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: 0.6820
+- Answer: {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}
+- Header: {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}
+- Question: {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}
+- Overall Precision: 0.7194
+- Overall Recall: 0.7797
+- Overall F1: 0.7484
+- Overall Accuracy: 0.8102
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7857        | 1.0   | 10   | 1.5985          | {'precision': 0.009248554913294798, 'recall': 0.009888751545117428, 'f1': 0.00955794504181601, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1273972602739726, 'recall': 0.08732394366197183, 'f1': 0.10362116991643454, 'number': 1065} | 0.0633            | 0.0507         | 0.0563     | 0.3562           |
+| 1.4597        | 2.0   | 20   | 1.2331          | {'precision': 0.18717683557394002, 'recall': 0.22373300370828184, 'f1': 0.20382882882882883, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4439461883408072, 'recall': 0.5577464788732395, 'f1': 0.4943820224719101, 'number': 1065}   | 0.3362            | 0.3889         | 0.3606     | 0.6007           |
+| 1.0902        | 3.0   | 30   | 0.9489          | {'precision': 0.4371069182389937, 'recall': 0.515451174289246, 'f1': 0.47305728871242203, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6257615317667538, 'recall': 0.6751173708920187, 'f1': 0.6495031616982836, 'number': 1065}   | 0.5311            | 0.5700         | 0.5499     | 0.6910           |
+| 0.8339        | 4.0   | 40   | 0.7979          | {'precision': 0.5977366255144033, 'recall': 0.7181705809641533, 'f1': 0.652442448062886, 'number': 809}       | {'precision': 0.13513513513513514, 'recall': 0.08403361344537816, 'f1': 0.10362694300518135, 'number': 119} | {'precision': 0.6854545454545454, 'recall': 0.707981220657277, 'f1': 0.6965357967667436, 'number': 1065}    | 0.6267            | 0.6749         | 0.6499     | 0.7453           |
+| 0.6983        | 5.0   | 50   | 0.7659          | {'precision': 0.6392896781354052, 'recall': 0.7119901112484549, 'f1': 0.6736842105263159, 'number': 809}      | {'precision': 0.19626168224299065, 'recall': 0.17647058823529413, 'f1': 0.18584070796460178, 'number': 119} | {'precision': 0.6688102893890675, 'recall': 0.7812206572769953, 'f1': 0.7206582936336077, 'number': 1065}   | 0.6345            | 0.7170         | 0.6733     | 0.7610           |
+| 0.5815        | 6.0   | 60   | 0.6907          | {'precision': 0.6410256410256411, 'recall': 0.7725587144622992, 'f1': 0.7006726457399104, 'number': 809}      | {'precision': 0.23863636363636365, 'recall': 0.17647058823529413, 'f1': 0.20289855072463767, 'number': 119} | {'precision': 0.7027463651050081, 'recall': 0.8169014084507042, 'f1': 0.7555362570560139, 'number': 1065}   | 0.6588            | 0.7607         | 0.7061     | 0.7913           |
+| 0.5044        | 7.0   | 70   | 0.6802          | {'precision': 0.6727078891257996, 'recall': 0.7799752781211372, 'f1': 0.7223812249570692, 'number': 809}      | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}  | {'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065}   | 0.6830            | 0.7556         | 0.7175     | 0.7902           |
+| 0.4534        | 8.0   | 80   | 0.6595          | {'precision': 0.7018701870187019, 'recall': 0.788627935723115, 'f1': 0.7427240977881256, 'number': 809}       | {'precision': 0.234375, 'recall': 0.25210084033613445, 'f1': 0.242914979757085, 'number': 119}              | {'precision': 0.7378559463986599, 'recall': 0.8272300469483568, 'f1': 0.779991146525011, 'number': 1065}    | 0.6943            | 0.7772         | 0.7334     | 0.8074           |
+| 0.3971        | 9.0   | 90   | 0.6625          | {'precision': 0.6967032967032967, 'recall': 0.7836835599505563, 'f1': 0.7376381617219313, 'number': 809}      | {'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119}           | {'precision': 0.7433930093776641, 'recall': 0.8187793427230047, 'f1': 0.7792672028596961, 'number': 1065}   | 0.6950            | 0.7742         | 0.7325     | 0.8060           |
+| 0.3593        | 10.0  | 100  | 0.6634          | {'precision': 0.7079152731326644, 'recall': 0.7849196538936959, 'f1': 0.7444314185228605, 'number': 809}      | {'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119}  | {'precision': 0.7571305099394987, 'recall': 0.8225352112676056, 'f1': 0.7884788478847885, 'number': 1065}   | 0.7060            | 0.7772         | 0.7399     | 0.8115           |
+| 0.3209        | 11.0  | 110  | 0.6655          | {'precision': 0.6973262032085561, 'recall': 0.8059332509270705, 'f1': 0.7477064220183487, 'number': 809}      | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119}    | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}   | 0.7162            | 0.7852         | 0.7492     | 0.8129           |
+| 0.3064        | 12.0  | 120  | 0.6771          | {'precision': 0.7104072398190046, 'recall': 0.7762669962917181, 'f1': 0.74187832250443, 'number': 809}        | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}    | {'precision': 0.7795698924731183, 'recall': 0.8169014084507042, 'f1': 0.797799174690509, 'number': 1065}    | 0.7166            | 0.7712         | 0.7429     | 0.8088           |
+| 0.286         | 13.0  | 130  | 0.6765          | {'precision': 0.7030905077262694, 'recall': 0.7873918417799752, 'f1': 0.7428571428571429, 'number': 809}      | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}    | {'precision': 0.769298245614035, 'recall': 0.8234741784037559, 'f1': 0.7954648526077097, 'number': 1065}    | 0.7088            | 0.7792         | 0.7424     | 0.8111           |
+| 0.2806        | 14.0  | 140  | 0.6820          | {'precision': 0.7052980132450332, 'recall': 0.7898640296662547, 'f1': 0.7451895043731779, 'number': 809}      | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}    | {'precision': 0.7793594306049823, 'recall': 0.8225352112676056, 'f1': 0.8003654636820466, 'number': 1065}   | 0.7145            | 0.7797         | 0.7457     | 0.8106           |
+| 0.2736        | 15.0  | 150  | 0.6820          | {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}      | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}    | {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}   | 0.7194            | 0.7797         | 0.7484     | 0.8102           |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1

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