End of training

README.md

@@ -0,0 +1,79 @@
+---
+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: 1.0045
+- Answer: {'precision': 0.7348314606741573, 'recall': 0.8084054388133498, 'f1': 0.7698646262507357, 'number': 809}
+- Header: {'precision': 0.44285714285714284, 'recall': 0.5210084033613446, 'f1': 0.47876447876447875, 'number': 119}
+- Question: {'precision': 0.8211009174311926, 'recall': 0.8403755868544601, 'f1': 0.8306264501160092, 'number': 1065}
+- Overall Precision: 0.7599
+- Overall Recall: 0.8083
+- Overall F1: 0.7834
+- Overall Accuracy: 0.8106
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1724        | 1.0   | 10   | 0.7657          | {'precision': 0.7097826086956521, 'recall': 0.8071693448702101, 'f1': 0.7553499132446501, 'number': 809} | {'precision': 0.3893129770992366, 'recall': 0.42857142857142855, 'f1': 0.40800000000000003, 'number': 119} | {'precision': 0.7941176470588235, 'recall': 0.8366197183098592, 'f1': 0.8148148148148148, 'number': 1065} | 0.7340            | 0.8003         | 0.7657     | 0.8134           |
+| 0.1451        | 2.0   | 20   | 0.8099          | {'precision': 0.7136659436008677, 'recall': 0.8133498145859085, 'f1': 0.7602541883304449, 'number': 809} | {'precision': 0.4215686274509804, 'recall': 0.36134453781512604, 'f1': 0.3891402714932127, 'number': 119}  | {'precision': 0.809437386569873, 'recall': 0.8375586854460094, 'f1': 0.823257960313798, 'number': 1065}   | 0.7493            | 0.7993         | 0.7735     | 0.8125           |
+| 0.1179        | 3.0   | 30   | 0.8622          | {'precision': 0.7099892588614393, 'recall': 0.8170580964153276, 'f1': 0.7597701149425288, 'number': 809} | {'precision': 0.4074074074074074, 'recall': 0.46218487394957986, 'f1': 0.4330708661417323, 'number': 119}  | {'precision': 0.8123300090661831, 'recall': 0.8413145539906103, 'f1': 0.8265682656826567, 'number': 1065} | 0.7432            | 0.8088         | 0.7746     | 0.8074           |
+| 0.0988        | 4.0   | 40   | 0.8587          | {'precision': 0.7141327623126338, 'recall': 0.8244746600741656, 'f1': 0.7653471026965003, 'number': 809} | {'precision': 0.4166666666666667, 'recall': 0.5042016806722689, 'f1': 0.4562737642585551, 'number': 119}   | {'precision': 0.8370998116760828, 'recall': 0.8347417840375587, 'f1': 0.8359191349318289, 'number': 1065} | 0.7551            | 0.8108         | 0.7820     | 0.8157           |
+| 0.0848        | 5.0   | 50   | 0.8933          | {'precision': 0.7255813953488373, 'recall': 0.7713226205191595, 'f1': 0.7477531455961653, 'number': 809} | {'precision': 0.4024390243902439, 'recall': 0.5546218487394958, 'f1': 0.46643109540636046, 'number': 119}  | {'precision': 0.8201834862385321, 'recall': 0.8394366197183099, 'f1': 0.8296983758700696, 'number': 1065} | 0.7493            | 0.7948         | 0.7714     | 0.8056           |
+| 0.073         | 6.0   | 60   | 0.9009          | {'precision': 0.7344444444444445, 'recall': 0.8170580964153276, 'f1': 0.7735517846693973, 'number': 809} | {'precision': 0.41721854304635764, 'recall': 0.5294117647058824, 'f1': 0.4666666666666667, 'number': 119}  | {'precision': 0.8107370336669699, 'recall': 0.8366197183098592, 'f1': 0.8234750462107209, 'number': 1065} | 0.7512            | 0.8103         | 0.7796     | 0.8123           |
+| 0.0655        | 7.0   | 70   | 0.9117          | {'precision': 0.7367231638418079, 'recall': 0.8059332509270705, 'f1': 0.769775678866588, 'number': 809}  | {'precision': 0.4357142857142857, 'recall': 0.5126050420168067, 'f1': 0.47104247104247104, 'number': 119}  | {'precision': 0.8170955882352942, 'recall': 0.8347417840375587, 'f1': 0.8258244310264746, 'number': 1065} | 0.7582            | 0.8038         | 0.7803     | 0.8088           |
+| 0.0599        | 8.0   | 80   | 0.9414          | {'precision': 0.7298474945533769, 'recall': 0.8281829419035847, 'f1': 0.7759119861030689, 'number': 809} | {'precision': 0.41496598639455784, 'recall': 0.5126050420168067, 'f1': 0.4586466165413534, 'number': 119}  | {'precision': 0.8100810081008101, 'recall': 0.8450704225352113, 'f1': 0.8272058823529411, 'number': 1065} | 0.7495            | 0.8184         | 0.7824     | 0.8089           |
+| 0.0551        | 9.0   | 90   | 0.9548          | {'precision': 0.746031746031746, 'recall': 0.8133498145859085, 'f1': 0.7782377291543465, 'number': 809}  | {'precision': 0.42953020134228187, 'recall': 0.5378151260504201, 'f1': 0.47761194029850745, 'number': 119} | {'precision': 0.823963133640553, 'recall': 0.8394366197183099, 'f1': 0.8316279069767442, 'number': 1065}  | 0.7637            | 0.8108         | 0.7866     | 0.8111           |
+| 0.0483        | 10.0  | 100  | 0.9684          | {'precision': 0.7390326209223848, 'recall': 0.8121137206427689, 'f1': 0.773851590106007, 'number': 809}  | {'precision': 0.42, 'recall': 0.5294117647058824, 'f1': 0.46840148698884754, 'number': 119}                | {'precision': 0.8232044198895028, 'recall': 0.8394366197183099, 'f1': 0.8312412831241283, 'number': 1065} | 0.7595            | 0.8098         | 0.7839     | 0.8091           |
+| 0.0424        | 11.0  | 110  | 0.9858          | {'precision': 0.7392290249433107, 'recall': 0.8059332509270705, 'f1': 0.7711413364872857, 'number': 809} | {'precision': 0.4258064516129032, 'recall': 0.5546218487394958, 'f1': 0.48175182481751827, 'number': 119}  | {'precision': 0.8252788104089219, 'recall': 0.8338028169014085, 'f1': 0.8295189163942083, 'number': 1065} | 0.7601            | 0.8058         | 0.7823     | 0.8094           |
+| 0.0402        | 12.0  | 120  | 0.9920          | {'precision': 0.7315436241610739, 'recall': 0.8084054388133498, 'f1': 0.7680563711098063, 'number': 809} | {'precision': 0.4460431654676259, 'recall': 0.5210084033613446, 'f1': 0.48062015503875966, 'number': 119}  | {'precision': 0.8205128205128205, 'recall': 0.8413145539906103, 'f1': 0.8307834955957348, 'number': 1065} | 0.7586            | 0.8088         | 0.7829     | 0.8111           |
+| 0.0392        | 13.0  | 130  | 1.0027          | {'precision': 0.7463193657984145, 'recall': 0.8145859085290482, 'f1': 0.7789598108747045, 'number': 809} | {'precision': 0.4397163120567376, 'recall': 0.5210084033613446, 'f1': 0.47692307692307695, 'number': 119}  | {'precision': 0.8216911764705882, 'recall': 0.8394366197183099, 'f1': 0.8304691128657686, 'number': 1065} | 0.7647            | 0.8103         | 0.7868     | 0.8104           |
+| 0.0361        | 14.0  | 140  | 1.0027          | {'precision': 0.7421171171171171, 'recall': 0.8145859085290482, 'f1': 0.7766647024160284, 'number': 809} | {'precision': 0.43884892086330934, 'recall': 0.5126050420168067, 'f1': 0.4728682170542636, 'number': 119}  | {'precision': 0.8205128205128205, 'recall': 0.8413145539906103, 'f1': 0.8307834955957348, 'number': 1065} | 0.7626            | 0.8108         | 0.7860     | 0.8115           |
+| 0.0349        | 15.0  | 150  | 1.0045          | {'precision': 0.7348314606741573, 'recall': 0.8084054388133498, 'f1': 0.7698646262507357, 'number': 809} | {'precision': 0.44285714285714284, 'recall': 0.5210084033613446, 'f1': 0.47876447876447875, 'number': 119} | {'precision': 0.8211009174311926, 'recall': 0.8403755868544601, 'f1': 0.8306264501160092, 'number': 1065} | 0.7599            | 0.8083         | 0.7834     | 0.8106           |
+
+
+### Framework versions
+
+- Transformers 4.21.2
+- Pytorch 1.11.0+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1

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