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: 0.6930
+- Answer: {'precision': 0.705114254624592, 'recall': 0.8009888751545118, 'f1': 0.7499999999999999, 'number': 809}
+- Header: {'precision': 0.2642857142857143, 'recall': 0.31092436974789917, 'f1': 0.28571428571428575, 'number': 119}
+- Question: {'precision': 0.7760141093474426, 'recall': 0.8262910798122066, 'f1': 0.8003638017280582, 'number': 1065}
+- Overall Precision: 0.7136
+- Overall Recall: 0.7852
+- Overall F1: 0.7477
+- Overall Accuracy: 0.8082
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.812         | 1.0   | 10   | 1.5657          | {'precision': 0.026246719160104987, 'recall': 0.024721878862793572, 'f1': 0.02546148949713558, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.1956521739130435, 'recall': 0.1267605633802817, 'f1': 0.15384615384615385, 'number': 1065} | 0.1067            | 0.0778         | 0.0900     | 0.3859           |
+| 1.4244        | 2.0   | 20   | 1.2288          | {'precision': 0.14189189189189189, 'recall': 0.103831891223733, 'f1': 0.11991434689507495, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.42844202898550726, 'recall': 0.444131455399061, 'f1': 0.43614568925772246, 'number': 1065} | 0.3284            | 0.2795         | 0.3020     | 0.5784           |
+| 1.1038        | 3.0   | 30   | 0.9813          | {'precision': 0.4468629961587708, 'recall': 0.43139678615574784, 'f1': 0.4389937106918239, 'number': 809}     | {'precision': 0.03225806451612903, 'recall': 0.008403361344537815, 'f1': 0.013333333333333332, 'number': 119} | {'precision': 0.6163522012578616, 'recall': 0.644131455399061, 'f1': 0.6299357208448118, 'number': 1065}   | 0.5382            | 0.5198         | 0.5288     | 0.7101           |
+| 0.8512        | 4.0   | 40   | 0.8085          | {'precision': 0.5877192982456141, 'recall': 0.6625463535228677, 'f1': 0.6228936664729808, 'number': 809}      | {'precision': 0.109375, 'recall': 0.058823529411764705, 'f1': 0.07650273224043715, 'number': 119}             | {'precision': 0.6793760831889082, 'recall': 0.7361502347417841, 'f1': 0.7066246056782335, 'number': 1065}  | 0.6230            | 0.6658         | 0.6437     | 0.7566           |
+| 0.6646        | 5.0   | 50   | 0.7071          | {'precision': 0.6478723404255319, 'recall': 0.7527812113720643, 'f1': 0.6963979416809606, 'number': 809}      | {'precision': 0.21052631578947367, 'recall': 0.16806722689075632, 'f1': 0.1869158878504673, 'number': 119}    | {'precision': 0.6853658536585366, 'recall': 0.7915492957746478, 'f1': 0.7346405228758169, 'number': 1065}  | 0.6499            | 0.7386         | 0.6914     | 0.7871           |
+| 0.5615        | 6.0   | 60   | 0.6934          | {'precision': 0.6427840327533265, 'recall': 0.7762669962917181, 'f1': 0.7032474804031356, 'number': 809}      | {'precision': 0.2191780821917808, 'recall': 0.13445378151260504, 'f1': 0.16666666666666669, 'number': 119}    | {'precision': 0.7584973166368515, 'recall': 0.7962441314553991, 'f1': 0.7769125057260651, 'number': 1065}  | 0.6882            | 0.7486         | 0.7171     | 0.8008           |
+| 0.4852        | 7.0   | 70   | 0.6675          | {'precision': 0.6806451612903226, 'recall': 0.7824474660074165, 'f1': 0.7280046003450259, 'number': 809}      | {'precision': 0.2421875, 'recall': 0.2605042016806723, 'f1': 0.2510121457489879, 'number': 119}               | {'precision': 0.7596759675967597, 'recall': 0.7924882629107981, 'f1': 0.775735294117647, 'number': 1065}   | 0.6953            | 0.7566         | 0.7247     | 0.8098           |
+| 0.4261        | 8.0   | 80   | 0.6601          | {'precision': 0.6707818930041153, 'recall': 0.8059332509270705, 'f1': 0.7321729365524987, 'number': 809}      | {'precision': 0.23770491803278687, 'recall': 0.24369747899159663, 'f1': 0.24066390041493776, 'number': 119}   | {'precision': 0.7515257192676548, 'recall': 0.8093896713615023, 'f1': 0.779385171790235, 'number': 1065}   | 0.6885            | 0.7742         | 0.7289     | 0.8027           |
+| 0.3798        | 9.0   | 90   | 0.6595          | {'precision': 0.6950431034482759, 'recall': 0.7972805933250927, 'f1': 0.7426597582037997, 'number': 809}      | {'precision': 0.2727272727272727, 'recall': 0.2773109243697479, 'f1': 0.27499999999999997, 'number': 119}     | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065}  | 0.7108            | 0.7832         | 0.7453     | 0.8120           |
+| 0.366         | 10.0  | 100  | 0.6659          | {'precision': 0.6912393162393162, 'recall': 0.799752781211372, 'f1': 0.7415472779369628, 'number': 809}       | {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119}     | {'precision': 0.7822222222222223, 'recall': 0.8262910798122066, 'f1': 0.8036529680365297, 'number': 1065}  | 0.7170            | 0.7832         | 0.7487     | 0.8196           |
+| 0.3112        | 11.0  | 110  | 0.6790          | {'precision': 0.674562306900103, 'recall': 0.8096415327564895, 'f1': 0.7359550561797752, 'number': 809}       | {'precision': 0.2890625, 'recall': 0.31092436974789917, 'f1': 0.29959514170040485, 'number': 119}             | {'precision': 0.7867383512544803, 'recall': 0.8244131455399061, 'f1': 0.8051352590554791, 'number': 1065}  | 0.7088            | 0.7878         | 0.7462     | 0.8022           |
+| 0.3003        | 12.0  | 120  | 0.6876          | {'precision': 0.7192393736017897, 'recall': 0.7948084054388134, 'f1': 0.7551379917792131, 'number': 809}      | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119}    | {'precision': 0.7788546255506608, 'recall': 0.8300469483568075, 'f1': 0.8036363636363637, 'number': 1065}  | 0.7241            | 0.7847         | 0.7532     | 0.8069           |
+| 0.28          | 13.0  | 130  | 0.6905          | {'precision': 0.7013963480128894, 'recall': 0.8071693448702101, 'f1': 0.7505747126436783, 'number': 809}      | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119}     | {'precision': 0.7860340196956133, 'recall': 0.8244131455399061, 'f1': 0.8047662694775436, 'number': 1065}  | 0.7204            | 0.7873         | 0.7523     | 0.8104           |
+| 0.2654        | 14.0  | 140  | 0.6952          | {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809}       | {'precision': 0.2569444444444444, 'recall': 0.31092436974789917, 'f1': 0.28136882129277563, 'number': 119}    | {'precision': 0.7758164165931156, 'recall': 0.8253521126760563, 'f1': 0.7998180163785259, 'number': 1065}  | 0.7130            | 0.7827         | 0.7462     | 0.8068           |
+| 0.2629        | 15.0  | 150  | 0.6930          | {'precision': 0.705114254624592, 'recall': 0.8009888751545118, 'f1': 0.7499999999999999, 'number': 809}       | {'precision': 0.2642857142857143, 'recall': 0.31092436974789917, 'f1': 0.28571428571428575, 'number': 119}    | {'precision': 0.7760141093474426, 'recall': 0.8262910798122066, 'f1': 0.8003638017280582, 'number': 1065}  | 0.7136            | 0.7852         | 0.7477     | 0.8082           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu118
+- Datasets 2.19.2
+- Tokenizers 0.19.1

logs/events.out.tfevents.1717846109.Designori.20936.0

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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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+}