trainer: training complete at 2024-02-19 20:31:04.777831.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8340320326776308
+      value: 0.836576014905586
 ---
 
 <!-- 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: 0.4397
-- 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}
+- Loss: 0.4521
+- Claim: {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}
+- Majorclaim: {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}
+- O: {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0}
+- Premise: {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0}
+- Accuracy: 0.8366
+- Macro avg: {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}
 
 ## Model description
 
@@ -64,17 +64,18 @@ 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: 5
+- num_epochs: 6
 
 ### Training results
 
-| 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} |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                 | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5884          | {'precision': 0.49868766404199477, 'recall': 0.22342427093132644, 'f1-score': 0.30859184667857725, 'support': 4252.0} | {'precision': 0.6308376575240919, 'recall': 0.3900091659028414, 'f1-score': 0.4820164259416595, 'support': 2182.0} | {'precision': 0.817888247382327, 'recall': 0.9011320754716982, 'f1-score': 0.8574946137273007, 'support': 9275.0}  | {'precision': 0.7873372125242449, 'recall': 0.9316393442622951, 'f1-score': 0.8534314461630875, 'support': 12200.0} | 0.7713   | {'precision': 0.6836876953681646, 'recall': 0.6115512141420403, 'f1-score': 0.6253835831276563, 'support': 27909.0} | {'precision': 0.7412782687839407, 'recall': 0.7712565838976674, 'f1-score': 0.7427359833384354, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4638          | {'precision': 0.5763888888888888, 'recall': 0.5075258701787394, 'f1-score': 0.5397698849424711, 'support': 4252.0}    | {'precision': 0.6741528762805359, 'recall': 0.7841429880843263, 'f1-score': 0.725, 'support': 2182.0}              | {'precision': 0.9210763341589732, 'recall': 0.8820485175202156, 'f1-score': 0.9011400561766811, 'support': 9275.0} | {'precision': 0.8506865437426442, 'recall': 0.8886885245901639, 'f1-score': 0.8692723992784124, 'support': 12200.0} | 0.8202   | {'precision': 0.7555761607677606, 'recall': 0.7656014750933613, 'f1-score': 0.7587955850993913, 'support': 27909.0} | {'precision': 0.8184874400582042, 'recall': 0.8202371994697051, 'f1-score': 0.8183829174463697, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.4497          | {'precision': 0.6111299626739056, 'recall': 0.4235653809971778, 'f1-score': 0.5003472704542298, 'support': 4252.0}    | {'precision': 0.7032967032967034, 'recall': 0.8212648945921174, 'f1-score': 0.7577167019027485, 'support': 2182.0} | {'precision': 0.9438293905139261, 'recall': 0.8732075471698113, 'f1-score': 0.9071460573476703, 'support': 9275.0} | {'precision': 0.8196342080532061, 'recall': 0.9293442622950819, 'f1-score': 0.8710482848692045, 'support': 12200.0} | 0.8252   | {'precision': 0.7694725661344353, 'recall': 0.7618455212635471, 'f1-score': 0.7590645786434633, 'support': 27909.0} | {'precision': 0.8200463271041109, 'recall': 0.8251818409831954, 'f1-score': 0.8177069473942856, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.4504          | {'precision': 0.5816213828142257, 'recall': 0.6192380056444027, 'f1-score': 0.5998405285340016, 'support': 4252.0}    | {'precision': 0.6949866054343666, 'recall': 0.8322639780018332, 'f1-score': 0.7574556830031283, 'support': 2182.0} | {'precision': 0.9409930715935335, 'recall': 0.8785983827493261, 'f1-score': 0.908725954836911, 'support': 9275.0}  | {'precision': 0.8776116937814848, 'recall': 0.8710655737704918, 'f1-score': 0.8743263811756962, 'support': 12200.0} | 0.8322   | {'precision': 0.7738031884059027, 'recall': 0.8002914850415135, 'f1-score': 0.7850871368874343, 'support': 27909.0} | {'precision': 0.8393023145203343, 'recall': 0.8321688344261707, 'f1-score': 0.8348025837219264, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.4540          | {'precision': 0.5803511891531451, 'recall': 0.6140639698965192, 'f1-score': 0.5967318020797622, 'support': 4252.0}    | {'precision': 0.7292703150912107, 'recall': 0.806141154903758, 'f1-score': 0.7657814540705268, 'support': 2182.0}  | {'precision': 0.9338842975206612, 'recall': 0.8893800539083558, 'f1-score': 0.9110890214269937, 'support': 9275.0} | {'precision': 0.8739005343197699, 'recall': 0.8713934426229508, 'f1-score': 0.8726451877693413, 'support': 12200.0} | 0.8331   | {'precision': 0.7793515840211966, 'recall': 0.795244655332896, 'f1-score': 0.7865618663366559, 'support': 27909.0}  | {'precision': 0.8378044523993523, 'recall': 0.8330646028162958, 'f1-score': 0.8350303027606281, 'support': 27909.0} |
+| No log        | 6.0   | 246  | 0.4521          | {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}    | {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}   | {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0} | {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0} | 0.8366   | {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}  | {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}   |
 
 
 ### Framework versions

meta_data/README_s42_e6.md

@@ -0,0 +1,86 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-simple
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: simple
+      split: test
+      args: simple
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.836576014905586
+---
+
+<!-- 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-simple
+
+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: 0.4521
+- Claim: {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}
+- Majorclaim: {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}
+- O: {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0}
+- Premise: {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0}
+- Accuracy: 0.8366
+- Macro avg: {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}
+
+## 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: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                 | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5884          | {'precision': 0.49868766404199477, 'recall': 0.22342427093132644, 'f1-score': 0.30859184667857725, 'support': 4252.0} | {'precision': 0.6308376575240919, 'recall': 0.3900091659028414, 'f1-score': 0.4820164259416595, 'support': 2182.0} | {'precision': 0.817888247382327, 'recall': 0.9011320754716982, 'f1-score': 0.8574946137273007, 'support': 9275.0}  | {'precision': 0.7873372125242449, 'recall': 0.9316393442622951, 'f1-score': 0.8534314461630875, 'support': 12200.0} | 0.7713   | {'precision': 0.6836876953681646, 'recall': 0.6115512141420403, 'f1-score': 0.6253835831276563, 'support': 27909.0} | {'precision': 0.7412782687839407, 'recall': 0.7712565838976674, 'f1-score': 0.7427359833384354, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4638          | {'precision': 0.5763888888888888, 'recall': 0.5075258701787394, 'f1-score': 0.5397698849424711, 'support': 4252.0}    | {'precision': 0.6741528762805359, 'recall': 0.7841429880843263, 'f1-score': 0.725, 'support': 2182.0}              | {'precision': 0.9210763341589732, 'recall': 0.8820485175202156, 'f1-score': 0.9011400561766811, 'support': 9275.0} | {'precision': 0.8506865437426442, 'recall': 0.8886885245901639, 'f1-score': 0.8692723992784124, 'support': 12200.0} | 0.8202   | {'precision': 0.7555761607677606, 'recall': 0.7656014750933613, 'f1-score': 0.7587955850993913, 'support': 27909.0} | {'precision': 0.8184874400582042, 'recall': 0.8202371994697051, 'f1-score': 0.8183829174463697, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.4497          | {'precision': 0.6111299626739056, 'recall': 0.4235653809971778, 'f1-score': 0.5003472704542298, 'support': 4252.0}    | {'precision': 0.7032967032967034, 'recall': 0.8212648945921174, 'f1-score': 0.7577167019027485, 'support': 2182.0} | {'precision': 0.9438293905139261, 'recall': 0.8732075471698113, 'f1-score': 0.9071460573476703, 'support': 9275.0} | {'precision': 0.8196342080532061, 'recall': 0.9293442622950819, 'f1-score': 0.8710482848692045, 'support': 12200.0} | 0.8252   | {'precision': 0.7694725661344353, 'recall': 0.7618455212635471, 'f1-score': 0.7590645786434633, 'support': 27909.0} | {'precision': 0.8200463271041109, 'recall': 0.8251818409831954, 'f1-score': 0.8177069473942856, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.4504          | {'precision': 0.5816213828142257, 'recall': 0.6192380056444027, 'f1-score': 0.5998405285340016, 'support': 4252.0}    | {'precision': 0.6949866054343666, 'recall': 0.8322639780018332, 'f1-score': 0.7574556830031283, 'support': 2182.0} | {'precision': 0.9409930715935335, 'recall': 0.8785983827493261, 'f1-score': 0.908725954836911, 'support': 9275.0}  | {'precision': 0.8776116937814848, 'recall': 0.8710655737704918, 'f1-score': 0.8743263811756962, 'support': 12200.0} | 0.8322   | {'precision': 0.7738031884059027, 'recall': 0.8002914850415135, 'f1-score': 0.7850871368874343, 'support': 27909.0} | {'precision': 0.8393023145203343, 'recall': 0.8321688344261707, 'f1-score': 0.8348025837219264, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.4540          | {'precision': 0.5803511891531451, 'recall': 0.6140639698965192, 'f1-score': 0.5967318020797622, 'support': 4252.0}    | {'precision': 0.7292703150912107, 'recall': 0.806141154903758, 'f1-score': 0.7657814540705268, 'support': 2182.0}  | {'precision': 0.9338842975206612, 'recall': 0.8893800539083558, 'f1-score': 0.9110890214269937, 'support': 9275.0} | {'precision': 0.8739005343197699, 'recall': 0.8713934426229508, 'f1-score': 0.8726451877693413, 'support': 12200.0} | 0.8331   | {'precision': 0.7793515840211966, 'recall': 0.795244655332896, 'f1-score': 0.7865618663366559, 'support': 27909.0}  | {'precision': 0.8378044523993523, 'recall': 0.8330646028162958, 'f1-score': 0.8350303027606281, 'support': 27909.0} |
+| No log        | 6.0   | 246  | 0.4521          | {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}    | {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}   | {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0} | {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0} | 0.8366   | {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}  | {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}   |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2