trainer: training complete at 2024-02-19 20:09:22.075710.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8280482998315956
+      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: 0.4474
-- Claim: {'precision': 0.5788206979542719, 'recall': 0.5656161806208843, 'f1-score': 0.5721422624003807, 'support': 4252.0}
-- Majorclaim: {'precision': 0.6985815602836879, 'recall': 0.812557286892759, 'f1-score': 0.751271186440678, 'support': 2182.0}
-- O: {'precision': 0.93909038572251, 'recall': 0.8793530997304583, 'f1-score': 0.9082405345211582, 'support': 9275.0}
-- Premise: {'precision': 0.8599473306200622, 'recall': 0.8832786885245901, 'f1-score': 0.8714568759856051, 'support': 12200.0}
-- Accuracy: 0.8280
-- Macro avg: {'precision': 0.7691099936451331, 'recall': 0.7852013139421729, 'f1-score': 0.7757777148369556, 'support': 27909.0}
-- Weighted avg: {'precision': 0.8308026562535961, 'recall': 0.8280482998315956, 'f1-score': 0.828683488238493, '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
 
@@ -64,16 +64,17 @@ 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: 4
+- num_epochs: 5
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.5887          | {'precision': 0.4995083579154376, 'recall': 0.2389463781749765, 'f1-score': 0.32325803372573975, 'support': 4252.0} | {'precision': 0.5970350404312669, 'recall': 0.4060494958753437, 'f1-score': 0.4833606110201855, 'support': 2182.0} | {'precision': 0.8159389073820247, 'recall': 0.898544474393531, 'f1-score': 0.8552516804351173, 'support': 9275.0}  | {'precision': 0.7941031247795726, 'recall': 0.9227868852459017, 'f1-score': 0.8536224741251849, 'support': 12200.0} | 0.7701   | {'precision': 0.6766463576270755, 'recall': 0.6165818084224383, 'f1-score': 0.6288731998265569, 'support': 27909.0} | {'precision': 0.7410703172581077, 'recall': 0.7701458310939123, 'f1-score': 0.7444136132792598, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.4737          | {'precision': 0.5664355062413314, 'recall': 0.48024459078080906, 'f1-score': 0.5197912689321624, 'support': 4252.0} | {'precision': 0.707936507936508, 'recall': 0.7153987167736022, 'f1-score': 0.7116480510599499, 'support': 2182.0}  | {'precision': 0.9119831504267819, 'recall': 0.8870080862533692, 'f1-score': 0.8993222562308703, 'support': 9275.0} | {'precision': 0.8385838813274201, 'recall': 0.8989344262295081, 'f1-score': 0.8677110530896431, 'support': 12200.0} | 0.8168   | {'precision': 0.7562347614830104, 'recall': 0.7453964550093222, 'f1-score': 0.7496181573281565, 'support': 27909.0} | {'precision': 0.8112998783639159, 'recall': 0.81683327958723, 'f1-score': 0.8130086100235527, 'support': 27909.0}   |
-| No log        | 3.0   | 123  | 0.4448          | {'precision': 0.6023609816713265, 'recall': 0.4560206961429915, 'f1-score': 0.5190737518404497, 'support': 4252.0}  | {'precision': 0.7517178195144297, 'recall': 0.7520623281393217, 'f1-score': 0.7518900343642613, 'support': 2182.0} | {'precision': 0.9046644403748788, 'recall': 0.9054447439353099, 'f1-score': 0.9050544239681, 'support': 9275.0}    | {'precision': 0.8368874773139746, 'recall': 0.9071311475409836, 'f1-score': 0.8705947136563877, 'support': 12200.0} | 0.8257   | {'precision': 0.7739076797186524, 'recall': 0.7551647289396517, 'f1-score': 0.7616532309572996, 'support': 27909.0} | {'precision': 0.8170223613871673, 'recall': 0.8257193020172704, 'f1-score': 0.8192110407653613, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.4474          | {'precision': 0.5788206979542719, 'recall': 0.5656161806208843, 'f1-score': 0.5721422624003807, 'support': 4252.0}  | {'precision': 0.6985815602836879, 'recall': 0.812557286892759, 'f1-score': 0.751271186440678, 'support': 2182.0}   | {'precision': 0.93909038572251, 'recall': 0.8793530997304583, 'f1-score': 0.9082405345211582, 'support': 9275.0}   | {'precision': 0.8599473306200622, 'recall': 0.8832786885245901, 'f1-score': 0.8714568759856051, 'support': 12200.0} | 0.8280   | {'precision': 0.7691099936451331, 'recall': 0.7852013139421729, 'f1-score': 0.7757777148369556, 'support': 27909.0} | {'precision': 0.8308026562535961, 'recall': 0.8280482998315956, 'f1-score': 0.828683488238493, '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

meta_data/README_s42_e5.md

@@ -0,0 +1,85 @@
+---
+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: 0.8340320326776308
+---
+
+<!-- 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: 0.4397
+- 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
+
+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: 5
+
+### 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} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

model.safetensors

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 size 592324828