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: 0.6940
+- Answer: {'precision': 0.721978021978022, 'recall': 0.8121137206427689, 'f1': 0.7643979057591623, 'number': 809}
+- Header: {'precision': 0.2662337662337662, 'recall': 0.3445378151260504, 'f1': 0.30036630036630035, 'number': 119}
+- Question: {'precision': 0.7816091954022989, 'recall': 0.8300469483568075, 'f1': 0.8051001821493625, 'number': 1065}
+- Overall Precision: 0.7207
+- Overall Recall: 0.7938
+- Overall F1: 0.7555
+- Overall Accuracy: 0.8073
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.755         | 1.0   | 10   | 1.5815          | {'precision': 0.026919242273180457, 'recall': 0.03337453646477132, 'f1': 0.02980132450331126, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.20780487804878048, 'recall': 0.2, 'f1': 0.20382775119617225, 'number': 1065}              | 0.1183            | 0.1204         | 0.1194     | 0.3885           |
+| 1.4375        | 2.0   | 20   | 1.2088          | {'precision': 0.28227848101265823, 'recall': 0.27564894932014833, 'f1': 0.2789243277048155, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4782964782964783, 'recall': 0.5483568075117371, 'f1': 0.5109361329833771, 'number': 1065} | 0.4013            | 0.4049         | 0.4031     | 0.6223           |
+| 1.0595        | 3.0   | 30   | 0.9379          | {'precision': 0.503954802259887, 'recall': 0.5512978986402967, 'f1': 0.526564344746163, 'number': 809}       | {'precision': 0.0425531914893617, 'recall': 0.01680672268907563, 'f1': 0.024096385542168672, 'number': 119} | {'precision': 0.6126205083260298, 'recall': 0.6563380281690141, 'f1': 0.6337262012692656, 'number': 1065} | 0.5533            | 0.5755         | 0.5642     | 0.7194           |
+| 0.8139        | 4.0   | 40   | 0.7735          | {'precision': 0.6280041797283177, 'recall': 0.7428924598269468, 'f1': 0.680634201585504, 'number': 809}      | {'precision': 0.13432835820895522, 'recall': 0.07563025210084033, 'f1': 0.09677419354838708, 'number': 119} | {'precision': 0.6600688468158348, 'recall': 0.72018779342723, 'f1': 0.688819039066008, 'number': 1065}    | 0.6299            | 0.6909         | 0.6590     | 0.7636           |
+| 0.664         | 5.0   | 50   | 0.7245          | {'precision': 0.6519453207150369, 'recall': 0.7663782447466008, 'f1': 0.7045454545454546, 'number': 809}     | {'precision': 0.24719101123595505, 'recall': 0.18487394957983194, 'f1': 0.21153846153846156, 'number': 119} | {'precision': 0.7090909090909091, 'recall': 0.7690140845070422, 'f1': 0.7378378378378379, 'number': 1065} | 0.6656            | 0.7331         | 0.6977     | 0.7757           |
+| 0.5505        | 6.0   | 60   | 0.6956          | {'precision': 0.6834061135371179, 'recall': 0.7737948084054388, 'f1': 0.7257971014492753, 'number': 809}     | {'precision': 0.28205128205128205, 'recall': 0.18487394957983194, 'f1': 0.2233502538071066, 'number': 119}  | {'precision': 0.723421926910299, 'recall': 0.8178403755868544, 'f1': 0.7677390921110622, 'number': 1065}  | 0.6911            | 0.7622         | 0.7249     | 0.7888           |
+| 0.4759        | 7.0   | 70   | 0.6712          | {'precision': 0.6844396082698585, 'recall': 0.7775030902348579, 'f1': 0.7280092592592592, 'number': 809}     | {'precision': 0.2727272727272727, 'recall': 0.2773109243697479, 'f1': 0.27499999999999997, 'number': 119}   | {'precision': 0.7472527472527473, 'recall': 0.8300469483568075, 'f1': 0.786476868327402, 'number': 1065}  | 0.6955            | 0.7757         | 0.7334     | 0.7975           |
+| 0.4276        | 8.0   | 80   | 0.6765          | {'precision': 0.6889375684556407, 'recall': 0.7775030902348579, 'f1': 0.7305458768873403, 'number': 809}     | {'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}   | {'precision': 0.7527333894028595, 'recall': 0.8403755868544601, 'f1': 0.7941437444543035, 'number': 1065} | 0.7017            | 0.7812         | 0.7393     | 0.8021           |
+| 0.3788        | 9.0   | 90   | 0.6653          | {'precision': 0.7081930415263749, 'recall': 0.7799752781211372, 'f1': 0.7423529411764707, 'number': 809}     | {'precision': 0.2647058823529412, 'recall': 0.3025210084033613, 'f1': 0.2823529411764706, 'number': 119}    | {'precision': 0.7667238421955404, 'recall': 0.8394366197183099, 'f1': 0.8014343343792021, 'number': 1065} | 0.7118            | 0.7832         | 0.7458     | 0.8049           |
+| 0.3466        | 10.0  | 100  | 0.6838          | {'precision': 0.7005464480874317, 'recall': 0.792336217552534, 'f1': 0.7436194895591649, 'number': 809}      | {'precision': 0.2706766917293233, 'recall': 0.3025210084033613, 'f1': 0.28571428571428564, 'number': 119}   | {'precision': 0.7728055077452668, 'recall': 0.8431924882629108, 'f1': 0.8064660978895375, 'number': 1065} | 0.7127            | 0.7903         | 0.7495     | 0.8047           |
+| 0.3142        | 11.0  | 110  | 0.6795          | {'precision': 0.6997816593886463, 'recall': 0.792336217552534, 'f1': 0.7431884057971013, 'number': 809}      | {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119}    | {'precision': 0.7994628469113697, 'recall': 0.8384976525821596, 'f1': 0.8185151237396883, 'number': 1065} | 0.7272            | 0.7878         | 0.7563     | 0.8067           |
+| 0.2978        | 12.0  | 120  | 0.6922          | {'precision': 0.6927194860813705, 'recall': 0.799752781211372, 'f1': 0.7423981640849111, 'number': 809}      | {'precision': 0.2585034013605442, 'recall': 0.31932773109243695, 'f1': 0.2857142857142857, 'number': 119}   | {'precision': 0.7768090671316478, 'recall': 0.8366197183098592, 'f1': 0.8056057866184448, 'number': 1065} | 0.7074            | 0.7908         | 0.7467     | 0.8026           |
+| 0.2824        | 13.0  | 130  | 0.6960          | {'precision': 0.7184357541899441, 'recall': 0.7948084054388134, 'f1': 0.754694835680751, 'number': 809}      | {'precision': 0.2611464968152866, 'recall': 0.3445378151260504, 'f1': 0.2971014492753623, 'number': 119}    | {'precision': 0.7757255936675461, 'recall': 0.828169014084507, 'f1': 0.8010899182561309, 'number': 1065}  | 0.7154            | 0.7858         | 0.7489     | 0.8045           |
+| 0.2696        | 14.0  | 140  | 0.6917          | {'precision': 0.7164667393675027, 'recall': 0.8121137206427689, 'f1': 0.7612977983777521, 'number': 809}     | {'precision': 0.2708333333333333, 'recall': 0.3277310924369748, 'f1': 0.2965779467680608, 'number': 119}    | {'precision': 0.7833775419982316, 'recall': 0.831924882629108, 'f1': 0.8069216757741348, 'number': 1065}  | 0.7217            | 0.7938         | 0.7560     | 0.8067           |
+| 0.2674        | 15.0  | 150  | 0.6940          | {'precision': 0.721978021978022, 'recall': 0.8121137206427689, 'f1': 0.7643979057591623, 'number': 809}      | {'precision': 0.2662337662337662, 'recall': 0.3445378151260504, 'f1': 0.30036630036630035, 'number': 119}   | {'precision': 0.7816091954022989, 'recall': 0.8300469483568075, 'f1': 0.8051001821493625, 'number': 1065} | 0.7207            | 0.7938         | 0.7555     | 0.8073           |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3

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