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-funsd2
+  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-funsd2
+
+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: 0.6965
+- Answer: {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809}
+- Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
+- Question: {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}
+- Overall Precision: 0.7162
+- Overall Recall: 0.7852
+- Overall F1: 0.7492
+- Overall Accuracy: 0.8006
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8343        | 1.0   | 10   | 1.5921          | {'precision': 0.006666666666666667, 'recall': 0.006180469715698393, 'f1': 0.006414368184733804, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.22067901234567902, 'recall': 0.13427230046948357, 'f1': 0.166958552247519, 'number': 1065} | 0.1059            | 0.0743         | 0.0873     | 0.3510           |
+| 1.4828        | 2.0   | 20   | 1.2849          | {'precision': 0.2738799661876585, 'recall': 0.4004944375772559, 'f1': 0.32530120481927705, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.38058114812189936, 'recall': 0.504225352112676, 'f1': 0.43376413570274636, 'number': 1065} | 0.3314            | 0.4320         | 0.3751     | 0.5951           |
+| 1.1444        | 3.0   | 30   | 0.9563          | {'precision': 0.4725897920604915, 'recall': 0.6180469715698393, 'f1': 0.5356186395286556, 'number': 809}       | {'precision': 0.041666666666666664, 'recall': 0.01680672268907563, 'f1': 0.02395209580838323, 'number': 119} | {'precision': 0.5378020265003897, 'recall': 0.647887323943662, 'f1': 0.5877342419080069, 'number': 1065}   | 0.4990            | 0.5981         | 0.5440     | 0.6955           |
+| 0.8658        | 4.0   | 40   | 0.7885          | {'precision': 0.5757009345794393, 'recall': 0.761433868974042, 'f1': 0.6556679084619478, 'number': 809}        | {'precision': 0.1388888888888889, 'recall': 0.08403361344537816, 'f1': 0.10471204188481677, 'number': 119}   | {'precision': 0.6567299006323396, 'recall': 0.6826291079812207, 'f1': 0.6694290976058932, 'number': 1065}  | 0.6016            | 0.6789         | 0.6379     | 0.7601           |
+| 0.6833        | 5.0   | 50   | 0.7124          | {'precision': 0.64375, 'recall': 0.7639060568603214, 'f1': 0.6986998304126625, 'number': 809}                  | {'precision': 0.35, 'recall': 0.23529411764705882, 'f1': 0.28140703517587945, 'number': 119}                 | {'precision': 0.6729354047424366, 'recall': 0.7727699530516432, 'f1': 0.7194055944055944, 'number': 1065}  | 0.6491            | 0.7371         | 0.6903     | 0.7810           |
+| 0.5898        | 6.0   | 60   | 0.6874          | {'precision': 0.6227141482194418, 'recall': 0.799752781211372, 'f1': 0.7002164502164502, 'number': 809}        | {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119}   | {'precision': 0.7236492471213464, 'recall': 0.7671361502347418, 'f1': 0.7447584320875114, 'number': 1065}  | 0.6627            | 0.7491         | 0.7033     | 0.7851           |
+| 0.5126        | 7.0   | 70   | 0.6599          | {'precision': 0.6705632306057385, 'recall': 0.7799752781211372, 'f1': 0.7211428571428572, 'number': 809}       | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119}   | {'precision': 0.7427821522309711, 'recall': 0.7971830985915493, 'f1': 0.7690217391304347, 'number': 1065}  | 0.6924            | 0.7592         | 0.7243     | 0.7963           |
+| 0.4534        | 8.0   | 80   | 0.6562          | {'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809}        | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}    | {'precision': 0.7476475620188195, 'recall': 0.8206572769953052, 'f1': 0.7824529991047449, 'number': 1065}  | 0.6910            | 0.7787         | 0.7322     | 0.7954           |
+| 0.3984        | 9.0   | 90   | 0.6561          | {'precision': 0.6838709677419355, 'recall': 0.7861557478368356, 'f1': 0.7314548591144335, 'number': 809}       | {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119}     | {'precision': 0.7555555555555555, 'recall': 0.8300469483568075, 'f1': 0.7910514541387024, 'number': 1065}  | 0.7047            | 0.7807         | 0.7408     | 0.7986           |
+| 0.3865        | 10.0  | 100  | 0.6673          | {'precision': 0.6877005347593583, 'recall': 0.7948084054388134, 'f1': 0.7373853211009175, 'number': 809}       | {'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119}   | {'precision': 0.7613240418118467, 'recall': 0.8206572769953052, 'f1': 0.7898779936737461, 'number': 1065}  | 0.7059            | 0.7802         | 0.7412     | 0.8019           |
+| 0.3343        | 11.0  | 110  | 0.6761          | {'precision': 0.6853220696937699, 'recall': 0.8022249690976514, 'f1': 0.7391799544419134, 'number': 809}       | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}    | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}  | 0.7110            | 0.7863         | 0.7467     | 0.7998           |
+| 0.314         | 12.0  | 120  | 0.6772          | {'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809}       | {'precision': 0.34545454545454546, 'recall': 0.31932773109243695, 'f1': 0.3318777292576419, 'number': 119}   | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065}  | 0.7184            | 0.7847         | 0.7501     | 0.8053           |
+| 0.3008        | 13.0  | 130  | 0.6878          | {'precision': 0.7048648648648649, 'recall': 0.8059332509270705, 'f1': 0.7520184544405998, 'number': 809}       | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119}   | {'precision': 0.7689594356261023, 'recall': 0.8187793427230047, 'f1': 0.793087767166894, 'number': 1065}   | 0.7186            | 0.7842         | 0.75       | 0.8033           |
+| 0.2797        | 14.0  | 140  | 0.6948          | {'precision': 0.7027322404371584, 'recall': 0.7948084054388134, 'f1': 0.7459396751740139, 'number': 809}       | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}  | {'precision': 0.7661996497373029, 'recall': 0.8215962441314554, 'f1': 0.7929315813321249, 'number': 1065}  | 0.7137            | 0.7817         | 0.7462     | 0.8017           |
+| 0.2722        | 15.0  | 150  | 0.6965          | {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809}        | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}   | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}  | 0.7162            | 0.7852         | 0.7492     | 0.8006           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

logs/events.out.tfevents.1718873965.HCIDC-SV-DMZ-ORC-NODE02.3913563.1

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preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
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+    "resample",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
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+}

special_tokens_map.json

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+}

tokenizer_config.json

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+}