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-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: 0.7094
+- Answer: {'precision': 0.7131868131868132, 'recall': 0.8022249690976514, 'f1': 0.755090168702734, 'number': 809}
+- Header: {'precision': 0.3445378151260504, 'recall': 0.3445378151260504, 'f1': 0.3445378151260504, 'number': 119}
+- Question: {'precision': 0.7785651018600531, 'recall': 0.8253521126760563, 'f1': 0.8012762078395624, 'number': 1065}
+- Overall Precision: 0.7271
+- Overall Recall: 0.7873
+- Overall F1: 0.7560
+- Overall Accuracy: 0.8026
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7767        | 1.0   | 10   | 1.5683          | {'precision': 0.021764032073310423, 'recall': 0.023485784919653894, 'f1': 0.022592152199762183, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.214987714987715, 'recall': 0.1643192488262911, 'f1': 0.18626929217668972, 'number': 1065} | 0.1150            | 0.0973         | 0.1054     | 0.3768           |
+| 1.4234        | 2.0   | 20   | 1.2196          | {'precision': 0.1918194640338505, 'recall': 0.1681087762669963, 'f1': 0.17918313570487485, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4225037257824143, 'recall': 0.532394366197183, 'f1': 0.47112588284171164, 'number': 1065} | 0.3409            | 0.3527         | 0.3467     | 0.5773           |
+| 1.0839        | 3.0   | 30   | 0.9585          | {'precision': 0.4686390532544379, 'recall': 0.4894932014833127, 'f1': 0.4788391777509069, 'number': 809}       | {'precision': 0.13043478260869565, 'recall': 0.05042016806722689, 'f1': 0.07272727272727272, 'number': 119} | {'precision': 0.5354637568199533, 'recall': 0.6450704225352113, 'f1': 0.5851788756388415, 'number': 1065} | 0.5009            | 0.5464         | 0.5227     | 0.7008           |
+| 0.8429        | 4.0   | 40   | 0.8025          | {'precision': 0.6150583244962884, 'recall': 0.7169344870210136, 'f1': 0.6621004566210046, 'number': 809}       | {'precision': 0.32142857142857145, 'recall': 0.15126050420168066, 'f1': 0.20571428571428574, 'number': 119} | {'precision': 0.6584536958368734, 'recall': 0.7276995305164319, 'f1': 0.6913470115967887, 'number': 1065} | 0.6310            | 0.6889         | 0.6587     | 0.7534           |
+| 0.6591        | 5.0   | 50   | 0.7255          | {'precision': 0.6464208242950108, 'recall': 0.7367119901112484, 'f1': 0.6886192952050838, 'number': 809}       | {'precision': 0.25, 'recall': 0.19327731092436976, 'f1': 0.2180094786729858, 'number': 119}                 | {'precision': 0.6565891472868217, 'recall': 0.7953051643192488, 'f1': 0.7193205944798302, 'number': 1065} | 0.6363            | 0.7356         | 0.6823     | 0.7769           |
+| 0.5607        | 6.0   | 60   | 0.7110          | {'precision': 0.6417759838546923, 'recall': 0.7861557478368356, 'f1': 0.7066666666666668, 'number': 809}       | {'precision': 0.30337078651685395, 'recall': 0.226890756302521, 'f1': 0.2596153846153846, 'number': 119}    | {'precision': 0.7202432667245873, 'recall': 0.7784037558685446, 'f1': 0.7481949458483754, 'number': 1065} | 0.6688            | 0.7486         | 0.7064     | 0.7806           |
+| 0.483         | 7.0   | 70   | 0.6787          | {'precision': 0.6635120925341745, 'recall': 0.7799752781211372, 'f1': 0.7170454545454545, 'number': 809}       | {'precision': 0.2777777777777778, 'recall': 0.25210084033613445, 'f1': 0.2643171806167401, 'number': 119}   | {'precision': 0.7391688770999116, 'recall': 0.7849765258215963, 'f1': 0.761384335154827, 'number': 1065}  | 0.6836            | 0.7511         | 0.7158     | 0.7923           |
+| 0.4275        | 8.0   | 80   | 0.6793          | {'precision': 0.6615067079463365, 'recall': 0.792336217552534, 'f1': 0.7210348706411699, 'number': 809}        | {'precision': 0.29906542056074764, 'recall': 0.2689075630252101, 'f1': 0.28318584070796454, 'number': 119}  | {'precision': 0.7489139878366637, 'recall': 0.8093896713615023, 'f1': 0.7779783393501806, 'number': 1065} | 0.6893            | 0.7702         | 0.7275     | 0.7970           |
+| 0.3762        | 9.0   | 90   | 0.6784          | {'precision': 0.6949516648764769, 'recall': 0.799752781211372, 'f1': 0.7436781609195402, 'number': 809}        | {'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}    | {'precision': 0.7506493506493507, 'recall': 0.8140845070422535, 'f1': 0.781081081081081, 'number': 1065}  | 0.7049            | 0.7792         | 0.7402     | 0.8000           |
+| 0.3634        | 10.0  | 100  | 0.6793          | {'precision': 0.6964477933261571, 'recall': 0.799752781211372, 'f1': 0.7445339470655927, 'number': 809}        | {'precision': 0.375, 'recall': 0.3277310924369748, 'f1': 0.3497757847533633, 'number': 119}                 | {'precision': 0.7650655021834061, 'recall': 0.8225352112676056, 'f1': 0.7927601809954752, 'number': 1065} | 0.7172            | 0.7837         | 0.7490     | 0.8033           |
+| 0.3104        | 11.0  | 110  | 0.6977          | {'precision': 0.694327731092437, 'recall': 0.8170580964153276, 'f1': 0.750709823963657, 'number': 809}         | {'precision': 0.33613445378151263, 'recall': 0.33613445378151263, 'f1': 0.33613445378151263, 'number': 119} | {'precision': 0.7766903914590747, 'recall': 0.819718309859155, 'f1': 0.7976244860666972, 'number': 1065}  | 0.7171            | 0.7898         | 0.7517     | 0.8032           |
+| 0.2928        | 12.0  | 120  | 0.6987          | {'precision': 0.6931330472103004, 'recall': 0.7985166872682324, 'f1': 0.7421022400919012, 'number': 809}       | {'precision': 0.4, 'recall': 0.35294117647058826, 'f1': 0.37500000000000006, 'number': 119}                 | {'precision': 0.7809439002671416, 'recall': 0.8234741784037559, 'f1': 0.8016453382084096, 'number': 1065} | 0.7245            | 0.7852         | 0.7537     | 0.8028           |
+| 0.2766        | 13.0  | 130  | 0.7057          | {'precision': 0.6996770721205597, 'recall': 0.8034610630407911, 'f1': 0.7479861910241656, 'number': 809}       | {'precision': 0.3277310924369748, 'recall': 0.3277310924369748, 'f1': 0.3277310924369748, 'number': 119}    | {'precision': 0.7749338040600177, 'recall': 0.8244131455399061, 'f1': 0.7989080982711555, 'number': 1065} | 0.7185            | 0.7863         | 0.7508     | 0.8031           |
+| 0.2627        | 14.0  | 140  | 0.7089          | {'precision': 0.7063318777292577, 'recall': 0.799752781211372, 'f1': 0.750144927536232, 'number': 809}         | {'precision': 0.3652173913043478, 'recall': 0.35294117647058826, 'f1': 0.35897435897435903, 'number': 119}  | {'precision': 0.7798408488063661, 'recall': 0.828169014084507, 'f1': 0.8032786885245902, 'number': 1065}  | 0.7266            | 0.7883         | 0.7562     | 0.8012           |
+| 0.2561        | 15.0  | 150  | 0.7094          | {'precision': 0.7131868131868132, 'recall': 0.8022249690976514, 'f1': 0.755090168702734, 'number': 809}        | {'precision': 0.3445378151260504, 'recall': 0.3445378151260504, 'f1': 0.3445378151260504, 'number': 119}    | {'precision': 0.7785651018600531, 'recall': 0.8253521126760563, 'f1': 0.8012762078395624, 'number': 1065} | 0.7271            | 0.7873         | 0.7560     | 0.8026           |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1729599442.reiza-AERO-15-YD.2134860.0

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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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+}