End of training

README.md

@@ -18,14 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0120
+- Loss: 0.9766
 - Eader: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}
-- Nswer: {'precision': 0.07565011820330969, 'recall': 0.22695035460992907, 'f1': 0.11347517730496454, 'number': 141}
-- Uestion: {'precision': 0.09090909090909091, 'recall': 0.2360248447204969, 'f1': 0.13126079447322972, 'number': 161}
-- Overall Precision: 0.0827
-- Overall Recall: 0.1950
-- Overall F1: 0.1162
-- Overall Accuracy: 0.6022
+- Nswer: {'precision': 0.07159353348729793, 'recall': 0.2198581560283688, 'f1': 0.10801393728222997, 'number': 141}
+- Uestion: {'precision': 0.1038135593220339, 'recall': 0.30434782608695654, 'f1': 0.15481832543443919, 'number': 161}
+- Overall Precision: 0.0880
+- Overall Recall: 0.2228
+- Overall F1: 0.1262
+- Overall Accuracy: 0.6103
 
 ## Model description
 
@@ -55,17 +55,17 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Eader                                                      | Nswer                                                                                                           | Uestion                                                                                                        | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.4643        | 1.0   | 4    | 1.4050          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.003937007874015748, 'recall': 0.0070921985815602835, 'f1': 0.005063291139240506, 'number': 141} | {'precision': 0.013215859030837005, 'recall': 0.055900621118012424, 'f1': 0.02137767220902613, 'number': 161}  | 0.0078            | 0.0279         | 0.0122     | 0.2729           |
-| 1.109         | 2.0   | 8    | 1.2847          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 141}                                                     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 161}                                                    | 0.0               | 0.0            | 0.0        | 0.3613           |
-| 0.8554        | 3.0   | 12   | 1.1887          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.016172506738544475, 'recall': 0.0425531914893617, 'f1': 0.023437500000000003, 'number': 141}    | {'precision': 0.010554089709762533, 'recall': 0.024844720496894408, 'f1': 0.014814814814814814, 'number': 161} | 0.0133            | 0.0279         | 0.0180     | 0.4262           |
-| 0.6863        | 4.0   | 16   | 1.1168          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.029411764705882353, 'recall': 0.11347517730496454, 'f1': 0.04671532846715328, 'number': 141}    | {'precision': 0.034358047016274866, 'recall': 0.11801242236024845, 'f1': 0.05322128851540617, 'number': 161}   | 0.0319            | 0.0975         | 0.0481     | 0.5231           |
-| 0.5693        | 5.0   | 20   | 1.0760          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.05, 'recall': 0.1773049645390071, 'f1': 0.078003120124805, 'number': 141}                       | {'precision': 0.057803468208092484, 'recall': 0.18633540372670807, 'f1': 0.08823529411764705, 'number': 161}   | 0.0539            | 0.1532         | 0.0798     | 0.5545           |
-| 0.4404        | 6.0   | 24   | 1.0488          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.06329113924050633, 'recall': 0.2127659574468085, 'f1': 0.09756097560975611, 'number': 141}      | {'precision': 0.06534653465346535, 'recall': 0.20496894409937888, 'f1': 0.0990990990990991, 'number': 161}     | 0.0642            | 0.1755         | 0.0940     | 0.5750           |
-| 0.3635        | 7.0   | 28   | 1.0277          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07534246575342465, 'recall': 0.23404255319148937, 'f1': 0.11398963730569948, 'number': 141}     | {'precision': 0.08053691275167785, 'recall': 0.2236024844720497, 'f1': 0.11842105263157895, 'number': 161}     | 0.0776            | 0.1922         | 0.1106     | 0.5998           |
-| 0.3201        | 8.0   | 32   | 1.0163          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07801418439716312, 'recall': 0.23404255319148937, 'f1': 0.11702127659574468, 'number': 141}     | {'precision': 0.08747044917257683, 'recall': 0.22981366459627328, 'f1': 0.1267123287671233, 'number': 161}     | 0.0823            | 0.1950         | 0.1157     | 0.6037           |
-| 0.2915        | 9.0   | 36   | 1.0120          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07565011820330969, 'recall': 0.22695035460992907, 'f1': 0.11347517730496454, 'number': 141}     | {'precision': 0.09090909090909091, 'recall': 0.2360248447204969, 'f1': 0.13126079447322972, 'number': 161}     | 0.0827            | 0.1950         | 0.1162     | 0.6022           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                        | Nswer                                                                                                         | Uestion                                                                                                        | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3476        | 1.0   | 4    | 1.3017          | {'precision': 0.01, 'recall': 0.05263157894736842, 'f1': 0.016806722689075633, 'number': 57} | {'precision': 0.012711864406779662, 'recall': 0.0425531914893617, 'f1': 0.019575856443719414, 'number': 141}  | {'precision': 0.015772870662460567, 'recall': 0.062111801242236024, 'f1': 0.025157232704402514, 'number': 161} | 0.0135            | 0.0529         | 0.0215     | 0.3592           |
+| 1.0607        | 2.0   | 8    | 1.2217          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.015384615384615385, 'recall': 0.02127659574468085, 'f1': 0.017857142857142856, 'number': 141} | {'precision': 0.010050251256281407, 'recall': 0.012422360248447204, 'f1': 0.011111111111111113, 'number': 161} | 0.0127            | 0.0139         | 0.0133     | 0.3607           |
+| 0.8532        | 3.0   | 12   | 1.1632          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.034375, 'recall': 0.07801418439716312, 'f1': 0.047722342733188726, 'number': 141}             | {'precision': 0.021671826625386997, 'recall': 0.043478260869565216, 'f1': 0.02892561983471074, 'number': 161}  | 0.0280            | 0.0501         | 0.0359     | 0.3963           |
+| 0.7208        | 4.0   | 16   | 1.1060          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.02895752895752896, 'recall': 0.10638297872340426, 'f1': 0.04552352048558422, 'number': 141}   | {'precision': 0.0380952380952381, 'recall': 0.12422360248447205, 'f1': 0.05830903790087465, 'number': 161}     | 0.0336            | 0.0975         | 0.0499     | 0.4848           |
+| 0.6082        | 5.0   | 20   | 1.0625          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.040229885057471264, 'recall': 0.14893617021276595, 'f1': 0.06334841628959276, 'number': 141}  | {'precision': 0.06554307116104868, 'recall': 0.21739130434782608, 'f1': 0.10071942446043164, 'number': 161}    | 0.0530            | 0.1560         | 0.0792     | 0.5349           |
+| 0.4981        | 6.0   | 24   | 1.0294          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.04573804573804574, 'recall': 0.15602836879432624, 'f1': 0.0707395498392283, 'number': 141}    | {'precision': 0.08695652173913043, 'recall': 0.2732919254658385, 'f1': 0.13193403298350825, 'number': 161}     | 0.0667            | 0.1838         | 0.0979     | 0.5663           |
+| 0.416         | 7.0   | 28   | 1.0031          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.05908096280087528, 'recall': 0.19148936170212766, 'f1': 0.09030100334448161, 'number': 141}   | {'precision': 0.09475806451612903, 'recall': 0.2919254658385093, 'f1': 0.1430745814307458, 'number': 161}      | 0.0774            | 0.2061         | 0.1125     | 0.5868           |
+| 0.3618        | 8.0   | 32   | 0.9854          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.06919642857142858, 'recall': 0.2198581560283688, 'f1': 0.10526315789473685, 'number': 141}    | {'precision': 0.10103092783505155, 'recall': 0.30434782608695654, 'f1': 0.15170278637770898, 'number': 161}    | 0.0855            | 0.2228         | 0.1236     | 0.6034           |
+| 0.3256        | 9.0   | 36   | 0.9766          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}                                   | {'precision': 0.07159353348729793, 'recall': 0.2198581560283688, 'f1': 0.10801393728222997, 'number': 141}    | {'precision': 0.1038135593220339, 'recall': 0.30434782608695654, 'f1': 0.15481832543443919, 'number': 161}     | 0.0880            | 0.2228         | 0.1262     | 0.6103           |
 
 
 ### Framework versions

logs/events.out.tfevents.1741612593.DESKTOP-HA84SVN.1146024.0

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model.safetensors

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