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vsystem
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layoutlm-morning

Token Classification
Transformers
TensorBoard
Safetensors
layoutlm
Generated from Trainer
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End of training

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Files changed (11) hide show
  1. README.md +82 -0
  2. config.json +44 -0
  3. logs/events.out.tfevents.1727444232.featurize +3 -0
  4. logs/events.out.tfevents.1727444569.featurize +3 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +13 -0
  7. special_tokens_map.json +37 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +80 -0
  10. training_args.bin +3 -0
  11. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/layoutlm-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - 01_load_data_set
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+ model-index:
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+ - name: layoutlm-morning
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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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+ # layoutlm-morning
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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 01_load_data_set dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6956
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+ - Answer: {'precision': 0.6905016008537886, 'recall': 0.799752781211372, 'f1': 0.7411225658648338, 'number': 809}
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+ - Header: {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}
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+ - Question: {'precision': 0.7729296527159395, 'recall': 0.8150234741784037, 'f1': 0.793418647166362, 'number': 1065}
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+ - Overall Precision: 0.7134
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+ - Overall Recall: 0.7782
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+ - Overall F1: 0.7444
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+ - Overall Accuracy: 0.8070
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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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+ | 1.8333 | 1.0 | 10 | 1.6190 | {'precision': 0.0047694753577106515, 'recall': 0.003708281829419036, 'f1': 0.004172461752433936, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.19169329073482427, 'recall': 0.11267605633802817, 'f1': 0.1419278533412182, 'number': 1065} | 0.0980 | 0.0617 | 0.0757 | 0.3380 |
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+ | 1.4863 | 2.0 | 20 | 1.2878 | {'precision': 0.18296892980437285, 'recall': 0.1965389369592089, 'f1': 0.1895113230035757, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.42759795570698467, 'recall': 0.47136150234741786, 'f1': 0.4484144707458687, 'number': 1065} | 0.3235 | 0.3317 | 0.3276 | 0.5761 |
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+ | 1.1342 | 3.0 | 30 | 0.9369 | {'precision': 0.4929444967074318, 'recall': 0.6477132262051916, 'f1': 0.5598290598290597, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5810372771474879, 'recall': 0.6732394366197183, 'f1': 0.6237494562853414, 'number': 1065} | 0.5340 | 0.6227 | 0.5749 | 0.7069 |
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+ | 0.8661 | 4.0 | 40 | 0.7953 | {'precision': 0.5635864592863677, 'recall': 0.761433868974042, 'f1': 0.6477392218717138, 'number': 809} | {'precision': 0.06779661016949153, 'recall': 0.03361344537815126, 'f1': 0.0449438202247191, 'number': 119} | {'precision': 0.6269035532994924, 'recall': 0.6957746478873239, 'f1': 0.6595460614152203, 'number': 1065} | 0.5831 | 0.6829 | 0.6291 | 0.7567 |
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+ | 0.6917 | 5.0 | 50 | 0.7124 | {'precision': 0.6372045220966084, 'recall': 0.7663782447466008, 'f1': 0.6958473625140292, 'number': 809} | {'precision': 0.19767441860465115, 'recall': 0.14285714285714285, 'f1': 0.16585365853658537, 'number': 119} | {'precision': 0.673469387755102, 'recall': 0.7746478873239436, 'f1': 0.7205240174672489, 'number': 1065} | 0.6401 | 0.7336 | 0.6837 | 0.7887 |
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+ | 0.591 | 6.0 | 60 | 0.7081 | {'precision': 0.6201171875, 'recall': 0.7849196538936959, 'f1': 0.6928532460447353, 'number': 809} | {'precision': 0.11009174311926606, 'recall': 0.10084033613445378, 'f1': 0.10526315789473684, 'number': 119} | {'precision': 0.7084063047285464, 'recall': 0.7596244131455399, 'f1': 0.7331218849116448, 'number': 1065} | 0.64 | 0.7306 | 0.6823 | 0.7810 |
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+ | 0.515 | 7.0 | 70 | 0.6769 | {'precision': 0.665625, 'recall': 0.7898640296662547, 'f1': 0.7224420576596947, 'number': 809} | {'precision': 0.21238938053097345, 'recall': 0.20168067226890757, 'f1': 0.20689655172413793, 'number': 119} | {'precision': 0.730836236933798, 'recall': 0.787793427230047, 'f1': 0.7582467239042024, 'number': 1065} | 0.6763 | 0.7536 | 0.7129 | 0.7947 |
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+ | 0.4607 | 8.0 | 80 | 0.6785 | {'precision': 0.6605128205128206, 'recall': 0.796044499381953, 'f1': 0.7219730941704037, 'number': 809} | {'precision': 0.20967741935483872, 'recall': 0.2184873949579832, 'f1': 0.2139917695473251, 'number': 119} | {'precision': 0.731686541737649, 'recall': 0.8065727699530516, 'f1': 0.7673068334077713, 'number': 1065} | 0.6727 | 0.7672 | 0.7168 | 0.7987 |
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+ | 0.408 | 9.0 | 90 | 0.6652 | {'precision': 0.6800847457627118, 'recall': 0.7935723114956736, 'f1': 0.7324586423274386, 'number': 809} | {'precision': 0.25806451612903225, 'recall': 0.2689075630252101, 'f1': 0.26337448559670784, 'number': 119} | {'precision': 0.7521588946459413, 'recall': 0.8178403755868544, 'f1': 0.783625730994152, 'number': 1065} | 0.6941 | 0.7752 | 0.7324 | 0.8068 |
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+ | 0.3936 | 10.0 | 100 | 0.6655 | {'precision': 0.6747114375655824, 'recall': 0.7948084054388134, 'f1': 0.7298524404086267, 'number': 809} | {'precision': 0.336734693877551, 'recall': 0.2773109243697479, 'f1': 0.30414746543778803, 'number': 119} | {'precision': 0.7747747747747747, 'recall': 0.8075117370892019, 'f1': 0.7908045977011493, 'number': 1065} | 0.7108 | 0.7707 | 0.7395 | 0.8122 |
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+ | 0.3423 | 11.0 | 110 | 0.6888 | {'precision': 0.7035398230088495, 'recall': 0.7861557478368356, 'f1': 0.7425569176882663, 'number': 809} | {'precision': 0.27702702702702703, 'recall': 0.3445378151260504, 'f1': 0.30711610486891383, 'number': 119} | {'precision': 0.7586805555555556, 'recall': 0.8206572769953052, 'f1': 0.7884528642309426, 'number': 1065} | 0.7037 | 0.7782 | 0.7391 | 0.8060 |
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+ | 0.3293 | 12.0 | 120 | 0.6758 | {'precision': 0.679324894514768, 'recall': 0.796044499381953, 'f1': 0.7330677290836654, 'number': 809} | {'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119} | {'precision': 0.7754919499105546, 'recall': 0.8140845070422535, 'f1': 0.7943197434722858, 'number': 1065} | 0.7104 | 0.7742 | 0.7409 | 0.8123 |
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+ | 0.3062 | 13.0 | 130 | 0.6923 | {'precision': 0.698051948051948, 'recall': 0.7972805933250927, 'f1': 0.7443739180611655, 'number': 809} | {'precision': 0.2890625, 'recall': 0.31092436974789917, 'f1': 0.29959514170040485, 'number': 119} | {'precision': 0.7679509632224168, 'recall': 0.8234741784037559, 'f1': 0.79474399637517, 'number': 1065} | 0.7106 | 0.7822 | 0.7447 | 0.8091 |
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+ | 0.2858 | 14.0 | 140 | 0.6966 | {'precision': 0.6915688367129136, 'recall': 0.8009888751545118, 'f1': 0.7422680412371134, 'number': 809} | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119} | {'precision': 0.7813911472448057, 'recall': 0.812206572769953, 'f1': 0.796500920810313, 'number': 1065} | 0.7185 | 0.7772 | 0.7467 | 0.8060 |
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+ | 0.286 | 15.0 | 150 | 0.6956 | {'precision': 0.6905016008537886, 'recall': 0.799752781211372, 'f1': 0.7411225658648338, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119} | {'precision': 0.7729296527159395, 'recall': 0.8150234741784037, 'f1': 0.793418647166362, 'number': 1065} | 0.7134 | 0.7782 | 0.7444 | 0.8070 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.1
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0
config.json ADDED
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+ "LayoutLMForTokenClassification"
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+ "0": "O",
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+ "3": "B-QUESTION",
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+ "model_type": "layoutlm",
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+ "num_attention_heads": 12,
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+ "position_embedding_type": "absolute",
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