trainer: training complete at 2024-02-19 18:42:39.317830.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9393385646207316
+      value: 0.9400193485972267
 ---
 
 <!-- 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: 0.1675
-- 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}
+- Loss: 0.1716
+- B: {'precision': 0.8278829604130808, 'recall': 0.9084041548630784, 'f1-score': 0.8662764520486267, 'support': 1059.0}
+- I: {'precision': 0.949054915557544, 'recall': 0.9656330014224751, 'f1-score': 0.9572721888484643, 'support': 17575.0}
+- O: {'precision': 0.9364918217710095, 'recall': 0.8950943396226415, 'f1-score': 0.9153252480705623, 'support': 9275.0}
+- Accuracy: 0.9400
+- Macro avg: {'precision': 0.9044765659138781, 'recall': 0.9230438319693982, 'f1-score': 0.9129579629892177, 'support': 27909.0}
+- Weighted avg: {'precision': 0.9402819822611845, 'recall': 0.9400193485972267, 'f1-score': 0.9398791485752167, 'support': 27909.0}
 
 ## Model description
 
@@ -69,11 +69,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
 |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| 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}  |
+| No log        | 1.0   | 41   | 0.2820          | {'precision': 0.8252595155709342, 'recall': 0.45042492917847027, 'f1-score': 0.582773365913256, 'support': 1059.0} | {'precision': 0.9113681210260908, 'recall': 0.9460597439544808, 'f1-score': 0.9283899606354169, 'support': 17575.0} | {'precision': 0.879608231539562, 'recall': 0.8617789757412398, 'f1-score': 0.8706023309007732, 'support': 9275.0}  | 0.8992   | {'precision': 0.8720786227121957, 'recall': 0.7527545496247302, 'f1-score': 0.793921885816482, 'support': 27909.0}  | {'precision': 0.8975459852217064, 'recall': 0.8992439714787345, 'f1-score': 0.896071058503503, 'support': 27909.0}  |
+| No log        | 2.0   | 82   | 0.1953          | {'precision': 0.812897366030881, 'recall': 0.8451369216241738, 'f1-score': 0.8287037037037038, 'support': 1059.0}  | {'precision': 0.9452124358178637, 'recall': 0.9531721194879089, 'f1-score': 0.9491755906850247, 'support': 17575.0} | {'precision': 0.9121629058888278, 'recall': 0.8934770889487871, 'f1-score': 0.902723311546841, 'support': 9275.0}  | 0.9292   | {'precision': 0.8900909025791909, 'recall': 0.8972620433536234, 'f1-score': 0.8935342019785232, 'support': 27909.0} | {'precision': 0.9292084210199053, 'recall': 0.9292342971801211, 'f1-score': 0.9291668258665118, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.1858          | {'precision': 0.7883211678832117, 'recall': 0.9178470254957507, 'f1-score': 0.8481675392670156, 'support': 1059.0} | {'precision': 0.9373831775700935, 'recall': 0.9701849217638692, 'f1-score': 0.9535020271214875, 'support': 17575.0} | {'precision': 0.9481498939429649, 'recall': 0.8674932614555256, 'f1-score': 0.9060300658746692, 'support': 9275.0} | 0.9341   | {'precision': 0.8912847464654234, 'recall': 0.9185084029050485, 'f1-score': 0.9025665440877241, 'support': 27909.0} | {'precision': 0.9353051606615684, 'recall': 0.9340714464867964, 'f1-score': 0.9337287760841115, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.1704          | {'precision': 0.8296943231441049, 'recall': 0.8970727101038716, 'f1-score': 0.8620689655172413, 'support': 1059.0} | {'precision': 0.9604448520981427, 'recall': 0.9532859174964438, 'f1-score': 0.9568519946314857, 'support': 17575.0} | {'precision': 0.9158798283261803, 'recall': 0.9203234501347709, 'f1-score': 0.9180962624361388, 'support': 9275.0} | 0.9402   | {'precision': 0.9020063345228092, 'recall': 0.923560692578362, 'f1-score': 0.9123390741949553, 'support': 27909.0}  | {'precision': 0.9406732585029842, 'recall': 0.9401985022752517, 'f1-score': 0.9403757810823142, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.1716          | {'precision': 0.8278829604130808, 'recall': 0.9084041548630784, 'f1-score': 0.8662764520486267, 'support': 1059.0} | {'precision': 0.949054915557544, 'recall': 0.9656330014224751, 'f1-score': 0.9572721888484643, 'support': 17575.0}  | {'precision': 0.9364918217710095, 'recall': 0.8950943396226415, 'f1-score': 0.9153252480705623, 'support': 9275.0} | 0.9400   | {'precision': 0.9044765659138781, 'recall': 0.9230438319693982, 'f1-score': 0.9129579629892177, 'support': 27909.0} | {'precision': 0.9402819822611845, 'recall': 0.9400193485972267, 'f1-score': 0.9398791485752167, 'support': 27909.0} |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:44cd8aa7100bf48c45decd89f2cf859d9cfd37a8e73f6e28d676c0265d6f5cba
 size 592318676