End of training

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  results: []
+---
+
+<!-- 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. -->
+
+# layoutlm-funsd
+
+This model was trained from scratch on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7189
+- Answer: {'precision': 0.7106145251396648, 'recall': 0.7861557478368356, 'f1': 0.7464788732394366, 'number': 809}
+- Header: {'precision': 0.319672131147541, 'recall': 0.3277310924369748, 'f1': 0.32365145228215775, 'number': 119}
+- Question: {'precision': 0.7786596119929453, 'recall': 0.8291079812206573, 'f1': 0.8030923146884948, 'number': 1065}
+- Overall Precision: 0.7243
+- Overall Recall: 0.7817
+- Overall F1: 0.7519
+- Overall Accuracy: 0.8021
+
+## 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: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                       | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8213        | 1.0   | 10   | 1.5802          | {'precision': 0.02383419689119171, 'recall': 0.02843016069221261, 'f1': 0.02593010146561443, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.20350877192982456, 'recall': 0.21784037558685446, 'f1': 0.21043083900226758, 'number': 1065} | 0.1211            | 0.1279         | 0.1245     | 0.3954           |
+| 1.3926        | 2.0   | 20   | 1.2004          | {'precision': 0.15946348733233978, 'recall': 0.13226205191594562, 'f1': 0.14459459459459462, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5316139767054908, 'recall': 0.6, 'f1': 0.5637406263784737, 'number': 1065}                   | 0.3974            | 0.3743         | 0.3855     | 0.5855           |
+| 1.0495        | 3.0   | 30   | 0.9320          | {'precision': 0.4661558109833972, 'recall': 0.4511742892459827, 'f1': 0.4585427135678392, 'number': 809}    | {'precision': 0.02702702702702703, 'recall': 0.008403361344537815, 'f1': 0.01282051282051282, 'number': 119} | {'precision': 0.634020618556701, 'recall': 0.6929577464788732, 'f1': 0.6621803499327052, 'number': 1065}     | 0.5565            | 0.5539         | 0.5552     | 0.7115           |
+| 0.8025        | 4.0   | 40   | 0.7743          | {'precision': 0.6133333333333333, 'recall': 0.7391841779975278, 'f1': 0.6704035874439461, 'number': 809}    | {'precision': 0.12244897959183673, 'recall': 0.05042016806722689, 'f1': 0.07142857142857142, 'number': 119}  | {'precision': 0.6703483432455395, 'recall': 0.7408450704225352, 'f1': 0.7038358608385369, 'number': 1065}    | 0.6329            | 0.6989         | 0.6643     | 0.7663           |
+| 0.6413        | 5.0   | 50   | 0.7123          | {'precision': 0.6552462526766595, 'recall': 0.7564894932014833, 'f1': 0.7022375215146299, 'number': 809}    | {'precision': 0.24675324675324675, 'recall': 0.15966386554621848, 'f1': 0.19387755102040818, 'number': 119}  | {'precision': 0.6920609462710505, 'recall': 0.8103286384976526, 'f1': 0.7465397923875431, 'number': 1065}    | 0.6616            | 0.7496         | 0.7029     | 0.7852           |
+| 0.5528        | 6.0   | 60   | 0.6853          | {'precision': 0.6561844863731656, 'recall': 0.7737948084054388, 'f1': 0.7101531480431083, 'number': 809}    | {'precision': 0.21621621621621623, 'recall': 0.13445378151260504, 'f1': 0.16580310880829016, 'number': 119}  | {'precision': 0.7071729957805907, 'recall': 0.7868544600938967, 'f1': 0.7448888888888887, 'number': 1065}    | 0.6688            | 0.7426         | 0.7038     | 0.7858           |
+| 0.4716        | 7.0   | 70   | 0.6697          | {'precision': 0.6731182795698925, 'recall': 0.7737948084054388, 'f1': 0.7199539965497411, 'number': 809}    | {'precision': 0.25252525252525254, 'recall': 0.21008403361344538, 'f1': 0.22935779816513763, 'number': 119}  | {'precision': 0.7363945578231292, 'recall': 0.8131455399061033, 'f1': 0.7728692547969657, 'number': 1065}    | 0.6880            | 0.7612         | 0.7227     | 0.7954           |
+| 0.4138        | 8.0   | 80   | 0.6751          | {'precision': 0.7039911308203991, 'recall': 0.7849196538936959, 'f1': 0.7422559906487435, 'number': 809}    | {'precision': 0.22764227642276422, 'recall': 0.23529411764705882, 'f1': 0.23140495867768596, 'number': 119}  | {'precision': 0.7502131287297528, 'recall': 0.8262910798122066, 'f1': 0.7864164432529044, 'number': 1065}    | 0.7020            | 0.7742         | 0.7363     | 0.7985           |
+| 0.3721        | 9.0   | 90   | 0.6652          | {'precision': 0.710239651416122, 'recall': 0.8059332509270705, 'f1': 0.755066589461494, 'number': 809}      | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119}     | {'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065}    | 0.7186            | 0.7842         | 0.75       | 0.8042           |
+| 0.3571        | 10.0  | 100  | 0.6931          | {'precision': 0.7142857142857143, 'recall': 0.7911001236093943, 'f1': 0.750733137829912, 'number': 809}     | {'precision': 0.2857142857142857, 'recall': 0.25210084033613445, 'f1': 0.26785714285714285, 'number': 119}   | {'precision': 0.7804444444444445, 'recall': 0.8244131455399061, 'f1': 0.8018264840182647, 'number': 1065}    | 0.7281            | 0.7767         | 0.7516     | 0.8057           |
+| 0.3057        | 11.0  | 110  | 0.6920          | {'precision': 0.7172489082969432, 'recall': 0.8121137206427689, 'f1': 0.7617391304347826, 'number': 809}    | {'precision': 0.3225806451612903, 'recall': 0.33613445378151263, 'f1': 0.3292181069958848, 'number': 119}    | {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}    | 0.7290            | 0.7898         | 0.7582     | 0.8040           |
+| 0.2932        | 12.0  | 120  | 0.7032          | {'precision': 0.7149220489977728, 'recall': 0.7935723114956736, 'f1': 0.7521968365553603, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119}     | {'precision': 0.7945454545454546, 'recall': 0.8206572769953052, 'f1': 0.8073903002309469, 'number': 1065}    | 0.7369            | 0.7787         | 0.7573     | 0.8071           |
+| 0.274         | 13.0  | 130  | 0.7165          | {'precision': 0.7197309417040358, 'recall': 0.7935723114956736, 'f1': 0.7548500881834216, 'number': 809}    | {'precision': 0.30708661417322836, 'recall': 0.3277310924369748, 'f1': 0.3170731707317073, 'number': 119}    | {'precision': 0.7790492957746479, 'recall': 0.8309859154929577, 'f1': 0.8041799182189914, 'number': 1065}    | 0.7267            | 0.7858         | 0.7551     | 0.8032           |
+| 0.2608        | 14.0  | 140  | 0.7181          | {'precision': 0.7203579418344519, 'recall': 0.796044499381953, 'f1': 0.756312389900176, 'number': 809}      | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119}   | {'precision': 0.7802491103202847, 'recall': 0.8234741784037559, 'f1': 0.801279122887163, 'number': 1065}     | 0.7283            | 0.7827         | 0.7545     | 0.8008           |
+| 0.2542        | 15.0  | 150  | 0.7189          | {'precision': 0.7106145251396648, 'recall': 0.7861557478368356, 'f1': 0.7464788732394366, 'number': 809}    | {'precision': 0.319672131147541, 'recall': 0.3277310924369748, 'f1': 0.32365145228215775, 'number': 119}     | {'precision': 0.7786596119929453, 'recall': 0.8291079812206573, 'f1': 0.8030923146884948, 'number': 1065}    | 0.7243            | 0.7817         | 0.7519     | 0.8021           |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.2.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1733447004.DESKTOP-IQUCR22.36464.0

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preprocessor_config.json

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+  "ocr_lang": null,
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special_tokens_map.json

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tokenizer_config.json

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+}