End of training

README.md

@@ -11,21 +11,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 # layoutlm-doclaynet-test
 
-This model is a fine-tuned version of [pmorelr/layoutlm-doclaynet-test](https://huggingface.co/pmorelr/layoutlm-doclaynet-test) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1275
-- Footer: {'precision': 0.3970037453183521, 'recall': 0.4140625, 'f1': 0.40535372848948376, 'number': 256}
-- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 95}
-- Able: {'precision': 0.4575757575757576, 'recall': 0.58984375, 'f1': 0.515358361774744, 'number': 256}
-- Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 64}
-- Ext: {'precision': 0.4011627906976744, 'recall': 0.45098039215686275, 'f1': 0.4246153846153846, 'number': 459}
-- Icture: {'precision': 0.1509433962264151, 'recall': 0.16326530612244897, 'f1': 0.15686274509803919, 'number': 49}
-- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
-- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
-- Overall Precision: 0.4034
-- Overall Recall: 0.3966
-- Overall F1: 0.4
-- Overall Accuracy: 0.9645
+- Loss: 0.3029
+- Footer: {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201}
+- Header: {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83}
+- Able: {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158}
+- Aption: {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67}
+- Ext: {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326}
+- Icture: {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65}
+- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
+- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
+- Overall Precision: 0.5930
+- Overall Recall: 0.6505
+- Overall F1: 0.6204
+- Overall Accuracy: 0.9197
 
 ## Model description
 
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Footer                                                                                           | Header                                                     | Able                                                                                               | Aption                                                     | Ext                                                                                                       | Icture                                                                                                    | Itle                                                       | Ootnote                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.3298        | 1.0   | 43   | 0.2178          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 256}                                      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 95} | {'precision': 0.18888888888888888, 'recall': 0.33203125, 'f1': 0.24079320113314445, 'number': 256} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 64} | {'precision': 0.2778993435448578, 'recall': 0.2766884531590414, 'f1': 0.27729257641921395, 'number': 459} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 49}                                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.2309            | 0.1782         | 0.2011     | 0.9419           |
-| 0.2034        | 2.0   | 86   | 0.1505          | {'precision': 0.4134078212290503, 'recall': 0.2890625, 'f1': 0.34022988505747126, 'number': 256} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 95} | {'precision': 0.3140495867768595, 'recall': 0.4453125, 'f1': 0.3683360258481421, 'number': 256}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 64} | {'precision': 0.36401673640167365, 'recall': 0.3790849673202614, 'f1': 0.3713980789754536, 'number': 459} | {'precision': 0.0625, 'recall': 0.061224489795918366, 'f1': 0.061855670103092786, 'number': 49}           | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.3408            | 0.3067         | 0.3229     | 0.9579           |
-| 0.1584        | 3.0   | 129  | 0.1275          | {'precision': 0.3970037453183521, 'recall': 0.4140625, 'f1': 0.40535372848948376, 'number': 256} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 95} | {'precision': 0.4575757575757576, 'recall': 0.58984375, 'f1': 0.515358361774744, 'number': 256}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 64} | {'precision': 0.4011627906976744, 'recall': 0.45098039215686275, 'f1': 0.4246153846153846, 'number': 459} | {'precision': 0.1509433962264151, 'recall': 0.16326530612244897, 'f1': 0.15686274509803919, 'number': 49} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.4034            | 0.3966         | 0.4        | 0.9645           |
+| Training Loss | Epoch | Step | Validation Loss | Footer                                                                                                   | Header                                                                                                  | Able                                                                                                     | Aption                                                                                                     | Ext                                                                                                      | Icture                                                                                                     | Itle                                                      | Ootnote                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.2414        | 1.0   | 426  | 0.1727          | {'precision': 0.6724137931034483, 'recall': 0.7761194029850746, 'f1': 0.720554272517321, 'number': 201}  | {'precision': 0.7142857142857143, 'recall': 0.5421686746987951, 'f1': 0.6164383561643836, 'number': 83} | {'precision': 0.5069124423963134, 'recall': 0.6962025316455697, 'f1': 0.5866666666666668, 'number': 158} | {'precision': 0.22916666666666666, 'recall': 0.16417910447761194, 'f1': 0.19130434782608696, 'number': 67} | {'precision': 0.5323383084577115, 'recall': 0.656441717791411, 'f1': 0.587912087912088, 'number': 326}   | {'precision': 0.24528301886792453, 'recall': 0.2, 'f1': 0.22033898305084745, 'number': 65}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5409            | 0.6053         | 0.5713     | 0.9584           |
+| 0.1037        | 2.0   | 852  | 0.1726          | {'precision': 0.7045454545454546, 'recall': 0.7711442786069652, 'f1': 0.7363420427553445, 'number': 201} | {'precision': 0.8529411764705882, 'recall': 0.6987951807228916, 'f1': 0.7682119205298014, 'number': 83} | {'precision': 0.5658536585365853, 'recall': 0.7341772151898734, 'f1': 0.6391184573002755, 'number': 158} | {'precision': 0.25333333333333335, 'recall': 0.2835820895522388, 'f1': 0.2676056338028169, 'number': 67}   | {'precision': 0.5640394088669951, 'recall': 0.7024539877300614, 'f1': 0.6256830601092896, 'number': 326} | {'precision': 0.16666666666666666, 'recall': 0.18461538461538463, 'f1': 0.17518248175182485, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5631            | 0.6494         | 0.6032     | 0.9510           |
+| 0.0647        | 3.0   | 1278 | 0.3029          | {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201} | {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83} | {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158}  | {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67}   | {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326} | {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5930            | 0.6505         | 0.6204     | 0.9197           |
 
 
 ### Framework versions
 
 - Transformers 4.26.1
-- Pytorch 1.12.1
+- Pytorch 1.12.1+cu102
 - Datasets 2.9.0
 - Tokenizers 0.13.2

logs/events.out.tfevents.1679079265.instance-1.22775.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:82d35aa3ac053a693ed035d46e970068660332a5bf1d5dfe41c4c3144efd47af
-size 6327
+oid sha256:b2152f959f5718a31563fbc36ccca4464d38871e5944fc05c05b7affc7399367
+size 6681

tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 512
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
   "added_tokens": [
     {
       "id": 0,

tokenizer_config.json

@@ -12,7 +12,7 @@
   "do_lower_case": true,
   "mask_token": "[MASK]",
   "model_max_length": 512,
-  "name_or_path": "pmorelr/layoutlm-doclaynet-test",
+  "name_or_path": "microsoft/layoutlmv2-base-uncased",
   "never_split": null,
   "only_label_first_subword": true,
   "pad_token": "[PAD]",