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: 1.0745
+- Answer: {'precision': 0.3554006968641115, 'recall': 0.5043263288009888, 'f1': 0.41696474195196725, 'number': 809}
+- Header: {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119}
+- Question: {'precision': 0.4910979228486647, 'recall': 0.6215962441314554, 'f1': 0.5486945710733527, 'number': 1065}
+- Overall Precision: 0.4258
+- Overall Recall: 0.5514
+- Overall F1: 0.4805
+- Overall Accuracy: 0.6117
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7729        | 1.0   | 10   | 1.5447          | {'precision': 0.04415584415584416, 'recall': 0.042027194066749075, 'f1': 0.04306523115896137, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.22091584158415842, 'recall': 0.3352112676056338, 'f1': 0.26631853785900783, 'number': 1065} | 0.1639            | 0.1962         | 0.1786     | 0.3568           |
+| 1.4627        | 2.0   | 20   | 1.3779          | {'precision': 0.12121212121212122, 'recall': 0.2373300370828183, 'f1': 0.16046803175929797, 'number': 809}   | {'precision': 0.04081632653061224, 'recall': 0.01680672268907563, 'f1': 0.023809523809523808, 'number': 119} | {'precision': 0.23460246360582307, 'recall': 0.39342723004694835, 'f1': 0.293931953700456, 'number': 1065}  | 0.1793            | 0.3076         | 0.2265     | 0.4108           |
+| 1.2914        | 3.0   | 30   | 1.2345          | {'precision': 0.15814696485623003, 'recall': 0.24474660074165636, 'f1': 0.19213973799126638, 'number': 809}  | {'precision': 0.15789473684210525, 'recall': 0.12605042016806722, 'f1': 0.14018691588785046, 'number': 119}  | {'precision': 0.31094527363184077, 'recall': 0.5868544600938967, 'f1': 0.4065040650406504, 'number': 1065}  | 0.2496            | 0.4205         | 0.3133     | 0.4524           |
+| 1.1698        | 4.0   | 40   | 1.1615          | {'precision': 0.2040990606319385, 'recall': 0.2954264524103832, 'f1': 0.2414141414141414, 'number': 809}     | {'precision': 0.19387755102040816, 'recall': 0.15966386554621848, 'f1': 0.17511520737327188, 'number': 119}  | {'precision': 0.351129363449692, 'recall': 0.6422535211267606, 'f1': 0.4540325257218719, 'number': 1065}    | 0.2928            | 0.4727         | 0.3616     | 0.4925           |
+| 1.096         | 5.0   | 50   | 1.1141          | {'precision': 0.22423802612481858, 'recall': 0.3819530284301607, 'f1': 0.28257887517146774, 'number': 809}   | {'precision': 0.2682926829268293, 'recall': 0.18487394957983194, 'f1': 0.21890547263681595, 'number': 119}   | {'precision': 0.3757159221076747, 'recall': 0.615962441314554, 'f1': 0.4667378157239417, 'number': 1065}    | 0.3079            | 0.4952         | 0.3797     | 0.5360           |
+| 1.0157        | 6.0   | 60   | 1.0480          | {'precision': 0.27807900852052675, 'recall': 0.4437577255871446, 'f1': 0.34190476190476193, 'number': 809}   | {'precision': 0.3013698630136986, 'recall': 0.18487394957983194, 'f1': 0.22916666666666669, 'number': 119}   | {'precision': 0.45481049562682213, 'recall': 0.5859154929577465, 'f1': 0.512105047189167, 'number': 1065}   | 0.3673            | 0.5043         | 0.4250     | 0.5881           |
+| 0.9412        | 7.0   | 70   | 1.0314          | {'precision': 0.29177057356608477, 'recall': 0.4338689740420272, 'f1': 0.34890656063618286, 'number': 809}   | {'precision': 0.2926829268292683, 'recall': 0.20168067226890757, 'f1': 0.23880597014925373, 'number': 119}   | {'precision': 0.45625451916124365, 'recall': 0.5924882629107981, 'f1': 0.5155228758169934, 'number': 1065}  | 0.3771            | 0.5048         | 0.4317     | 0.5961           |
+| 0.8828        | 8.0   | 80   | 1.0804          | {'precision': 0.3174061433447099, 'recall': 0.45982694684796044, 'f1': 0.37556789500252397, 'number': 809}   | {'precision': 0.2828282828282828, 'recall': 0.23529411764705882, 'f1': 0.25688073394495414, 'number': 119}   | {'precision': 0.46117804551539493, 'recall': 0.6469483568075117, 'f1': 0.5384915982805784, 'number': 1065}  | 0.3939            | 0.5464         | 0.4578     | 0.5872           |
+| 0.8304        | 9.0   | 90   | 1.0436          | {'precision': 0.3404255319148936, 'recall': 0.49443757725587145, 'f1': 0.40322580645161293, 'number': 809}   | {'precision': 0.36363636363636365, 'recall': 0.23529411764705882, 'f1': 0.2857142857142857, 'number': 119}   | {'precision': 0.4878765613519471, 'recall': 0.6234741784037559, 'f1': 0.5474031327287716, 'number': 1065}   | 0.4179            | 0.5479         | 0.4742     | 0.6095           |
+| 0.814         | 10.0  | 100  | 1.0871          | {'precision': 0.3464391691394659, 'recall': 0.5772558714462299, 'f1': 0.4330088085303662, 'number': 809}     | {'precision': 0.4166666666666667, 'recall': 0.25210084033613445, 'f1': 0.31413612565445026, 'number': 119}   | {'precision': 0.5084294587400178, 'recall': 0.5380281690140845, 'f1': 0.5228102189781022, 'number': 1065}   | 0.4201            | 0.5369         | 0.4714     | 0.5989           |
+| 0.7273        | 11.0  | 110  | 1.0650          | {'precision': 0.3483348334833483, 'recall': 0.4783683559950556, 'f1': 0.40312499999999996, 'number': 809}    | {'precision': 0.30434782608695654, 'recall': 0.23529411764705882, 'f1': 0.2654028436018957, 'number': 119}   | {'precision': 0.4900953778429934, 'recall': 0.6272300469483568, 'f1': 0.5502471169686985, 'number': 1065}   | 0.4221            | 0.5434         | 0.4751     | 0.6139           |
+| 0.7257        | 12.0  | 120  | 1.1221          | {'precision': 0.34212629896083135, 'recall': 0.5290482076637825, 'f1': 0.41553398058252433, 'number': 809}   | {'precision': 0.38666666666666666, 'recall': 0.24369747899159663, 'f1': 0.29896907216494845, 'number': 119}  | {'precision': 0.48787878787878786, 'recall': 0.6046948356807512, 'f1': 0.5400419287211741, 'number': 1065}  | 0.4161            | 0.5524         | 0.4747     | 0.6032           |
+| 0.694         | 13.0  | 130  | 1.0688          | {'precision': 0.3702451394759087, 'recall': 0.5414091470951793, 'f1': 0.43975903614457834, 'number': 809}    | {'precision': 0.345679012345679, 'recall': 0.23529411764705882, 'f1': 0.27999999999999997, 'number': 119}    | {'precision': 0.5052041633306645, 'recall': 0.5924882629107981, 'f1': 0.5453759723422645, 'number': 1065}   | 0.4365            | 0.5504         | 0.4869     | 0.6148           |
+| 0.6617        | 14.0  | 140  | 1.0465          | {'precision': 0.3598901098901099, 'recall': 0.4857849196538937, 'f1': 0.41346659652814305, 'number': 809}    | {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119}   | {'precision': 0.48916184971098264, 'recall': 0.6356807511737089, 'f1': 0.5528787260106166, 'number': 1065}  | 0.4291            | 0.5514         | 0.4827     | 0.6191           |
+| 0.6536        | 15.0  | 150  | 1.0745          | {'precision': 0.3554006968641115, 'recall': 0.5043263288009888, 'f1': 0.41696474195196725, 'number': 809}    | {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119}   | {'precision': 0.4910979228486647, 'recall': 0.6215962441314554, 'f1': 0.5486945710733527, 'number': 1065}   | 0.4258            | 0.5514         | 0.4805     | 0.6117           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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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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