trainer: training complete at 2024-02-19 18:47:19.333329.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.835142785481386
+      value: 0.8340320326776308
 ---
 
 <!-- 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: 0.4315
-- Claim: {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}
-- Majorclaim: {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0}
-- O: {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}
-- Premise: {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}
-- Accuracy: 0.8351
-- Macro avg: {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0}
-- Weighted avg: {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}
+- Loss: 0.4397
+- Claim: {'precision': 0.5897372943776087, 'recall': 0.5649106302916275, 'f1-score': 0.5770570570570571, 'support': 4252.0}
+- Majorclaim: {'precision': 0.7365996649916248, 'recall': 0.806141154903758, 'f1-score': 0.7698030634573303, 'support': 2182.0}
+- O: {'precision': 0.9290423511006817, 'recall': 0.8963881401617251, 'f1-score': 0.9124231782265146, 'support': 9275.0}
+- Premise: {'precision': 0.8642291383310665, 'recall': 0.8854098360655738, 'f1-score': 0.8746912830478967, 'support': 12200.0}
+- Accuracy: 0.8340
+- Macro avg: {'precision': 0.7799021122002454, 'recall': 0.7882124403556711, 'f1-score': 0.7834936454471997, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8339706452686643, 'recall': 0.8340320326776308, 'f1-score': 0.8336850307178961, 'support': 27909.0}
 
 ## Model description
 
@@ -68,13 +68,13 @@ 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   | 0.5743          | {'precision': 0.5082508250825083, 'recall': 0.2535277516462841, 'f1-score': 0.33830221245881065, 'support': 4252.0} | {'precision': 0.5805350028457599, 'recall': 0.4674610449129239, 'f1-score': 0.5178979436405179, 'support': 2182.0} | {'precision': 0.8466549477820288, 'recall': 0.8828032345013477, 'f1-score': 0.8643513142615855, 'support': 9275.0} | {'precision': 0.7886490250696379, 'recall': 0.9282786885245902, 'f1-score': 0.8527861445783133, 'support': 12200.0} | 0.7743   | {'precision': 0.6810224501949838, 'recall': 0.6330176798962865, 'f1-score': 0.6433344037348069, 'support': 27909.0} | {'precision': 0.7489359214227731, 'recall': 0.7743380271596976, 'f1-score': 0.7520643421129422, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.4563          | {'precision': 0.5752391997680487, 'recall': 0.4666039510818438, 'f1-score': 0.5152577587326321, 'support': 4252.0}  | {'precision': 0.7043734230445753, 'recall': 0.7676443629697525, 'f1-score': 0.7346491228070176, 'support': 2182.0} | {'precision': 0.9195569478630566, 'recall': 0.8861455525606469, 'f1-score': 0.9025421402295064, 'support': 9275.0} | {'precision': 0.8371119902617163, 'recall': 0.9018852459016393, 'f1-score': 0.868292297979798, 'support': 12200.0}  | 0.8198   | {'precision': 0.7590703902343492, 'recall': 0.7555697781284707, 'f1-score': 0.7551853299372385, 'support': 27909.0} | {'precision': 0.8142361553305312, 'recall': 0.8198430613780501, 'f1-score': 0.8154403512156749, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.4417          | {'precision': 0.6114437791084497, 'recall': 0.43226716839134527, 'f1-score': 0.5064756131165611, 'support': 4252.0} | {'precision': 0.6908951798010712, 'recall': 0.8276810265811182, 'f1-score': 0.7531276063386154, 'support': 2182.0} | {'precision': 0.9402591445935099, 'recall': 0.8840970350404312, 'f1-score': 0.9113136252500555, 'support': 9275.0} | {'precision': 0.827903891509434, 'recall': 0.9207377049180328, 'f1-score': 0.8718565662837628, 'support': 12200.0}  | 0.8269   | {'precision': 0.7676254987531161, 'recall': 0.7661957337327319, 'f1-score': 0.7606933527472487, 'support': 27909.0} | {'precision': 0.821553021377153, 'recall': 0.8268658855566304, 'f1-score': 0.8200201629172901, 'support': 27909.0}  |
-| No log        | 4.0   | 164  | 0.4382          | {'precision': 0.5850725952813067, 'recall': 0.6065380997177798, 'f1-score': 0.5956120092378753, 'support': 4252.0}  | {'precision': 0.6956022944550669, 'recall': 0.8336388634280477, 'f1-score': 0.7583906608296852, 'support': 2182.0} | {'precision': 0.9404094704334897, 'recall': 0.8864690026954178, 'f1-score': 0.9126429126429128, 'support': 9275.0} | {'precision': 0.8778720250349996, 'recall': 0.8737704918032787, 'f1-score': 0.8758164564761943, 'support': 12200.0} | 0.8341   | {'precision': 0.7747390963012157, 'recall': 0.8001041144111309, 'f1-score': 0.7856155097966668, 'support': 27909.0} | {'precision': 0.8397961025237265, 'recall': 0.8341395248844459, 'f1-score': 0.8361845450923504, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.4315          | {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}  | {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0} | {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}  | {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}  | 0.8351   | {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0} | {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}  |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                          | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5888          | {'precision': 0.49844559585492226, 'recall': 0.2262464722483537, 'f1-score': 0.311226140407635, 'support': 4252.0}  | {'precision': 0.6139372822299651, 'recall': 0.40375802016498624, 'f1-score': 0.4871440420237766, 'support': 2182.0} | {'precision': 0.8171685569026202, 'recall': 0.9011320754716982, 'f1-score': 0.8570989078603293, 'support': 9275.0} | {'precision': 0.7903744062587315, 'recall': 0.9274590163934426, 'f1-score': 0.8534469754110725, 'support': 12200.0} | 0.7709   | {'precision': 0.6799814603115598, 'recall': 0.6146488960696201, 'f1-score': 0.6272290164257033, 'support': 27909.0} | {'precision': 0.7410085615761669, 'recall': 0.7709341072772224, 'f1-score': 0.7434134981235008, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4676          | {'precision': 0.574496644295302, 'recall': 0.5032925682031985, 'f1-score': 0.5365425598595963, 'support': 4252.0}   | {'precision': 0.6832784184514004, 'recall': 0.7603116406966086, 'f1-score': 0.7197396963123645, 'support': 2182.0}  | {'precision': 0.9165271733065506, 'recall': 0.8854986522911051, 'f1-score': 0.9007457775828033, 'support': 9275.0} | {'precision': 0.8488472059398202, 'recall': 0.8902459016393443, 'f1-score': 0.8690538107621524, 'support': 12200.0} | 0.8196   | {'precision': 0.7557873604982683, 'recall': 0.7598371907075642, 'f1-score': 0.7565204611292291, 'support': 27909.0} | {'precision': 0.816596749632328, 'recall': 0.8195564154932101, 'f1-score': 0.8172533792058241, 'support': 27909.0}  |
+| No log        | 3.0   | 123  | 0.4384          | {'precision': 0.6117381489841986, 'recall': 0.44614299153339604, 'f1-score': 0.5159798721610226, 'support': 4252.0} | {'precision': 0.7290375877736472, 'recall': 0.8088909257561869, 'f1-score': 0.7668911579404737, 'support': 2182.0}  | {'precision': 0.9303112313937754, 'recall': 0.889487870619946, 'f1-score': 0.9094416579397012, 'support': 9275.0}  | {'precision': 0.8289074635697906, 'recall': 0.9185245901639344, 'f1-score': 0.8714180178078463, 'support': 12200.0} | 0.8283   | {'precision': 0.774998607930353, 'recall': 0.7657615945183658, 'f1-score': 0.7659326764622609, 'support': 27909.0}  | {'precision': 0.8217126501390813, 'recall': 0.8283349457164355, 'f1-score': 0.8217304137626299, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.4487          | {'precision': 0.5776205218929678, 'recall': 0.6142991533396049, 'f1-score': 0.5953954866651471, 'support': 4252.0}  | {'precision': 0.7034400948991696, 'recall': 0.8153070577451879, 'f1-score': 0.7552536616429633, 'support': 2182.0}  | {'precision': 0.9331742243436754, 'recall': 0.8852830188679245, 'f1-score': 0.9085979860573199, 'support': 9275.0} | {'precision': 0.8791773778920309, 'recall': 0.8690163934426229, 'f1-score': 0.8740673564450308, 'support': 12200.0} | 0.8314   | {'precision': 0.7733530547569609, 'recall': 0.795976405848835, 'f1-score': 0.7833286227026153, 'support': 27909.0}  | {'precision': 0.837439667749803, 'recall': 0.8314163889784657, 'f1-score': 0.8337974548825171, 'support': 27909.0}  |
+| No log        | 5.0   | 205  | 0.4397          | {'precision': 0.5897372943776087, 'recall': 0.5649106302916275, 'f1-score': 0.5770570570570571, 'support': 4252.0}  | {'precision': 0.7365996649916248, 'recall': 0.806141154903758, 'f1-score': 0.7698030634573303, 'support': 2182.0}   | {'precision': 0.9290423511006817, 'recall': 0.8963881401617251, 'f1-score': 0.9124231782265146, 'support': 9275.0} | {'precision': 0.8642291383310665, 'recall': 0.8854098360655738, 'f1-score': 0.8746912830478967, 'support': 12200.0} | 0.8340   | {'precision': 0.7799021122002454, 'recall': 0.7882124403556711, 'f1-score': 0.7834936454471997, 'support': 27909.0} | {'precision': 0.8339706452686643, 'recall': 0.8340320326776308, 'f1-score': 0.8336850307178961, 'support': 27909.0} |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:6b40087e659871f06bd6a05a9a59d051f42431deb5ab9aa6b9aa44a5b35facf4
 size 592324828