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: 0.7279
+- Answer: {'precision': 0.706858407079646, 'recall': 0.7898640296662547, 'f1': 0.7460595446584939, 'number': 809}
+- Header: {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}
+- Question: {'precision': 0.7741935483870968, 'recall': 0.8338028169014085, 'f1': 0.8028933092224232, 'number': 1065}
+- Overall Precision: 0.7197
+- Overall Recall: 0.7858
+- Overall F1: 0.7513
+- Overall Accuracy: 0.8034
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7555        | 1.0   | 10   | 1.5320          | {'precision': 0.02317596566523605, 'recall': 0.03337453646477132, 'f1': 0.02735562310030395, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.11706783369803063, 'recall': 0.10046948356807511, 'f1': 0.10813542193026779, 'number': 1065} | 0.0645            | 0.0672         | 0.0658     | 0.3975           |
+| 1.3972        | 2.0   | 20   | 1.1941          | {'precision': 0.2606060606060606, 'recall': 0.2657601977750309, 'f1': 0.2631578947368421, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4398805078416729, 'recall': 0.5530516431924882, 'f1': 0.49001663893510816, 'number': 1065}   | 0.3715            | 0.4034         | 0.3868     | 0.6107           |
+| 1.0502        | 3.0   | 30   | 0.9315          | {'precision': 0.5343347639484979, 'recall': 0.61557478368356, 'f1': 0.5720850086157381, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5706304868316041, 'recall': 0.6713615023474179, 'f1': 0.6169111302847283, 'number': 1065}    | 0.5484            | 0.6086         | 0.5769     | 0.7259           |
+| 0.8107        | 4.0   | 40   | 0.7975          | {'precision': 0.6146288209606987, 'recall': 0.695920889987639, 'f1': 0.6527536231884058, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6344605475040258, 'recall': 0.739906103286385, 'f1': 0.683138274815778, 'number': 1065}      | 0.6124            | 0.6779         | 0.6435     | 0.7593           |
+| 0.6553        | 5.0   | 50   | 0.7487          | {'precision': 0.6507760532150776, 'recall': 0.7255871446229913, 'f1': 0.6861484511981296, 'number': 809}    | {'precision': 0.12, 'recall': 0.07563025210084033, 'f1': 0.09278350515463916, 'number': 119}                | {'precision': 0.6690085870413739, 'recall': 0.8046948356807512, 'f1': 0.7306052855924979, 'number': 1065}    | 0.6435            | 0.7291         | 0.6836     | 0.7719           |
+| 0.5642        | 6.0   | 60   | 0.7147          | {'precision': 0.6557203389830508, 'recall': 0.765142150803461, 'f1': 0.7062179121505989, 'number': 809}     | {'precision': 0.20833333333333334, 'recall': 0.12605042016806722, 'f1': 0.15706806282722513, 'number': 119} | {'precision': 0.7058333333333333, 'recall': 0.7953051643192488, 'f1': 0.7479028697571745, 'number': 1065}    | 0.6683            | 0.7431         | 0.7037     | 0.7847           |
+| 0.4833        | 7.0   | 70   | 0.6895          | {'precision': 0.6919691969196919, 'recall': 0.7775030902348579, 'f1': 0.7322467986030267, 'number': 809}    | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}  | {'precision': 0.7268041237113402, 'recall': 0.7943661971830986, 'f1': 0.7590847913862719, 'number': 1065}    | 0.6904            | 0.7531         | 0.7204     | 0.7920           |
+| 0.4356        | 8.0   | 80   | 0.6896          | {'precision': 0.6869109947643979, 'recall': 0.8108776266996292, 'f1': 0.7437641723356009, 'number': 809}    | {'precision': 0.27884615384615385, 'recall': 0.24369747899159663, 'f1': 0.2600896860986547, 'number': 119}  | {'precision': 0.730185497470489, 'recall': 0.8131455399061033, 'f1': 0.7694358063083073, 'number': 1065}     | 0.6909            | 0.7782         | 0.7319     | 0.7919           |
+| 0.3874        | 9.0   | 90   | 0.7000          | {'precision': 0.7158836689038032, 'recall': 0.7911001236093943, 'f1': 0.7516147974163241, 'number': 809}    | {'precision': 0.2818181818181818, 'recall': 0.2605042016806723, 'f1': 0.27074235807860264, 'number': 119}   | {'precision': 0.7395388556789069, 'recall': 0.8131455399061033, 'f1': 0.7745974955277279, 'number': 1065}    | 0.7067            | 0.7712         | 0.7375     | 0.7957           |
+| 0.3771        | 10.0  | 100  | 0.7200          | {'precision': 0.7097142857142857, 'recall': 0.7676143386897404, 'f1': 0.7375296912114014, 'number': 809}    | {'precision': 0.272, 'recall': 0.2857142857142857, 'f1': 0.27868852459016397, 'number': 119}                | {'precision': 0.7438715131022823, 'recall': 0.8262910798122066, 'f1': 0.7829181494661922, 'number': 1065}    | 0.7032            | 0.7702         | 0.7352     | 0.7886           |
+| 0.3231        | 11.0  | 110  | 0.7101          | {'precision': 0.7115177610333692, 'recall': 0.8170580964153276, 'f1': 0.760644418872267, 'number': 809}     | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119}  | {'precision': 0.7672188317349607, 'recall': 0.8262910798122066, 'f1': 0.7956600361663653, 'number': 1065}    | 0.7163            | 0.7918         | 0.7521     | 0.8000           |
+| 0.3026        | 12.0  | 120  | 0.7186          | {'precision': 0.7138009049773756, 'recall': 0.7799752781211372, 'f1': 0.7454223272297696, 'number': 809}    | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}                 | {'precision': 0.7658833768494343, 'recall': 0.8262910798122066, 'f1': 0.7949412827461607, 'number': 1065}    | 0.7199            | 0.7777         | 0.7477     | 0.7984           |
+| 0.2876        | 13.0  | 130  | 0.7213          | {'precision': 0.7156862745098039, 'recall': 0.8121137206427689, 'f1': 0.7608569774174871, 'number': 809}    | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}                 | {'precision': 0.7737676056338029, 'recall': 0.8253521126760563, 'f1': 0.7987278509768287, 'number': 1065}    | 0.7245            | 0.7903         | 0.7559     | 0.8025           |
+| 0.2709        | 14.0  | 140  | 0.7269          | {'precision': 0.7147613762486127, 'recall': 0.796044499381953, 'f1': 0.7532163742690058, 'number': 809}     | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}   | {'precision': 0.7709059233449478, 'recall': 0.8309859154929577, 'f1': 0.7998192498870312, 'number': 1065}    | 0.7205            | 0.7863         | 0.7519     | 0.8033           |
+| 0.2745        | 15.0  | 150  | 0.7279          | {'precision': 0.706858407079646, 'recall': 0.7898640296662547, 'f1': 0.7460595446584939, 'number': 809}     | {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}                | {'precision': 0.7741935483870968, 'recall': 0.8338028169014085, 'f1': 0.8028933092224232, 'number': 1065}    | 0.7197            | 0.7858         | 0.7513     | 0.8034           |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1724764274.2ab8e5e967d3.332.0

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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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+}