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: 0.6650
+- Answer: {'precision': 0.7158712541620422, 'recall': 0.7972805933250927, 'f1': 0.7543859649122808, 'number': 809}
+- Header: {'precision': 0.2982456140350877, 'recall': 0.2857142857142857, 'f1': 0.2918454935622318, 'number': 119}
+- Question: {'precision': 0.7667238421955404, 'recall': 0.8394366197183099, 'f1': 0.8014343343792021, 'number': 1065}
+- Overall Precision: 0.7212
+- Overall Recall: 0.7893
+- Overall F1: 0.7537
+- Overall Accuracy: 0.8191
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7902        | 1.0   | 10   | 1.6058          | {'precision': 0.0174496644295302, 'recall': 0.016069221260815822, 'f1': 0.01673101673101673, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.24484848484848484, 'recall': 0.18967136150234742, 'f1': 0.21375661375661376, 'number': 1065} | 0.1369            | 0.1079         | 0.1207     | 0.3425           |
+| 1.4512        | 2.0   | 20   | 1.2477          | {'precision': 0.22826086956521738, 'recall': 0.23362175525339926, 'f1': 0.23091020158827122, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4611066559743384, 'recall': 0.539906103286385, 'f1': 0.49740484429065746, 'number': 1065}    | 0.3680            | 0.3833         | 0.3755     | 0.5802           |
+| 1.0772        | 3.0   | 30   | 0.9579          | {'precision': 0.47790055248618785, 'recall': 0.4276885043263288, 'f1': 0.45140247879973905, 'number': 809}  | {'precision': 0.05555555555555555, 'recall': 0.01680672268907563, 'f1': 0.025806451612903226, 'number': 119} | {'precision': 0.6270125223613596, 'recall': 0.6582159624413145, 'f1': 0.6422354557947779, 'number': 1065}    | 0.5586            | 0.5263         | 0.5420     | 0.6919           |
+| 0.8282        | 4.0   | 40   | 0.7735          | {'precision': 0.6132368148914168, 'recall': 0.7330037082818294, 'f1': 0.6677927927927928, 'number': 809}    | {'precision': 0.17647058823529413, 'recall': 0.10084033613445378, 'f1': 0.1283422459893048, 'number': 119}   | {'precision': 0.6726649528706083, 'recall': 0.7370892018779343, 'f1': 0.703405017921147, 'number': 1065}     | 0.6312            | 0.6974         | 0.6627     | 0.7621           |
+| 0.6763        | 5.0   | 50   | 0.7086          | {'precision': 0.6333333333333333, 'recall': 0.7515451174289246, 'f1': 0.6873940079140758, 'number': 809}    | {'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119}                 | {'precision': 0.6769731489015459, 'recall': 0.7812206572769953, 'f1': 0.7253705318221447, 'number': 1065}    | 0.6461            | 0.7356         | 0.6879     | 0.7869           |
+| 0.5577        | 6.0   | 60   | 0.6736          | {'precision': 0.6542155816435432, 'recall': 0.757725587144623, 'f1': 0.7021764032073311, 'number': 809}     | {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}    | {'precision': 0.6952822892498066, 'recall': 0.844131455399061, 'f1': 0.7625106022052586, 'number': 1065}     | 0.6657            | 0.7722         | 0.7150     | 0.7955           |
+| 0.4901        | 7.0   | 70   | 0.6510          | {'precision': 0.6706263498920086, 'recall': 0.7676143386897404, 'f1': 0.7158501440922191, 'number': 809}    | {'precision': 0.27927927927927926, 'recall': 0.2605042016806723, 'f1': 0.26956521739130435, 'number': 119}   | {'precision': 0.7412765957446809, 'recall': 0.8178403755868544, 'f1': 0.7776785714285714, 'number': 1065}    | 0.6885            | 0.7642         | 0.7244     | 0.7998           |
+| 0.4474        | 8.0   | 80   | 0.6389          | {'precision': 0.6828478964401294, 'recall': 0.7824474660074165, 'f1': 0.7292626728110598, 'number': 809}    | {'precision': 0.3137254901960784, 'recall': 0.2689075630252101, 'f1': 0.2895927601809955, 'number': 119}     | {'precision': 0.7523564695801199, 'recall': 0.8244131455399061, 'f1': 0.7867383512544801, 'number': 1065}    | 0.7026            | 0.7742         | 0.7367     | 0.8049           |
+| 0.4055        | 9.0   | 90   | 0.6371          | {'precision': 0.6855277475516867, 'recall': 0.7787391841779975, 'f1': 0.7291666666666666, 'number': 809}    | {'precision': 0.288135593220339, 'recall': 0.2857142857142857, 'f1': 0.2869198312236287, 'number': 119}      | {'precision': 0.7368852459016394, 'recall': 0.844131455399061, 'f1': 0.7868708971553611, 'number': 1065}     | 0.6925            | 0.7842         | 0.7355     | 0.8111           |
+| 0.3597        | 10.0  | 100  | 0.6547          | {'precision': 0.7027932960893855, 'recall': 0.7775030902348579, 'f1': 0.7382629107981221, 'number': 809}    | {'precision': 0.25925925925925924, 'recall': 0.29411764705882354, 'f1': 0.2755905511811024, 'number': 119}   | {'precision': 0.7463330457290768, 'recall': 0.812206572769953, 'f1': 0.7778776978417264, 'number': 1065}     | 0.6985            | 0.7672         | 0.7312     | 0.8070           |
+| 0.3295        | 11.0  | 110  | 0.6618          | {'precision': 0.709070796460177, 'recall': 0.792336217552534, 'f1': 0.7483946293053124, 'number': 809}      | {'precision': 0.3333333333333333, 'recall': 0.25210084033613445, 'f1': 0.28708133971291866, 'number': 119}   | {'precision': 0.7857142857142857, 'recall': 0.8366197183098592, 'f1': 0.8103683492496588, 'number': 1065}    | 0.7340            | 0.7837         | 0.7581     | 0.8106           |
+| 0.3169        | 12.0  | 120  | 0.6639          | {'precision': 0.7094972067039106, 'recall': 0.7849196538936959, 'f1': 0.7453051643192488, 'number': 809}    | {'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119}   | {'precision': 0.7582417582417582, 'recall': 0.8422535211267606, 'f1': 0.7980427046263344, 'number': 1065}    | 0.7142            | 0.7863         | 0.7485     | 0.8152           |
+| 0.2951        | 13.0  | 130  | 0.6653          | {'precision': 0.7094972067039106, 'recall': 0.7849196538936959, 'f1': 0.7453051643192488, 'number': 809}    | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}     | {'precision': 0.7784588441330998, 'recall': 0.8347417840375587, 'f1': 0.805618486633439, 'number': 1065}     | 0.7253            | 0.7817         | 0.7525     | 0.8167           |
+| 0.2872        | 14.0  | 140  | 0.6667          | {'precision': 0.7116022099447514, 'recall': 0.796044499381953, 'f1': 0.751458576429405, 'number': 809}      | {'precision': 0.2982456140350877, 'recall': 0.2857142857142857, 'f1': 0.2918454935622318, 'number': 119}     | {'precision': 0.7737162750217581, 'recall': 0.8347417840375587, 'f1': 0.803071364046974, 'number': 1065}     | 0.7228            | 0.7863         | 0.7532     | 0.8179           |
+| 0.2779        | 15.0  | 150  | 0.6650          | {'precision': 0.7158712541620422, 'recall': 0.7972805933250927, 'f1': 0.7543859649122808, 'number': 809}    | {'precision': 0.2982456140350877, 'recall': 0.2857142857142857, 'f1': 0.2918454935622318, 'number': 119}     | {'precision': 0.7667238421955404, 'recall': 0.8394366197183099, 'f1': 0.8014343343792021, 'number': 1065}    | 0.7212            | 0.7893         | 0.7537     | 0.8191           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.12.1
+- Datasets 2.8.0
+- Tokenizers 0.13.2

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