End of training

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-finetuned-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-finetuned-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: 0.6806
+- Answer: {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809}
+- Header: {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119}
+- Question: {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065}
+- Overall Precision: 0.7305
+- Overall Recall: 0.7943
+- Overall F1: 0.7611
+- Overall Accuracy: 0.8085
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8023        | 1.0   | 10   | 1.6152          | {'precision': 0.009448818897637795, 'recall': 0.007416563658838072, 'f1': 0.008310249307479225, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.12998266897746968, 'recall': 0.07042253521126761, 'f1': 0.09135200974421437, 'number': 1065} | 0.0668            | 0.0406         | 0.0505     | 0.3276           |
+| 1.4614        | 2.0   | 20   | 1.2547          | {'precision': 0.2211253701875617, 'recall': 0.276885043263288, 'f1': 0.24588364434687154, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.43353028064992616, 'recall': 0.5511737089201878, 'f1': 0.4853245142620918, 'number': 1065}   | 0.3420            | 0.4069         | 0.3717     | 0.5926           |
+| 1.0795        | 3.0   | 30   | 0.9150          | {'precision': 0.49837486457204766, 'recall': 0.5686032138442522, 'f1': 0.5311778290993071, 'number': 809}      | {'precision': 0.04081632653061224, 'recall': 0.01680672268907563, 'f1': 0.023809523809523808, 'number': 119} | {'precision': 0.5953338696701529, 'recall': 0.6948356807511737, 'f1': 0.6412478336221837, 'number': 1065}    | 0.5427            | 0.6031         | 0.5713     | 0.7145           |
+| 0.8025        | 4.0   | 40   | 0.7686          | {'precision': 0.6056622851365016, 'recall': 0.7404202719406675, 'f1': 0.6662958843159066, 'number': 809}       | {'precision': 0.10975609756097561, 'recall': 0.07563025210084033, 'f1': 0.08955223880597014, 'number': 119}  | {'precision': 0.6737400530503979, 'recall': 0.7154929577464789, 'f1': 0.6939890710382515, 'number': 1065}    | 0.6222            | 0.6874         | 0.6532     | 0.7506           |
+| 0.6638        | 5.0   | 50   | 0.7034          | {'precision': 0.644535240040858, 'recall': 0.7799752781211372, 'f1': 0.7058165548098434, 'number': 809}        | {'precision': 0.23711340206185566, 'recall': 0.19327731092436976, 'f1': 0.21296296296296294, 'number': 119}  | {'precision': 0.7158992180712423, 'recall': 0.7737089201877935, 'f1': 0.7436823104693141, 'number': 1065}    | 0.6637            | 0.7416         | 0.7005     | 0.7841           |
+| 0.5567        | 6.0   | 60   | 0.6784          | {'precision': 0.6687435098650052, 'recall': 0.796044499381953, 'f1': 0.7268623024830699, 'number': 809}        | {'precision': 0.2857142857142857, 'recall': 0.2184873949579832, 'f1': 0.24761904761904763, 'number': 119}    | {'precision': 0.7160392798690671, 'recall': 0.8215962441314554, 'f1': 0.765194578049847, 'number': 1065}     | 0.6788            | 0.7752         | 0.7238     | 0.7903           |
+| 0.4925        | 7.0   | 70   | 0.6815          | {'precision': 0.6839779005524862, 'recall': 0.765142150803461, 'f1': 0.7222870478413069, 'number': 809}        | {'precision': 0.2894736842105263, 'recall': 0.2773109243697479, 'f1': 0.2832618025751073, 'number': 119}     | {'precision': 0.7233169129720853, 'recall': 0.8272300469483568, 'f1': 0.7717915024091108, 'number': 1065}    | 0.6853            | 0.7692         | 0.7248     | 0.7913           |
+| 0.4494        | 8.0   | 80   | 0.6765          | {'precision': 0.6962305986696231, 'recall': 0.7762669962917181, 'f1': 0.734073641145529, 'number': 809}        | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119}                 | {'precision': 0.7360066833751044, 'recall': 0.8272300469483568, 'f1': 0.7789566755083996, 'number': 1065}    | 0.6942            | 0.7747         | 0.7323     | 0.8004           |
+| 0.3986        | 9.0   | 90   | 0.6587          | {'precision': 0.7077777777777777, 'recall': 0.7873918417799752, 'f1': 0.7454651843183148, 'number': 809}       | {'precision': 0.3274336283185841, 'recall': 0.31092436974789917, 'f1': 0.3189655172413793, 'number': 119}    | {'precision': 0.7487266553480475, 'recall': 0.828169014084507, 'f1': 0.7864467231386535, 'number': 1065}     | 0.7102            | 0.7807         | 0.7438     | 0.8019           |
+| 0.3597        | 10.0  | 100  | 0.6607          | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809}       | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119}    | {'precision': 0.7600685518423308, 'recall': 0.8328638497652582, 'f1': 0.7948028673835125, 'number': 1065}    | 0.7144            | 0.7868         | 0.7488     | 0.8048           |
+| 0.3266        | 11.0  | 110  | 0.6751          | {'precision': 0.7050279329608938, 'recall': 0.7799752781211372, 'f1': 0.7406103286384977, 'number': 809}       | {'precision': 0.3684210526315789, 'recall': 0.35294117647058826, 'f1': 0.3605150214592275, 'number': 119}    | {'precision': 0.7711571675302246, 'recall': 0.8384976525821596, 'f1': 0.8034188034188035, 'number': 1065}    | 0.7227            | 0.7858         | 0.7529     | 0.8056           |
+| 0.3103        | 12.0  | 120  | 0.6799          | {'precision': 0.7047413793103449, 'recall': 0.8084054388133498, 'f1': 0.7530224525043179, 'number': 809}       | {'precision': 0.3474576271186441, 'recall': 0.3445378151260504, 'f1': 0.3459915611814346, 'number': 119}     | {'precision': 0.7799295774647887, 'recall': 0.831924882629108, 'f1': 0.8050885960926851, 'number': 1065}     | 0.7246            | 0.7933         | 0.7574     | 0.8034           |
+| 0.2893        | 13.0  | 130  | 0.6773          | {'precision': 0.7191392978482446, 'recall': 0.7849196538936959, 'f1': 0.7505910165484633, 'number': 809}       | {'precision': 0.35833333333333334, 'recall': 0.36134453781512604, 'f1': 0.35983263598326365, 'number': 119}  | {'precision': 0.7861524978089395, 'recall': 0.8422535211267606, 'f1': 0.8132366273798729, 'number': 1065}    | 0.7346            | 0.7903         | 0.7614     | 0.8050           |
+| 0.2743        | 14.0  | 140  | 0.6788          | {'precision': 0.7063318777292577, 'recall': 0.799752781211372, 'f1': 0.750144927536232, 'number': 809}         | {'precision': 0.37168141592920356, 'recall': 0.35294117647058826, 'f1': 0.36206896551724144, 'number': 119}  | {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065}     | 0.7316            | 0.7933         | 0.7612     | 0.8085           |
+| 0.2756        | 15.0  | 150  | 0.6806          | {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809}        | {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119}   | {'precision': 0.7866549604916594, 'recall': 0.8413145539906103, 'f1': 0.8130671506352087, 'number': 1065}    | 0.7305            | 0.7943         | 0.7611     | 0.8085           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2

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