End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+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 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: 0.6795
+- Answer: {'precision': 0.7247807017543859, 'recall': 0.8170580964153276, 'f1': 0.768158047646717, 'number': 809}
+- Header: {'precision': 0.3208955223880597, 'recall': 0.36134453781512604, 'f1': 0.33992094861660077, 'number': 119}
+- Question: {'precision': 0.7793721973094171, 'recall': 0.815962441314554, 'f1': 0.7972477064220184, 'number': 1065}
+- Overall Precision: 0.7279
+- Overall Recall: 0.7893
+- Overall F1: 0.7573
+- Overall Accuracy: 0.8097
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7157        | 1.0   | 10   | 1.4996          | {'precision': 0.07431693989071038, 'recall': 0.08405438813349815, 'f1': 0.0788863109048724, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.22849213691026826, 'recall': 0.231924882629108, 'f1': 0.23019571295433364, 'number': 1065} | 0.1578            | 0.1581         | 0.1579     | 0.4464           |
+| 1.3388        | 2.0   | 20   | 1.1680          | {'precision': 0.2980891719745223, 'recall': 0.2892459826946848, 'f1': 0.2936010037641154, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.49192245557350567, 'recall': 0.571830985915493, 'f1': 0.5288753799392097, 'number': 1065}  | 0.4167            | 0.4230         | 0.4198     | 0.6170           |
+| 1.0157        | 3.0   | 30   | 0.8917          | {'precision': 0.5579302587176603, 'recall': 0.6131025957972805, 'f1': 0.5842167255594817, 'number': 809}   | {'precision': 0.13513513513513514, 'recall': 0.04201680672268908, 'f1': 0.06410256410256411, 'number': 119} | {'precision': 0.5931528662420382, 'recall': 0.6995305164319249, 'f1': 0.6419646704006894, 'number': 1065}  | 0.5710            | 0.6252         | 0.5969     | 0.7325           |
+| 0.7726        | 4.0   | 40   | 0.7448          | {'precision': 0.6359875904860393, 'recall': 0.7601977750309024, 'f1': 0.6925675675675675, 'number': 809}   | {'precision': 0.29850746268656714, 'recall': 0.16806722689075632, 'f1': 0.21505376344086022, 'number': 119} | {'precision': 0.691696113074205, 'recall': 0.7352112676056338, 'f1': 0.7127901684114702, 'number': 1065}   | 0.6547            | 0.7115         | 0.6819     | 0.7725           |
+| 0.6355        | 5.0   | 50   | 0.6844          | {'precision': 0.6714129244249726, 'recall': 0.757725587144623, 'f1': 0.7119628339140535, 'number': 809}    | {'precision': 0.3170731707317073, 'recall': 0.2184873949579832, 'f1': 0.25870646766169153, 'number': 119}   | {'precision': 0.705, 'recall': 0.7943661971830986, 'f1': 0.7470198675496689, 'number': 1065}               | 0.6765            | 0.7451         | 0.7092     | 0.7944           |
+| 0.5353        | 6.0   | 60   | 0.6699          | {'precision': 0.6676860346585117, 'recall': 0.8096415327564895, 'f1': 0.7318435754189945, 'number': 809}   | {'precision': 0.3068181818181818, 'recall': 0.226890756302521, 'f1': 0.2608695652173913, 'number': 119}     | {'precision': 0.7171717171717171, 'recall': 0.8, 'f1': 0.7563249001331558, 'number': 1065}                 | 0.6797            | 0.7697         | 0.7219     | 0.7951           |
+| 0.4614        | 7.0   | 70   | 0.6517          | {'precision': 0.7006507592190889, 'recall': 0.7985166872682324, 'f1': 0.7463893703061815, 'number': 809}   | {'precision': 0.26495726495726496, 'recall': 0.2605042016806723, 'f1': 0.2627118644067797, 'number': 119}   | {'precision': 0.7355442176870748, 'recall': 0.812206572769953, 'f1': 0.7719767960731816, 'number': 1065}   | 0.6962            | 0.7737         | 0.7329     | 0.8045           |
+| 0.4076        | 8.0   | 80   | 0.6567          | {'precision': 0.7194719471947195, 'recall': 0.8084054388133498, 'f1': 0.761350407450524, 'number': 809}    | {'precision': 0.2713178294573643, 'recall': 0.29411764705882354, 'f1': 0.28225806451612906, 'number': 119}  | {'precision': 0.7508561643835616, 'recall': 0.8234741784037559, 'f1': 0.7854903716972683, 'number': 1065}  | 0.7099            | 0.7858         | 0.7459     | 0.8056           |
+| 0.3664        | 9.0   | 90   | 0.6529          | {'precision': 0.7176724137931034, 'recall': 0.823238566131026, 'f1': 0.7668393782383419, 'number': 809}    | {'precision': 0.265625, 'recall': 0.2857142857142857, 'f1': 0.27530364372469635, 'number': 119}             | {'precision': 0.7686768676867687, 'recall': 0.8018779342723005, 'f1': 0.7849264705882354, 'number': 1065}  | 0.7171            | 0.7797         | 0.7471     | 0.8098           |
+| 0.3515        | 10.0  | 100  | 0.6537          | {'precision': 0.7129032258064516, 'recall': 0.8195302843016069, 'f1': 0.7625071880391028, 'number': 809}   | {'precision': 0.32075471698113206, 'recall': 0.2857142857142857, 'f1': 0.30222222222222217, 'number': 119}  | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}  | 0.7280            | 0.7898         | 0.7576     | 0.8157           |
+| 0.3077        | 11.0  | 110  | 0.6694          | {'precision': 0.7241379310344828, 'recall': 0.8046971569839307, 'f1': 0.7622950819672132, 'number': 809}   | {'precision': 0.302158273381295, 'recall': 0.35294117647058826, 'f1': 0.3255813953488373, 'number': 119}    | {'precision': 0.7599653379549394, 'recall': 0.8234741784037559, 'f1': 0.790446146913024, 'number': 1065}   | 0.7162            | 0.7878         | 0.7503     | 0.8078           |
+| 0.2941        | 12.0  | 120  | 0.6687          | {'precision': 0.7135076252723311, 'recall': 0.8096415327564895, 'f1': 0.7585408222350897, 'number': 809}   | {'precision': 0.3053435114503817, 'recall': 0.33613445378151263, 'f1': 0.32000000000000006, 'number': 119}  | {'precision': 0.7793721973094171, 'recall': 0.815962441314554, 'f1': 0.7972477064220184, 'number': 1065}   | 0.7227            | 0.7847         | 0.7525     | 0.8118           |
+| 0.2726        | 13.0  | 130  | 0.6769          | {'precision': 0.720348204570185, 'recall': 0.8182941903584673, 'f1': 0.7662037037037037, 'number': 809}    | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}  | {'precision': 0.7713280562884784, 'recall': 0.8234741784037559, 'f1': 0.7965485921889193, 'number': 1065}  | 0.7248            | 0.7928         | 0.7572     | 0.8095           |
+| 0.2575        | 14.0  | 140  | 0.6821          | {'precision': 0.7238723872387238, 'recall': 0.8133498145859085, 'f1': 0.7660069848661233, 'number': 809}   | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}     | {'precision': 0.7810545129579982, 'recall': 0.8206572769953052, 'f1': 0.8003663003663004, 'number': 1065}  | 0.7296            | 0.7908         | 0.7590     | 0.8095           |
+| 0.2563        | 15.0  | 150  | 0.6795          | {'precision': 0.7247807017543859, 'recall': 0.8170580964153276, 'f1': 0.768158047646717, 'number': 809}    | {'precision': 0.3208955223880597, 'recall': 0.36134453781512604, 'f1': 0.33992094861660077, 'number': 119}  | {'precision': 0.7793721973094171, 'recall': 0.815962441314554, 'f1': 0.7972477064220184, 'number': 1065}   | 0.7279            | 0.7893         | 0.7573     | 0.8097           |
+
+
+### Framework versions
+
+- Transformers 4.47.0
+- Pytorch 2.5.1+cpu
+- Datasets 3.2.0
+- Tokenizers 0.21.0

logs/events.out.tfevents.1735326415.DESKTOP-CP49C74.10700.0

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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+}