End of training

README.md

@@ -0,0 +1,78 @@
+---
+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: 0.7037
+- Answer: {'precision': 0.7206703910614525, 'recall': 0.7972805933250927, 'f1': 0.7570422535211268, 'number': 809}
+- Header: {'precision': 0.3006993006993007, 'recall': 0.36134453781512604, 'f1': 0.3282442748091603, 'number': 119}
+- Question: {'precision': 0.7585004359197908, 'recall': 0.8169014084507042, 'f1': 0.7866184448462928, 'number': 1065}
+- Overall Precision: 0.7130
+- Overall Recall: 0.7817
+- Overall F1: 0.7458
+- Overall Accuracy: 0.7989
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7815        | 1.0   | 10   | 1.5703          | {'precision': 0.022222222222222223, 'recall': 0.022249690976514216, 'f1': 0.022235948116121063, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.21046643913538113, 'recall': 0.17370892018779344, 'f1': 0.19032921810699588, 'number': 1065} | 0.1202            | 0.1019         | 0.1103     | 0.3789           |
+| 1.4352        | 2.0   | 20   | 1.2331          | {'precision': 0.12166172106824925, 'recall': 0.10135970333745364, 'f1': 0.11058664868509778, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4863247863247863, 'recall': 0.5342723004694836, 'f1': 0.50917225950783, 'number': 1065}      | 0.3530            | 0.3266         | 0.3393     | 0.5662           |
+| 1.0804        | 3.0   | 30   | 0.9725          | {'precision': 0.4528985507246377, 'recall': 0.4635352286773795, 'f1': 0.4581551618814906, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6272806255430061, 'recall': 0.6779342723004694, 'f1': 0.6516245487364621, 'number': 1065}    | 0.5447            | 0.5504         | 0.5475     | 0.6845           |
+| 0.8495        | 4.0   | 40   | 0.7990          | {'precision': 0.5910973084886129, 'recall': 0.7058096415327565, 'f1': 0.6433802816901407, 'number': 809}       | {'precision': 0.05970149253731343, 'recall': 0.03361344537815126, 'f1': 0.04301075268817204, 'number': 119} | {'precision': 0.6702222222222223, 'recall': 0.707981220657277, 'f1': 0.6885844748858447, 'number': 1065}     | 0.6158            | 0.6668         | 0.6403     | 0.7510           |
+| 0.6866        | 5.0   | 50   | 0.7357          | {'precision': 0.6541436464088398, 'recall': 0.7317676143386898, 'f1': 0.6907817969661612, 'number': 809}       | {'precision': 0.2235294117647059, 'recall': 0.15966386554621848, 'f1': 0.18627450980392157, 'number': 119}  | {'precision': 0.7028619528619529, 'recall': 0.784037558685446, 'f1': 0.7412339103417664, 'number': 1065}     | 0.6639            | 0.7255         | 0.6934     | 0.7698           |
+| 0.5626        | 6.0   | 60   | 0.6982          | {'precision': 0.6594871794871795, 'recall': 0.7948084054388134, 'f1': 0.7208520179372198, 'number': 809}       | {'precision': 0.28378378378378377, 'recall': 0.17647058823529413, 'f1': 0.21761658031088082, 'number': 119} | {'precision': 0.6939417781274587, 'recall': 0.828169014084507, 'f1': 0.7551369863013697, 'number': 1065}     | 0.6664            | 0.7757         | 0.7169     | 0.7872           |
+| 0.4875        | 7.0   | 70   | 0.6710          | {'precision': 0.6905286343612335, 'recall': 0.7750309023485785, 'f1': 0.7303436225975539, 'number': 809}       | {'precision': 0.2336448598130841, 'recall': 0.21008403361344538, 'f1': 0.22123893805309733, 'number': 119}  | {'precision': 0.7287145242070117, 'recall': 0.819718309859155, 'f1': 0.7715422006186478, 'number': 1065}     | 0.6891            | 0.7652         | 0.7252     | 0.7924           |
+| 0.4499        | 8.0   | 80   | 0.6635          | {'precision': 0.6888412017167382, 'recall': 0.7935723114956736, 'f1': 0.7375071797817346, 'number': 809}       | {'precision': 0.25210084033613445, 'recall': 0.25210084033613445, 'f1': 0.25210084033613445, 'number': 119} | {'precision': 0.7314814814814815, 'recall': 0.815962441314554, 'f1': 0.771415889924545, 'number': 1065}      | 0.6883            | 0.7732         | 0.7283     | 0.7977           |
+| 0.3939        | 9.0   | 90   | 0.6686          | {'precision': 0.709070796460177, 'recall': 0.792336217552534, 'f1': 0.7483946293053124, 'number': 809}         | {'precision': 0.24817518248175183, 'recall': 0.2857142857142857, 'f1': 0.265625, 'number': 119}             | {'precision': 0.7311557788944724, 'recall': 0.819718309859155, 'f1': 0.7729083665338645, 'number': 1065}     | 0.6926            | 0.7767         | 0.7323     | 0.7970           |
+| 0.3522        | 10.0  | 100  | 0.6728          | {'precision': 0.7094668117519043, 'recall': 0.8059332509270705, 'f1': 0.7546296296296295, 'number': 809}       | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}  | {'precision': 0.7573149741824441, 'recall': 0.8262910798122066, 'f1': 0.7903008531656939, 'number': 1065}    | 0.7135            | 0.7873         | 0.7486     | 0.8034           |
+| 0.3124        | 11.0  | 110  | 0.6859          | {'precision': 0.7041800643086816, 'recall': 0.8121137206427689, 'f1': 0.7543053960964409, 'number': 809}       | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119}   | {'precision': 0.7731316725978647, 'recall': 0.815962441314554, 'f1': 0.793969849246231, 'number': 1065}      | 0.7185            | 0.7837         | 0.7497     | 0.8006           |
+| 0.306         | 12.0  | 120  | 0.6947          | {'precision': 0.720489977728285, 'recall': 0.799752781211372, 'f1': 0.7580550673696543, 'number': 809}         | {'precision': 0.2773722627737226, 'recall': 0.31932773109243695, 'f1': 0.296875, 'number': 119}             | {'precision': 0.7567332754126846, 'recall': 0.8178403755868544, 'f1': 0.7861010830324908, 'number': 1065}    | 0.7118            | 0.7807         | 0.7447     | 0.7987           |
+| 0.283         | 13.0  | 130  | 0.6948          | {'precision': 0.7201783723522854, 'recall': 0.7985166872682324, 'f1': 0.7573270808909731, 'number': 809}       | {'precision': 0.30597014925373134, 'recall': 0.3445378151260504, 'f1': 0.3241106719367589, 'number': 119}   | {'precision': 0.7585004359197908, 'recall': 0.8169014084507042, 'f1': 0.7866184448462928, 'number': 1065}    | 0.7149            | 0.7812         | 0.7466     | 0.8000           |
+| 0.2726        | 14.0  | 140  | 0.7002          | {'precision': 0.7119205298013245, 'recall': 0.7972805933250927, 'f1': 0.7521865889212828, 'number': 809}       | {'precision': 0.3049645390070922, 'recall': 0.36134453781512604, 'f1': 0.3307692307692308, 'number': 119}   | {'precision': 0.762532981530343, 'recall': 0.8140845070422535, 'f1': 0.787465940054496, 'number': 1065}      | 0.7120            | 0.7802         | 0.7446     | 0.8001           |
+| 0.264         | 15.0  | 150  | 0.7037          | {'precision': 0.7206703910614525, 'recall': 0.7972805933250927, 'f1': 0.7570422535211268, 'number': 809}       | {'precision': 0.3006993006993007, 'recall': 0.36134453781512604, 'f1': 0.3282442748091603, 'number': 119}   | {'precision': 0.7585004359197908, 'recall': 0.8169014084507042, 'f1': 0.7866184448462928, 'number': 1065}    | 0.7130            | 0.7817         | 0.7458     | 0.7989           |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2

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