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layoutml_funsd_rjz

Token Classification
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layoutlm
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End of training

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  1. README.md +79 -0
  2. logs/events.out.tfevents.1679524644.69b95b832af1.1895.2 +2 -2
  3. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - funsd
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+ model-index:
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+ - name: layoutmlv2_funsd_rjz
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # layoutmlv2_funsd_rjz
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+
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+ This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9422
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+ - Answer: {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809}
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+ - Header: {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119}
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+ - Question: {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065}
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+ - Overall Precision: 0.7527
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+ - Overall Recall: 0.7973
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+ - Overall F1: 0.7744
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+ - Overall Accuracy: 0.8096
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.3143 | 1.0 | 10 | 0.7685 | {'precision': 0.7, 'recall': 0.7700865265760197, 'f1': 0.7333725721012359, 'number': 809} | {'precision': 0.2986111111111111, 'recall': 0.36134453781512604, 'f1': 0.32699619771863114, 'number': 119} | {'precision': 0.7693032015065914, 'recall': 0.7671361502347418, 'f1': 0.768218147625764, 'number': 1065} | 0.7075 | 0.7441 | 0.7254 | 0.7924 |
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+ | 0.2816 | 2.0 | 20 | 0.7829 | {'precision': 0.7162315550510783, 'recall': 0.7799752781211372, 'f1': 0.7467455621301775, 'number': 809} | {'precision': 0.33152173913043476, 'recall': 0.5126050420168067, 'f1': 0.40264026402640263, 'number': 119} | {'precision': 0.7855839416058394, 'recall': 0.8084507042253521, 'f1': 0.7968533086534013, 'number': 1065} | 0.7186 | 0.7792 | 0.7477 | 0.7976 |
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+ | 0.2216 | 3.0 | 30 | 0.7825 | {'precision': 0.7016806722689075, 'recall': 0.8257107540173053, 'f1': 0.7586598523566157, 'number': 809} | {'precision': 0.35570469798657717, 'recall': 0.44537815126050423, 'f1': 0.39552238805970147, 'number': 119} | {'precision': 0.7851985559566786, 'recall': 0.8169014084507042, 'f1': 0.8007363092498849, 'number': 1065} | 0.7202 | 0.7983 | 0.7573 | 0.7942 |
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+ | 0.1973 | 4.0 | 40 | 0.7683 | {'precision': 0.7095032397408207, 'recall': 0.8121137206427689, 'f1': 0.7573487031700288, 'number': 809} | {'precision': 0.3968253968253968, 'recall': 0.42016806722689076, 'f1': 0.40816326530612246, 'number': 119} | {'precision': 0.802367941712204, 'recall': 0.8272300469483568, 'f1': 0.8146093388811835, 'number': 1065} | 0.7386 | 0.7968 | 0.7666 | 0.8143 |
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+ | 0.1671 | 5.0 | 50 | 0.7918 | {'precision': 0.7269585253456221, 'recall': 0.7799752781211372, 'f1': 0.7525342874180083, 'number': 809} | {'precision': 0.4076923076923077, 'recall': 0.44537815126050423, 'f1': 0.42570281124497994, 'number': 119} | {'precision': 0.7848888888888889, 'recall': 0.8291079812206573, 'f1': 0.8063926940639269, 'number': 1065} | 0.7381 | 0.7863 | 0.7614 | 0.8139 |
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+ | 0.1342 | 6.0 | 60 | 0.8295 | {'precision': 0.7234972677595628, 'recall': 0.8182941903584673, 'f1': 0.7679814385150812, 'number': 809} | {'precision': 0.37857142857142856, 'recall': 0.44537815126050423, 'f1': 0.4092664092664093, 'number': 119} | {'precision': 0.7939339875111507, 'recall': 0.8356807511737089, 'f1': 0.8142726440988106, 'number': 1065} | 0.7376 | 0.8053 | 0.7700 | 0.8120 |
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+ | 0.1212 | 7.0 | 70 | 0.8632 | {'precision': 0.7337883959044369, 'recall': 0.7972805933250927, 'f1': 0.764218009478673, 'number': 809} | {'precision': 0.4084507042253521, 'recall': 0.48739495798319327, 'f1': 0.4444444444444445, 'number': 119} | {'precision': 0.8137347130761995, 'recall': 0.812206572769953, 'f1': 0.8129699248120301, 'number': 1065} | 0.7524 | 0.7868 | 0.7692 | 0.8082 |
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+ | 0.1131 | 8.0 | 80 | 0.9081 | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} | {'precision': 0.40131578947368424, 'recall': 0.5126050420168067, 'f1': 0.4501845018450184, 'number': 119} | {'precision': 0.8097876269621422, 'recall': 0.8234741784037559, 'f1': 0.8165735567970206, 'number': 1065} | 0.7446 | 0.8018 | 0.7722 | 0.8011 |
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+ | 0.1043 | 9.0 | 90 | 0.9021 | {'precision': 0.7308132875143184, 'recall': 0.788627935723115, 'f1': 0.7586206896551724, 'number': 809} | {'precision': 0.425531914893617, 'recall': 0.5042016806722689, 'f1': 0.4615384615384615, 'number': 119} | {'precision': 0.7914818101153505, 'recall': 0.8375586854460094, 'f1': 0.8138686131386863, 'number': 1065} | 0.7426 | 0.7978 | 0.7692 | 0.8075 |
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+ | 0.0884 | 10.0 | 100 | 0.9126 | {'precision': 0.7231450719822813, 'recall': 0.8071693448702101, 'f1': 0.7628504672897196, 'number': 809} | {'precision': 0.40939597315436244, 'recall': 0.5126050420168067, 'f1': 0.4552238805970149, 'number': 119} | {'precision': 0.819718309859155, 'recall': 0.819718309859155, 'f1': 0.819718309859155, 'number': 1065} | 0.7496 | 0.7963 | 0.7723 | 0.8094 |
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+ | 0.084 | 11.0 | 110 | 0.9354 | {'precision': 0.7502944640753828, 'recall': 0.7873918417799752, 'f1': 0.7683956574185766, 'number': 809} | {'precision': 0.4140127388535032, 'recall': 0.5462184873949579, 'f1': 0.47101449275362317, 'number': 119} | {'precision': 0.7946428571428571, 'recall': 0.8356807511737089, 'f1': 0.8146453089244852, 'number': 1065} | 0.7488 | 0.7988 | 0.7730 | 0.8064 |
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+ | 0.0794 | 12.0 | 120 | 0.9323 | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809} | {'precision': 0.4172661870503597, 'recall': 0.48739495798319327, 'f1': 0.4496124031007752, 'number': 119} | {'precision': 0.8152985074626866, 'recall': 0.8206572769953052, 'f1': 0.8179691155825924, 'number': 1065} | 0.7502 | 0.7988 | 0.7738 | 0.8094 |
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+ | 0.0803 | 13.0 | 130 | 0.9429 | {'precision': 0.7401129943502824, 'recall': 0.8096415327564895, 'f1': 0.7733175914994096, 'number': 809} | {'precision': 0.42592592592592593, 'recall': 0.5798319327731093, 'f1': 0.49110320284697506, 'number': 119} | {'precision': 0.8110599078341014, 'recall': 0.8262910798122066, 'f1': 0.8186046511627907, 'number': 1065} | 0.7523 | 0.8048 | 0.7777 | 0.8085 |
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+ | 0.0754 | 14.0 | 140 | 0.9393 | {'precision': 0.7425629290617849, 'recall': 0.8022249690976514, 'f1': 0.7712418300653594, 'number': 809} | {'precision': 0.4225352112676056, 'recall': 0.5042016806722689, 'f1': 0.45977011494252873, 'number': 119} | {'precision': 0.8018099547511313, 'recall': 0.831924882629108, 'f1': 0.816589861751152, 'number': 1065} | 0.7520 | 0.8003 | 0.7754 | 0.8106 |
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+ | 0.0732 | 15.0 | 150 | 0.9422 | {'precision': 0.7382857142857143, 'recall': 0.7985166872682324, 'f1': 0.7672209026128266, 'number': 809} | {'precision': 0.42758620689655175, 'recall': 0.5210084033613446, 'f1': 0.4696969696969697, 'number': 119} | {'precision': 0.8075160403299725, 'recall': 0.8272300469483568, 'f1': 0.8172541743970314, 'number': 1065} | 0.7527 | 0.7973 | 0.7744 | 0.8096 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.2
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.10.1
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+ - Tokenizers 0.13.2
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