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layoutlm-funsd

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  1. README.md +81 -0
  2. logs/events.out.tfevents.1723637769.venkanna-vm01.1122146.0 +2 -2
  3. model.safetensors +1 -1
  4. preprocessor_config.json +13 -0
  5. special_tokens_map.json +37 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +80 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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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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+ - funsd
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+ model-index:
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+ - name: layoutlm-funsd
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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-funsd
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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: 0.6718
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+ - Answer: {'precision': 0.7074235807860262, 'recall': 0.8009888751545118, 'f1': 0.7513043478260869, 'number': 809}
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+ - Header: {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}
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+ - Question: {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}
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+ - Overall Precision: 0.7242
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+ - Overall Recall: 0.7852
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+ - Overall F1: 0.7535
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+ - Overall Accuracy: 0.8120
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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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+ | 1.8183 | 1.0 | 10 | 1.6239 | {'precision': 0.010256410256410256, 'recall': 0.004944375772558714, 'f1': 0.006672226855713093, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18360655737704917, 'recall': 0.05258215962441314, 'f1': 0.08175182481751825, 'number': 1065} | 0.0863 | 0.0301 | 0.0446 | 0.3196 |
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+ | 1.4789 | 2.0 | 20 | 1.2907 | {'precision': 0.12058465286236297, 'recall': 0.12237330037082818, 'f1': 0.12147239263803682, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4493717664449372, 'recall': 0.5708920187793427, 'f1': 0.5028949545078577, 'number': 1065} | 0.3252 | 0.3547 | 0.3393 | 0.5843 |
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+ | 1.139 | 3.0 | 30 | 0.9533 | {'precision': 0.4473409801876955, 'recall': 0.5302843016069221, 'f1': 0.4852941176470588, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5831399845320959, 'recall': 0.707981220657277, 'f1': 0.6395250212044105, 'number': 1065} | 0.5232 | 0.5936 | 0.5562 | 0.7090 |
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+ | 0.8802 | 4.0 | 40 | 0.7961 | {'precision': 0.5869565217391305, 'recall': 0.7676143386897404, 'f1': 0.6652383502945903, 'number': 809} | {'precision': 0.05714285714285714, 'recall': 0.01680672268907563, 'f1': 0.025974025974025972, 'number': 119} | {'precision': 0.6845878136200717, 'recall': 0.7173708920187793, 'f1': 0.7005960568546539, 'number': 1065} | 0.6279 | 0.6959 | 0.6602 | 0.7604 |
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+ | 0.6957 | 5.0 | 50 | 0.7201 | {'precision': 0.6137040714995035, 'recall': 0.7639060568603214, 'f1': 0.6806167400881058, 'number': 809} | {'precision': 0.18518518518518517, 'recall': 0.12605042016806722, 'f1': 0.15, 'number': 119} | {'precision': 0.6730769230769231, 'recall': 0.7887323943661971, 'f1': 0.7263294422827496, 'number': 1065} | 0.6306 | 0.7391 | 0.6805 | 0.7774 |
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+ | 0.5881 | 6.0 | 60 | 0.6950 | {'precision': 0.6294820717131474, 'recall': 0.7812113720642769, 'f1': 0.6971869829012686, 'number': 809} | {'precision': 0.23684210526315788, 'recall': 0.15126050420168066, 'f1': 0.1846153846153846, 'number': 119} | {'precision': 0.7171453437771975, 'recall': 0.7737089201877935, 'f1': 0.7443541102077688, 'number': 1065} | 0.6613 | 0.7396 | 0.6982 | 0.7890 |
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+ | 0.5129 | 7.0 | 70 | 0.6612 | {'precision': 0.6733615221987315, 'recall': 0.7873918417799752, 'f1': 0.7259259259259259, 'number': 809} | {'precision': 0.25, 'recall': 0.24369747899159663, 'f1': 0.24680851063829787, 'number': 119} | {'precision': 0.7306052855924978, 'recall': 0.8046948356807512, 'f1': 0.7658623771224308, 'number': 1065} | 0.6814 | 0.7642 | 0.7204 | 0.8005 |
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+ | 0.4565 | 8.0 | 80 | 0.6582 | {'precision': 0.6840458811261731, 'recall': 0.8108776266996292, 'f1': 0.7420814479638008, 'number': 809} | {'precision': 0.28846153846153844, 'recall': 0.25210084033613445, 'f1': 0.26905829596412556, 'number': 119} | {'precision': 0.7543859649122807, 'recall': 0.8075117370892019, 'f1': 0.780045351473923, 'number': 1065} | 0.7018 | 0.7757 | 0.7369 | 0.8050 |
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+ | 0.4033 | 9.0 | 90 | 0.6490 | {'precision': 0.7037037037037037, 'recall': 0.7985166872682324, 'f1': 0.7481181239143023, 'number': 809} | {'precision': 0.30357142857142855, 'recall': 0.2857142857142857, 'f1': 0.2943722943722944, 'number': 119} | {'precision': 0.7824116047144152, 'recall': 0.8103286384976526, 'f1': 0.7961254612546125, 'number': 1065} | 0.7234 | 0.7742 | 0.7479 | 0.8103 |
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+ | 0.3954 | 10.0 | 100 | 0.6486 | {'precision': 0.7044711014176663, 'recall': 0.7985166872682324, 'f1': 0.7485515643105446, 'number': 809} | {'precision': 0.3185840707964602, 'recall': 0.3025210084033613, 'f1': 0.3103448275862069, 'number': 119} | {'precision': 0.7813333333333333, 'recall': 0.8253521126760563, 'f1': 0.8027397260273973, 'number': 1065} | 0.7244 | 0.7832 | 0.7527 | 0.8159 |
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+ | 0.3404 | 11.0 | 110 | 0.6524 | {'precision': 0.7085152838427947, 'recall': 0.8022249690976514, 'f1': 0.7524637681159421, 'number': 809} | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119} | {'precision': 0.7857142857142857, 'recall': 0.8262910798122066, 'f1': 0.8054919908466819, 'number': 1065} | 0.7263 | 0.7858 | 0.7549 | 0.8134 |
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+ | 0.3191 | 12.0 | 120 | 0.6599 | {'precision': 0.7135016465422612, 'recall': 0.8034610630407911, 'f1': 0.7558139534883722, 'number': 809} | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119} | {'precision': 0.7857785778577858, 'recall': 0.819718309859155, 'f1': 0.8023897058823529, 'number': 1065} | 0.7282 | 0.7822 | 0.7542 | 0.8138 |
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+ | 0.3046 | 13.0 | 130 | 0.6726 | {'precision': 0.7176339285714286, 'recall': 0.7948084054388134, 'f1': 0.7542521994134898, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7784642541924095, 'recall': 0.828169014084507, 'f1': 0.802547770700637, 'number': 1065} | 0.7262 | 0.7852 | 0.7546 | 0.8159 |
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+ | 0.2839 | 14.0 | 140 | 0.6710 | {'precision': 0.7043478260869566, 'recall': 0.8009888751545118, 'f1': 0.7495662232504339, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7802491103202847, 'recall': 0.8234741784037559, 'f1': 0.801279122887163, 'number': 1065} | 0.7212 | 0.7852 | 0.7519 | 0.8139 |
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+ | 0.2826 | 15.0 | 150 | 0.6718 | {'precision': 0.7074235807860262, 'recall': 0.8009888751545118, 'f1': 0.7513043478260869, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065} | 0.7242 | 0.7852 | 0.7535 | 0.8120 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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+ {
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vocab.txt ADDED
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