End of training

README.md

@@ -0,0 +1,78 @@
+---
+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: 0.6909
+- Answer: {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809}
+- Header: {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}
+- Question: {'precision': 0.7631352282515074, 'recall': 0.831924882629108, 'f1': 0.7960467205750225, 'number': 1065}
+- Overall Precision: 0.7164
+- Overall Recall: 0.7923
+- Overall F1: 0.7524
+- Overall Accuracy: 0.8064
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7913        | 1.0   | 10   | 1.5806          | {'precision': 0.02405857740585774, 'recall': 0.02843016069221261, 'f1': 0.026062322946175637, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.17197452229299362, 'recall': 0.15211267605633802, 'f1': 0.16143497757847533, 'number': 1065} | 0.0975            | 0.0928         | 0.0951     | 0.3662           |
+| 1.4607        | 2.0   | 20   | 1.2580          | {'precision': 0.22879464285714285, 'recall': 0.25339925834363414, 'f1': 0.2404692082111437, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.41384499623777277, 'recall': 0.5164319248826291, 'f1': 0.4594820384294069, 'number': 1065}   | 0.3393            | 0.3788         | 0.3580     | 0.5702           |
+| 1.104         | 3.0   | 30   | 0.9936          | {'precision': 0.4552058111380145, 'recall': 0.4647713226205192, 'f1': 0.4599388379204893, 'number': 809}     | {'precision': 0.14705882352941177, 'recall': 0.04201680672268908, 'f1': 0.06535947712418301, 'number': 119} | {'precision': 0.5559471365638766, 'recall': 0.5924882629107981, 'f1': 0.5736363636363637, 'number': 1065}    | 0.5073            | 0.5078         | 0.5075     | 0.6862           |
+| 0.8426        | 4.0   | 40   | 0.8075          | {'precision': 0.5957918050941307, 'recall': 0.6650185414091471, 'f1': 0.6285046728971962, 'number': 809}     | {'precision': 0.3220338983050847, 'recall': 0.15966386554621848, 'f1': 0.21348314606741572, 'number': 119}  | {'precision': 0.6645739910313901, 'recall': 0.6957746478873239, 'f1': 0.6798165137614679, 'number': 1065}    | 0.6249            | 0.6513         | 0.6378     | 0.7554           |
+| 0.6743        | 5.0   | 50   | 0.7167          | {'precision': 0.6370370370370371, 'recall': 0.7441285537700866, 'f1': 0.6864310148232612, 'number': 809}     | {'precision': 0.35365853658536583, 'recall': 0.24369747899159663, 'f1': 0.2885572139303482, 'number': 119}  | {'precision': 0.6849192100538599, 'recall': 0.7164319248826291, 'f1': 0.700321248279027, 'number': 1065}     | 0.6511            | 0.6994         | 0.6744     | 0.7781           |
+| 0.5571        | 6.0   | 60   | 0.6785          | {'precision': 0.6492146596858639, 'recall': 0.7663782447466008, 'f1': 0.7029478458049887, 'number': 809}     | {'precision': 0.36585365853658536, 'recall': 0.25210084033613445, 'f1': 0.29850746268656714, 'number': 119} | {'precision': 0.6846275752773375, 'recall': 0.8112676056338028, 'f1': 0.742587021916631, 'number': 1065}     | 0.6585            | 0.7597         | 0.7055     | 0.7929           |
+| 0.4858        | 7.0   | 70   | 0.6678          | {'precision': 0.6611740473738414, 'recall': 0.7935723114956736, 'f1': 0.7213483146067416, 'number': 809}     | {'precision': 0.39080459770114945, 'recall': 0.2857142857142857, 'f1': 0.33009708737864074, 'number': 119}  | {'precision': 0.7212543554006968, 'recall': 0.7774647887323943, 'f1': 0.7483054676909172, 'number': 1065}    | 0.6818            | 0.7546         | 0.7164     | 0.7961           |
+| 0.4397        | 8.0   | 80   | 0.6626          | {'precision': 0.6826608505997819, 'recall': 0.7737948084054388, 'f1': 0.7253765932792584, 'number': 809}     | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119}  | {'precision': 0.742437337942956, 'recall': 0.8065727699530516, 'f1': 0.7731773177317731, 'number': 1065}     | 0.6979            | 0.7617         | 0.7284     | 0.8015           |
+| 0.393         | 9.0   | 90   | 0.6611          | {'precision': 0.6856223175965666, 'recall': 0.7898640296662547, 'f1': 0.7340608845491098, 'number': 809}     | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119}  | {'precision': 0.7425658453695837, 'recall': 0.8206572769953052, 'f1': 0.7796610169491525, 'number': 1065}    | 0.6954            | 0.7777         | 0.7342     | 0.8020           |
+| 0.351         | 10.0  | 100  | 0.6665          | {'precision': 0.6994535519125683, 'recall': 0.7911001236093943, 'f1': 0.7424593967517401, 'number': 809}     | {'precision': 0.33043478260869563, 'recall': 0.31932773109243695, 'f1': 0.32478632478632474, 'number': 119} | {'precision': 0.7415254237288136, 'recall': 0.8215962441314554, 'f1': 0.7795100222717148, 'number': 1065}    | 0.7027            | 0.7792         | 0.7390     | 0.8054           |
+| 0.3187        | 11.0  | 110  | 0.6752          | {'precision': 0.6963123644251626, 'recall': 0.7935723114956736, 'f1': 0.7417677642980935, 'number': 809}     | {'precision': 0.3275862068965517, 'recall': 0.31932773109243695, 'f1': 0.3234042553191489, 'number': 119}   | {'precision': 0.7708516242317822, 'recall': 0.8244131455399061, 'f1': 0.7967332123411976, 'number': 1065}    | 0.7157            | 0.7817         | 0.7472     | 0.8076           |
+| 0.3034        | 12.0  | 120  | 0.6826          | {'precision': 0.6970998925886144, 'recall': 0.8022249690976514, 'f1': 0.7459770114942528, 'number': 809}     | {'precision': 0.3486238532110092, 'recall': 0.31932773109243695, 'f1': 0.3333333333333333, 'number': 119}   | {'precision': 0.7675814751286449, 'recall': 0.8403755868544601, 'f1': 0.8023307933662035, 'number': 1065}    | 0.7171            | 0.7938         | 0.7535     | 0.8080           |
+| 0.2825        | 13.0  | 130  | 0.6909          | {'precision': 0.6901408450704225, 'recall': 0.7873918417799752, 'f1': 0.7355658198614318, 'number': 809}     | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119}    | {'precision': 0.7626086956521739, 'recall': 0.8234741784037559, 'f1': 0.7918735891647856, 'number': 1065}    | 0.7068            | 0.7802         | 0.7417     | 0.8055           |
+| 0.2745        | 14.0  | 140  | 0.6884          | {'precision': 0.7039827771797632, 'recall': 0.8084054388133498, 'f1': 0.7525891829689298, 'number': 809}     | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119}  | {'precision': 0.7651122625215889, 'recall': 0.831924882629108, 'f1': 0.7971210076473234, 'number': 1065}     | 0.7167            | 0.7923         | 0.7526     | 0.8070           |
+| 0.2711        | 15.0  | 150  | 0.6909          | {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809}     | {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}   | {'precision': 0.7631352282515074, 'recall': 0.831924882629108, 'f1': 0.7960467205750225, 'number': 1065}     | 0.7164            | 0.7923         | 0.7524     | 0.8064           |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.12.1
+- Datasets 2.9.0
+- Tokenizers 0.13.2

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