0.9388664988803944, 'recall': 0.9386936113798416, 'f1-score': 0.9383821463286331, 'support': 27909.0} |
-| No log        | 7.0   | 287  | 0.1773          | {'precision': 0.8664850136239782, 'recall': 0.9008498583569405, 'f1-score': 0.8833333333333333, 'support': 1059.0} | {'precision': 0.9508068130883012, 'recall': 0.9655761024182077, 'f1-score': 0.95813454535189, 'support': 17575.0}   | {'precision': 0.9341517857142857, 'recall': 0.9024258760107817, 'f1-score': 0.9180148066904306, 'support': 9275.0} | 0.9421   | {'precision': 0.9171478708088551, 'recall': 0.9229506122619767, 'f1-score': 0.9198275617918847, 'support': 27909.0} | {'precision': 0.9420722771132855, 'recall': 0.9421333619979219, 'f1-score': 0.941963236468996, 'support': 27909.0}  |
-| No log        | 8.0   | 328  | 0.2030          | {'precision': 0.8580413297394429, 'recall': 0.9017941454202077, 'f1-score': 0.8793738489871087, 'support': 1059.0} | {'precision': 0.9481547552681459, 'recall': 0.9677382645803698, 'f1-score': 0.957846422436854, 'support': 17575.0}  | {'precision': 0.9384736960939264, 'recall': 0.8962803234501348, 'f1-score': 0.9168918546296807, 'support': 9275.0} | 0.9415   | {'precision': 0.9148899270338383, 'recall': 0.921937577816904, 'f1-score': 0.9180373753512144, 'support': 27909.0}  | {'precision': 0.941518116854882, 'recall': 0.9414884087570318, 'f1-score': 0.9412583658352268, 'support': 27909.0}  |
-| No log        | 9.0   | 369  | 0.2027          | {'precision': 0.8658759124087592, 'recall': 0.8961284230406044, 'f1-score': 0.8807424593967518, 'support': 1059.0} | {'precision': 0.956234096692112, 'recall': 0.9622190611664296, 'f1-score': 0.959217243335224, 'support': 17575.0}   | {'precision': 0.928680981595092, 'recall': 0.9139622641509434, 'f1-score': 0.9212628375808294, 'support': 9275.0}  | 0.9437   | {'precision': 0.9169303302319877, 'recall': 0.9241032494526591, 'f1-score': 0.9204075134376017, 'support': 27909.0} | {'precision': 0.9436487493245629, 'recall': 0.9436740836289369, 'f1-score': 0.9436261469303778, 'support': 27909.0} |
-| No log        | 10.0  | 410  | 0.2002          | {'precision': 0.8633879781420765, 'recall': 0.8951841359773371, 'f1-score': 0.878998609179416, 'support': 1059.0}  | {'precision': 0.9522312657872579, 'recall': 0.9652347083926032, 'f1-score': 0.9586888951681265, 'support': 17575.0} | {'precision': 0.9335260115606936, 'recall': 0.9054447439353099, 'f1-score': 0.91927097586339, 'support': 9275.0}   | 0.9427   | {'precision': 0.9163817518300093, 'recall': 0.9219545294350834, 'f1-score': 0.9189861600703108, 'support': 27909.0} | {'precision': 0.9426438110390537, 'recall': 0.9427066537676019, 'f1-score': 0.9425653072784325, 'support': 27909.0} |
+| No log        | 1.0   | 41   | 0.2773          | {'precision': 0.7656804733727811, 'recall': 0.6109537299339, 'f1-score': 0.6796218487394958, 'support': 1059.0}    | {'precision': 0.9200088755755256, 'recall': 0.943669985775249, 'f1-score': 0.931689230942082, 'support': 17575.0}   | {'precision': 0.8860241230496846, 'recall': 0.8632884097035041, 'f1-score': 0.8745085190039319, 'support': 9275.0} | 0.9043   | {'precision': 0.8572378239993305, 'recall': 0.8059707084708844, 'f1-score': 0.82860653289517, 'support': 27909.0}   | {'precision': 0.9028587678106511, 'recall': 0.9043319359346448, 'f1-score': 0.9031217272343576, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.1955          | {'precision': 0.7943201376936316, 'recall': 0.8715769593956563, 'f1-score': 0.8311571364250337, 'support': 1059.0} | {'precision': 0.9362793776895068, 'recall': 0.9656330014224751, 'f1-score': 0.9507296714377748, 'support': 17575.0} | {'precision': 0.9372462591346712, 'recall': 0.8711590296495957, 'f1-score': 0.9029950827000447, 'support': 9275.0} | 0.9307   | {'precision': 0.8892819248392699, 'recall': 0.9027896634892424, 'f1-score': 0.8949606301876177, 'support': 27909.0} | {'precision': 0.9312140937398226, 'recall': 0.9306675266043212, 'f1-score': 0.9303288822614897, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.1872          | {'precision': 0.7751385589865399, 'recall': 0.9244570349386213, 'f1-score': 0.8432385874246339, 'support': 1059.0} | {'precision': 0.9386327328816174, 'recall': 0.96950213371266, 'f1-score': 0.9538177339901479, 'support': 17575.0}   | {'precision': 0.9483103732485576, 'recall': 0.868355795148248, 'f1-score': 0.9065736154885187, 'support': 9275.0}  | 0.9342   | {'precision': 0.8873605550389051, 'recall': 0.9207716545998431, 'f1-score': 0.9012099789677669, 'support': 27909.0} | {'precision': 0.9356451584163368, 'recall': 0.9341789386936113, 'f1-score': 0.933921194690442, 'support': 27909.0}  |
+| No log        | 4.0   | 164  | 0.1684          | {'precision': 0.8173322005097706, 'recall': 0.9084041548630784, 'f1-score': 0.8604651162790699, 'support': 1059.0} | {'precision': 0.9426896055761464, 'recall': 0.9696159317211949, 'f1-score': 0.9559631998204869, 'support': 17575.0} | {'precision': 0.9440785673021375, 'recall': 0.8809703504043127, 'f1-score': 0.9114333519241495, 'support': 9275.0} | 0.9378   | {'precision': 0.9013667911293516, 'recall': 0.9196634789961954, 'f1-score': 0.9092872226745689, 'support': 27909.0} | {'precision': 0.938394544056324, 'recall': 0.9378336737253216, 'f1-score': 0.9375409414196524, 'support': 27909.0}  |
+| No log        | 5.0   | 205  | 0.1675          | {'precision': 0.8321678321678322, 'recall': 0.898961284230406, 'f1-score': 0.864275987290059, 'support': 1059.0}   | {'precision': 0.9499635384529085, 'recall': 0.9635846372688478, 'f1-score': 0.956725608722671, 'support': 17575.0}  | {'precision': 0.9318639516670396, 'recall': 0.8980053908355795, 'f1-score': 0.9146214242573986, 'support': 9275.0} | 0.9393   | {'precision': 0.9046651074292601, 'recall': 0.9201837707782777, 'f1-score': 0.9118743400900429, 'support': 27909.0} | {'precision': 0.939478772950926, 'recall': 0.9393385646207316, 'f1-score': 0.9392251443558882, 'support': 27909.0}  |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:14a45a91f3842d11e7bdc72e0799d71134509fe705dfff277547ad23e8d0e0ec
 size 592318676