End of training

README.md

@@ -16,14 +16,14 @@ 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: 0.6680
-- Answer: {'precision': 0.6523109243697479, 'recall': 0.7676143386897404, 'f1': 0.7052810902896083, 'number': 809}
-- Header: {'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119}
-- Question: {'precision': 0.7324786324786324, 'recall': 0.8046948356807512, 'f1': 0.766890380313199, 'number': 1065}
-- Overall Precision: 0.6751
-- Overall Recall: 0.7536
-- Overall F1: 0.7122
-- Overall Accuracy: 0.7957
+- Loss: 0.6510
+- Answer: {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}
+- Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}
+- Question: {'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065}
+- Overall Precision: 0.7090
+- Overall Recall: 0.7737
+- Overall F1: 0.7399
+- Overall Accuracy: 0.8032
 
 ## Model description
 
@@ -52,18 +52,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8904        | 1.0   | 10   | 1.6569          | {'precision': 0.0226628895184136, 'recall': 0.029666254635352288, 'f1': 0.025695931477516063, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.13233724653148346, 'recall': 0.11643192488262911, 'f1': 0.12387612387612387, 'number': 1065} | 0.074             | 0.0743         | 0.0741     | 0.3562           |
-| 1.5103        | 2.0   | 20   | 1.3215          | {'precision': 0.16376306620209058, 'recall': 0.17428924598269468, 'f1': 0.1688622754491018, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.37047970479704795, 'recall': 0.47136150234741786, 'f1': 0.4148760330578512, 'number': 1065}  | 0.2902            | 0.3226         | 0.3055     | 0.5678           |
-| 1.1593        | 3.0   | 30   | 0.9985          | {'precision': 0.45689655172413796, 'recall': 0.5241038318912238, 'f1': 0.4881980426021877, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5746268656716418, 'recall': 0.6507042253521127, 'f1': 0.6103038309114928, 'number': 1065}    | 0.5176            | 0.5605         | 0.5382     | 0.6952           |
-| 0.8944        | 4.0   | 40   | 0.8291          | {'precision': 0.5676982591876208, 'recall': 0.7255871446229913, 'f1': 0.6370048833423766, 'number': 809}     | {'precision': 0.125, 'recall': 0.05042016806722689, 'f1': 0.07185628742514971, 'number': 119}               | {'precision': 0.648323301805675, 'recall': 0.707981220657277, 'f1': 0.6768402154398564, 'number': 1065}      | 0.6               | 0.6759         | 0.6357     | 0.7440           |
-| 0.7344        | 5.0   | 50   | 0.7416          | {'precision': 0.6231422505307855, 'recall': 0.7255871446229913, 'f1': 0.670474014848658, 'number': 809}      | {'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119}  | {'precision': 0.6871794871794872, 'recall': 0.7549295774647887, 'f1': 0.7194630872483222, 'number': 1065}    | 0.6419            | 0.7070         | 0.6729     | 0.7718           |
-| 0.6312        | 6.0   | 60   | 0.7028          | {'precision': 0.616956077630235, 'recall': 0.7466007416563659, 'f1': 0.6756152125279643, 'number': 809}      | {'precision': 0.2413793103448276, 'recall': 0.17647058823529413, 'f1': 0.2038834951456311, 'number': 119}   | {'precision': 0.7020033388981636, 'recall': 0.7896713615023474, 'f1': 0.7432611577551921, 'number': 1065}    | 0.6475            | 0.7356         | 0.6887     | 0.7880           |
-| 0.5603        | 7.0   | 70   | 0.6980          | {'precision': 0.6331550802139038, 'recall': 0.7317676143386898, 'f1': 0.6788990825688073, 'number': 809}     | {'precision': 0.2604166666666667, 'recall': 0.21008403361344538, 'f1': 0.23255813953488375, 'number': 119}  | {'precision': 0.6994219653179191, 'recall': 0.7953051643192488, 'f1': 0.7442882249560634, 'number': 1065}    | 0.6530            | 0.7346         | 0.6914     | 0.7861           |
-| 0.5272        | 8.0   | 80   | 0.6733          | {'precision': 0.6592827004219409, 'recall': 0.7725587144622992, 'f1': 0.7114399544678428, 'number': 809}     | {'precision': 0.25, 'recall': 0.20168067226890757, 'f1': 0.22325581395348837, 'number': 119}                | {'precision': 0.7175188600167645, 'recall': 0.8037558685446009, 'f1': 0.758193091231178, 'number': 1065}     | 0.6728            | 0.7551         | 0.7116     | 0.7927           |
-| 0.4849        | 9.0   | 90   | 0.6716          | {'precision': 0.6549145299145299, 'recall': 0.757725587144623, 'f1': 0.7025787965616046, 'number': 809}      | {'precision': 0.23809523809523808, 'recall': 0.21008403361344538, 'f1': 0.22321428571428573, 'number': 119} | {'precision': 0.7216666666666667, 'recall': 0.8131455399061033, 'f1': 0.7646799116997792, 'number': 1065}    | 0.6711            | 0.7546         | 0.7104     | 0.7961           |
-| 0.4695        | 10.0  | 100  | 0.6680          | {'precision': 0.6523109243697479, 'recall': 0.7676143386897404, 'f1': 0.7052810902896083, 'number': 809}     | {'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119} | {'precision': 0.7324786324786324, 'recall': 0.8046948356807512, 'f1': 0.766890380313199, 'number': 1065}     | 0.6751            | 0.7536         | 0.7122     | 0.7957           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                        | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7428        | 1.0   | 10   | 1.5458          | {'precision': 0.030690537084398978, 'recall': 0.04449938195302843, 'f1': 0.036326942482341064, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.18740157480314962, 'recall': 0.22347417840375586, 'f1': 0.2038543897216274, 'number': 1065} | 0.1122            | 0.1375         | 0.1235     | 0.4326           |
+| 1.3991        | 2.0   | 20   | 1.2229          | {'precision': 0.1326676176890157, 'recall': 0.11495673671199011, 'f1': 0.12317880794701987, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.5, 'recall': 0.5352112676056338, 'f1': 0.5170068027210885, 'number': 1065}                  | 0.3597            | 0.3327         | 0.3457     | 0.5731           |
+| 1.0911        | 3.0   | 30   | 0.9391          | {'precision': 0.47231638418079097, 'recall': 0.5166872682323856, 'f1': 0.4935064935064935, 'number': 809}     | {'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119} | {'precision': 0.6528268551236749, 'recall': 0.6938967136150235, 'f1': 0.6727355484751935, 'number': 1065}   | 0.5651            | 0.5815         | 0.5732     | 0.7183           |
+| 0.8461        | 4.0   | 40   | 0.7784          | {'precision': 0.6047717842323651, 'recall': 0.7206427688504327, 'f1': 0.6576424139875917, 'number': 809}      | {'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119}    | {'precision': 0.6666666666666666, 'recall': 0.7455399061032864, 'f1': 0.7039007092198581, 'number': 1065}   | 0.6275            | 0.6949         | 0.6595     | 0.7638           |
+| 0.6966        | 5.0   | 50   | 0.7307          | {'precision': 0.6315228966986155, 'recall': 0.7330037082818294, 'f1': 0.6784897025171623, 'number': 809}      | {'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119}    | {'precision': 0.6925064599483204, 'recall': 0.7549295774647887, 'f1': 0.7223719676549865, 'number': 1065}   | 0.6494            | 0.7090         | 0.6779     | 0.7703           |
+| 0.6037        | 6.0   | 60   | 0.6834          | {'precision': 0.657922350472193, 'recall': 0.7750309023485785, 'f1': 0.7116912599318955, 'number': 809}       | {'precision': 0.3150684931506849, 'recall': 0.19327731092436976, 'f1': 0.23958333333333334, 'number': 119}    | {'precision': 0.7021103896103896, 'recall': 0.812206572769953, 'f1': 0.7531562908141053, 'number': 1065}    | 0.6709            | 0.7602         | 0.7128     | 0.7915           |
+| 0.5421        | 7.0   | 70   | 0.6692          | {'precision': 0.671306209850107, 'recall': 0.7750309023485785, 'f1': 0.7194492254733217, 'number': 809}       | {'precision': 0.2823529411764706, 'recall': 0.20168067226890757, 'f1': 0.23529411764705882, 'number': 119}    | {'precision': 0.7227467811158799, 'recall': 0.7906103286384977, 'f1': 0.7551569506726458, 'number': 1065}   | 0.6836            | 0.7491         | 0.7149     | 0.7931           |
+| 0.5085        | 8.0   | 80   | 0.6549          | {'precision': 0.6901874310915105, 'recall': 0.7737948084054388, 'f1': 0.7296037296037297, 'number': 809}      | {'precision': 0.3023255813953488, 'recall': 0.2184873949579832, 'f1': 0.25365853658536586, 'number': 119}     | {'precision': 0.7408637873754153, 'recall': 0.8375586854460094, 'f1': 0.7862494490965183, 'number': 1065}   | 0.7028            | 0.7747         | 0.7370     | 0.7982           |
+| 0.4692        | 9.0   | 90   | 0.6517          | {'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809}      | {'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119}      | {'precision': 0.7534364261168385, 'recall': 0.8234741784037559, 'f1': 0.7868999551368328, 'number': 1065}   | 0.7100            | 0.7727         | 0.7400     | 0.8025           |
+| 0.4538        | 10.0  | 100  | 0.6510          | {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}      | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}     | {'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065}    | 0.7090            | 0.7737         | 0.7399     | 0.8032           |
 
 
 ### Framework versions

logs/events.out.tfevents.1690948081.210128a59701.2240.0

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+size 11137

pytorch_model.bin

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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": [
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