End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd1
+  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-funsd1
+
+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: 0.6985
+- Answer: {'precision': 0.7292134831460674, 'recall': 0.8022249690976514, 'f1': 0.7639788110653325, 'number': 809}
+- Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
+- Question: {'precision': 0.7711267605633803, 'recall': 0.8225352112676056, 'f1': 0.7960018173557474, 'number': 1065}
+- Overall Precision: 0.7242
+- Overall Recall: 0.7852
+- Overall F1: 0.7535
+- Overall Accuracy: 0.8108
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7326        | 1.0   | 10   | 1.5225          | {'precision': 0.0576307363927428, 'recall': 0.06674907292954264, 'f1': 0.06185567010309278, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2126899016979446, 'recall': 0.22347417840375586, 'f1': 0.21794871794871795, 'number': 1065} | 0.1420            | 0.1465         | 0.1442     | 0.4302           |
+| 1.3559        | 2.0   | 20   | 1.1907          | {'precision': 0.2647058823529412, 'recall': 0.22249690976514216, 'f1': 0.24177300201477503, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.48519736842105265, 'recall': 0.5539906103286385, 'f1': 0.5173169662428759, 'number': 1065}  | 0.4055            | 0.3864         | 0.3957     | 0.5967           |
+| 1.0329        | 3.0   | 30   | 0.9021          | {'precision': 0.4879518072289157, 'recall': 0.5006180469715699, 'f1': 0.49420378279438687, 'number': 809}  | {'precision': 0.1, 'recall': 0.04201680672268908, 'f1': 0.059171597633136105, 'number': 119}                | {'precision': 0.647636039250669, 'recall': 0.6816901408450704, 'f1': 0.6642268984446478, 'number': 1065}    | 0.5677            | 0.5700         | 0.5689     | 0.7304           |
+| 0.779         | 4.0   | 40   | 0.7524          | {'precision': 0.6258205689277899, 'recall': 0.7070457354758962, 'f1': 0.6639582124201974, 'number': 809}   | {'precision': 0.25675675675675674, 'recall': 0.15966386554621848, 'f1': 0.19689119170984457, 'number': 119} | {'precision': 0.6596814752724225, 'recall': 0.7389671361502348, 'f1': 0.6970770593445527, 'number': 1065}   | 0.6318            | 0.6914         | 0.6603     | 0.7734           |
+| 0.6249        | 5.0   | 50   | 0.6899          | {'precision': 0.6615553121577218, 'recall': 0.7466007416563659, 'f1': 0.7015098722415796, 'number': 809}   | {'precision': 0.3157894736842105, 'recall': 0.20168067226890757, 'f1': 0.24615384615384614, 'number': 119}  | {'precision': 0.6818181818181818, 'recall': 0.7746478873239436, 'f1': 0.7252747252747253, 'number': 1065}   | 0.6608            | 0.7291         | 0.6932     | 0.7938           |
+| 0.5376        | 6.0   | 60   | 0.6911          | {'precision': 0.6773504273504274, 'recall': 0.7836835599505563, 'f1': 0.7266475644699141, 'number': 809}   | {'precision': 0.29411764705882354, 'recall': 0.21008403361344538, 'f1': 0.2450980392156863, 'number': 119}  | {'precision': 0.7166377816291161, 'recall': 0.7765258215962442, 'f1': 0.7453808021631364, 'number': 1065}   | 0.6832            | 0.7456         | 0.7131     | 0.7926           |
+| 0.4627        | 7.0   | 70   | 0.6573          | {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}   | {'precision': 0.2882882882882883, 'recall': 0.2689075630252101, 'f1': 0.2782608695652174, 'number': 119}    | {'precision': 0.735494880546075, 'recall': 0.8093896713615023, 'f1': 0.7706750111756816, 'number': 1065}    | 0.6975            | 0.7727         | 0.7332     | 0.8012           |
+| 0.4082        | 8.0   | 80   | 0.6650          | {'precision': 0.6871741397288843, 'recall': 0.8145859085290482, 'f1': 0.7454751131221721, 'number': 809}   | {'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119}   | {'precision': 0.7446626814688301, 'recall': 0.8187793427230047, 'f1': 0.7799642218246869, 'number': 1065}   | 0.6976            | 0.7837         | 0.7382     | 0.8040           |
+| 0.3665        | 9.0   | 90   | 0.6682          | {'precision': 0.7011995637949836, 'recall': 0.7948084054388134, 'f1': 0.7450753186558517, 'number': 809}   | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119}   | {'precision': 0.7519582245430809, 'recall': 0.8112676056338028, 'f1': 0.7804878048780487, 'number': 1065}   | 0.7068            | 0.7742         | 0.7390     | 0.8071           |
+| 0.3554        | 10.0  | 100  | 0.6680          | {'precision': 0.7168338907469343, 'recall': 0.7948084054388134, 'f1': 0.753810082063306, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.35294117647058826, 'f1': 0.34285714285714286, 'number': 119}  | {'precision': 0.7586206896551724, 'recall': 0.8262910798122066, 'f1': 0.7910112359550561, 'number': 1065}   | 0.7169            | 0.7852         | 0.7495     | 0.8101           |
+| 0.3056        | 11.0  | 110  | 0.6786          | {'precision': 0.707027027027027, 'recall': 0.8084054388133498, 'f1': 0.7543252595155711, 'number': 809}    | {'precision': 0.296, 'recall': 0.31092436974789917, 'f1': 0.30327868852459017, 'number': 119}               | {'precision': 0.7668393782383419, 'recall': 0.8338028169014085, 'f1': 0.7989203778677464, 'number': 1065}   | 0.7151            | 0.7923         | 0.7517     | 0.8087           |
+| 0.2977        | 12.0  | 120  | 0.6900          | {'precision': 0.7291196388261851, 'recall': 0.7985166872682324, 'f1': 0.7622418879056048, 'number': 809}   | {'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119}  | {'precision': 0.7726872246696035, 'recall': 0.8234741784037559, 'f1': 0.7972727272727272, 'number': 1065}   | 0.7274            | 0.7858         | 0.7554     | 0.8097           |
+| 0.2788        | 13.0  | 130  | 0.6937          | {'precision': 0.7224669603524229, 'recall': 0.8108776266996292, 'f1': 0.7641234711706465, 'number': 809}   | {'precision': 0.3023255813953488, 'recall': 0.3277310924369748, 'f1': 0.314516129032258, 'number': 119}     | {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}   | 0.7236            | 0.7883         | 0.7546     | 0.8099           |
+| 0.2593        | 14.0  | 140  | 0.6981          | {'precision': 0.7278835386338186, 'recall': 0.8034610630407911, 'f1': 0.7638072855464161, 'number': 809}   | {'precision': 0.29850746268656714, 'recall': 0.33613445378151263, 'f1': 0.31620553359683795, 'number': 119} | {'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065}   | 0.7242            | 0.7868         | 0.7542     | 0.8110           |
+| 0.2581        | 15.0  | 150  | 0.6985          | {'precision': 0.7292134831460674, 'recall': 0.8022249690976514, 'f1': 0.7639788110653325, 'number': 809}   | {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}  | {'precision': 0.7711267605633803, 'recall': 0.8225352112676056, 'f1': 0.7960018173557474, 'number': 1065}   | 0.7242            | 0.7852         | 0.7535     | 0.8108           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

logs/events.out.tfevents.1718873323.HCIDC-SV-DMZ-ORC-NODE02.3919717.0

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preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
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+    "resample",
+    "apply_ocr",
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+}

special_tokens_map.json

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+}

tokenizer_config.json

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+}