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 [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9065
-- Answer: {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}
-- Header: {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119}
-- Question: {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077}
-- Overall Precision: 0.8330
-- Overall Recall: 0.8723
-- Overall F1: 0.8522
-- Overall Accuracy: 0.7918
+- Loss: 1.2479
+- Answer: {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}
+- Header: {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}
+- Question: {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077}
+- Overall Precision: 0.8657
+- Overall Recall: 0.8932
+- Overall F1: 0.8792
+- Overall Accuracy: 0.8133
 
 ## Model description
 
@@ -48,15 +48,15 @@ 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
-- training_steps: 200
+- training_steps: 500
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.7017        | 5.26  | 100  | 0.7391          | {'precision': 0.8216340621403913, 'recall': 0.8739290085679314, 'f1': 0.8469750889679716, 'number': 817} | {'precision': 0.4533333333333333, 'recall': 0.2857142857142857, 'f1': 0.3505154639175258, 'number': 119}  | {'precision': 0.8234323432343235, 'recall': 0.9266480965645311, 'f1': 0.8719965050240279, 'number': 1077} | 0.8098            | 0.8674         | 0.8376     | 0.8073           |
-| 0.1656        | 10.53 | 200  | 0.9065          | {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}  | {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119} | {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077} | 0.8330            | 0.8723         | 0.8522     | 0.7918           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4245        | 10.53 | 200  | 0.9942          | {'precision': 0.8187845303867404, 'recall': 0.9069767441860465, 'f1': 0.8606271777003485, 'number': 817} | {'precision': 0.5178571428571429, 'recall': 0.48739495798319327, 'f1': 0.5021645021645021, 'number': 119} | {'precision': 0.8821396192203083, 'recall': 0.903435468895079, 'f1': 0.8926605504587157, 'number': 1077} | 0.8358            | 0.8803         | 0.8575     | 0.8150           |
+| 0.0366        | 21.05 | 400  | 1.2479          | {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817} | {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}  | {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077} | 0.8657            | 0.8932         | 0.8792     | 0.8133           |
 
 
 ### Framework versions

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+size 5932

logs/events.out.tfevents.1678442783.ab0c0a9c2e5c.1886.2

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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": 1,
+    "pad_type_id": 0,
+    "pad_token": "<pad>"
+  },
   "added_tokens": [
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