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LiLt-funsd-en

Token Classification
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PyTorch
TensorBoard
lilt
Generated from Trainer
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End of training

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  1. README.md +85 -0
  2. logs/events.out.tfevents.1669920212.132-145-128-28.65525.0 +2 -2
  3. merges.txt +0 -0
  4. preprocessor_config.json +21 -0
  5. pytorch_model.bin +1 -1
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +86 -0
  9. vocab.json +0 -0
README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - funsd-layoutlmv3
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+ model-index:
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+ - name: LiLt-funsd-en
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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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+ # LiLt-funsd-en
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+
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+ This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7735
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+ - Answer: {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817}
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+ - Header: {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}
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+ - Question: {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077}
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+ - Overall Precision: 0.8760
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+ - Overall Recall: 0.8882
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+ - Overall F1: 0.8821
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+ - Overall Accuracy: 0.8027
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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: 5e-05
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+ - train_batch_size: 8
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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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+ - training_steps: 4000
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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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+ | 0.4108 | 10.53 | 200 | 0.9495 | {'precision': 0.7949260042283298, 'recall': 0.9204406364749081, 'f1': 0.8530913216108904, 'number': 817} | {'precision': 0.5338345864661654, 'recall': 0.5966386554621849, 'f1': 0.5634920634920635, 'number': 119} | {'precision': 0.8950914340712224, 'recall': 0.8635097493036211, 'f1': 0.8790170132325141, 'number': 1077} | 0.8277 | 0.8708 | 0.8487 | 0.7845 |
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+ | 0.0402 | 21.05 | 400 | 1.3124 | {'precision': 0.8167597765363128, 'recall': 0.8947368421052632, 'f1': 0.8539719626168224, 'number': 817} | {'precision': 0.5071428571428571, 'recall': 0.5966386554621849, 'f1': 0.5482625482625483, 'number': 119} | {'precision': 0.9133398247322297, 'recall': 0.8709377901578459, 'f1': 0.8916349809885932, 'number': 1077} | 0.8438 | 0.8644 | 0.8540 | 0.7901 |
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+ | 0.0141 | 31.58 | 600 | 1.4747 | {'precision': 0.8429378531073446, 'recall': 0.9130966952264382, 'f1': 0.8766157461809635, 'number': 817} | {'precision': 0.6493506493506493, 'recall': 0.42016806722689076, 'f1': 0.5102040816326531, 'number': 119} | {'precision': 0.8861788617886179, 'recall': 0.9108635097493036, 'f1': 0.8983516483516484, 'number': 1077} | 0.8589 | 0.8828 | 0.8707 | 0.7950 |
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+ | 0.0079 | 42.11 | 800 | 1.6605 | {'precision': 0.8132464712269273, 'recall': 0.9167686658506732, 'f1': 0.8619102416570771, 'number': 817} | {'precision': 0.5407407407407407, 'recall': 0.6134453781512605, 'f1': 0.5748031496062992, 'number': 119} | {'precision': 0.8919925512104283, 'recall': 0.8895078922934077, 'f1': 0.8907484890748489, 'number': 1077} | 0.8357 | 0.8843 | 0.8593 | 0.7910 |
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+ | 0.0064 | 52.63 | 1000 | 1.6328 | {'precision': 0.8551564310544612, 'recall': 0.9033047735618115, 'f1': 0.8785714285714284, 'number': 817} | {'precision': 0.7466666666666667, 'recall': 0.47058823529411764, 'f1': 0.5773195876288659, 'number': 119} | {'precision': 0.9029918404351768, 'recall': 0.924791086350975, 'f1': 0.9137614678899083, 'number': 1077} | 0.8770 | 0.8892 | 0.8831 | 0.7950 |
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+ | 0.0091 | 63.16 | 1200 | 1.6620 | {'precision': 0.8280044101433297, 'recall': 0.9192166462668299, 'f1': 0.87122969837587, 'number': 817} | {'precision': 0.6875, 'recall': 0.46218487394957986, 'f1': 0.5527638190954773, 'number': 119} | {'precision': 0.9016697588126159, 'recall': 0.9025069637883009, 'f1': 0.9020881670533641, 'number': 1077} | 0.8610 | 0.8833 | 0.8720 | 0.7979 |
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+ | 0.0031 | 73.68 | 1400 | 1.7310 | {'precision': 0.855039637599094, 'recall': 0.9241126070991432, 'f1': 0.8882352941176471, 'number': 817} | {'precision': 0.7023809523809523, 'recall': 0.4957983193277311, 'f1': 0.58128078817734, 'number': 119} | {'precision': 0.8969917958067457, 'recall': 0.9136490250696379, 'f1': 0.9052437902483901, 'number': 1077} | 0.8711 | 0.8932 | 0.8820 | 0.7938 |
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+ | 0.002 | 84.21 | 1600 | 1.6978 | {'precision': 0.870023419203747, 'recall': 0.9094247246022031, 'f1': 0.8892878515858766, 'number': 817} | {'precision': 0.6987951807228916, 'recall': 0.48739495798319327, 'f1': 0.5742574257425742, 'number': 119} | {'precision': 0.9021237303785781, 'recall': 0.9071494893221913, 'f1': 0.9046296296296297, 'number': 1077} | 0.8802 | 0.8833 | 0.8817 | 0.8048 |
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+ | 0.0023 | 94.74 | 1800 | 1.5996 | {'precision': 0.8639534883720931, 'recall': 0.9094247246022031, 'f1': 0.8861061419200955, 'number': 817} | {'precision': 0.6629213483146067, 'recall': 0.4957983193277311, 'f1': 0.5673076923076922, 'number': 119} | {'precision': 0.8969641214351426, 'recall': 0.9052924791086351, 'f1': 0.9011090573012939, 'number': 1077} | 0.8728 | 0.8828 | 0.8777 | 0.8026 |
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+ | 0.0009 | 105.26 | 2000 | 1.7239 | {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} | {'precision': 0.889487870619946, 'recall': 0.9192200557103064, 'f1': 0.9041095890410958, 'number': 1077} | 0.8698 | 0.8927 | 0.8811 | 0.8008 |
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+ | 0.0009 | 115.79 | 2200 | 1.6091 | {'precision': 0.8576349024110218, 'recall': 0.9143206854345165, 'f1': 0.8850710900473934, 'number': 817} | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} | {'precision': 0.8901098901098901, 'recall': 0.9025069637883009, 'f1': 0.896265560165975, 'number': 1077} | 0.8630 | 0.8857 | 0.8742 | 0.8093 |
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+ | 0.0003 | 126.32 | 2400 | 1.7210 | {'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817} | {'precision': 0.6555555555555556, 'recall': 0.4957983193277311, 'f1': 0.5645933014354068, 'number': 119} | {'precision': 0.8873994638069705, 'recall': 0.9220055710306406, 'f1': 0.9043715846994536, 'number': 1077} | 0.8671 | 0.8942 | 0.8804 | 0.8072 |
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+ | 0.0006 | 136.84 | 2600 | 1.8215 | {'precision': 0.8293478260869566, 'recall': 0.9339045287637698, 'f1': 0.8785261945883708, 'number': 817} | {'precision': 0.5887850467289719, 'recall': 0.5294117647058824, 'f1': 0.5575221238938053, 'number': 119} | {'precision': 0.9232227488151659, 'recall': 0.904363974001857, 'f1': 0.9136960600375235, 'number': 1077} | 0.8646 | 0.8942 | 0.8791 | 0.7946 |
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+ | 0.0002 | 147.37 | 2800 | 1.8084 | {'precision': 0.875886524822695, 'recall': 0.9069767441860465, 'f1': 0.8911605532170775, 'number': 817} | {'precision': 0.5752212389380531, 'recall': 0.5462184873949579, 'f1': 0.5603448275862069, 'number': 119} | {'precision': 0.8896860986547085, 'recall': 0.9210770659238626, 'f1': 0.9051094890510949, 'number': 1077} | 0.8669 | 0.8932 | 0.8799 | 0.8000 |
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+ | 0.0002 | 157.89 | 3000 | 1.8224 | {'precision': 0.9067164179104478, 'recall': 0.8922888616891065, 'f1': 0.8994447871684145, 'number': 817} | {'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119} | {'precision': 0.8937112488928255, 'recall': 0.9368616527390901, 'f1': 0.9147778785131461, 'number': 1077} | 0.8868 | 0.8912 | 0.8890 | 0.8082 |
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+ | 0.0005 | 168.42 | 3200 | 1.7383 | {'precision': 0.8754406580493537, 'recall': 0.9118727050183598, 'f1': 0.8932853717026379, 'number': 817} | {'precision': 0.6451612903225806, 'recall': 0.5042016806722689, 'f1': 0.5660377358490566, 'number': 119} | {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} | 0.8780 | 0.8937 | 0.8858 | 0.8117 |
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+ | 0.0002 | 178.95 | 3400 | 1.7757 | {'precision': 0.885954381752701, 'recall': 0.9033047735618115, 'f1': 0.8945454545454545, 'number': 817} | {'precision': 0.6896551724137931, 'recall': 0.5042016806722689, 'f1': 0.5825242718446602, 'number': 119} | {'precision': 0.9043321299638989, 'recall': 0.9303621169916435, 'f1': 0.917162471395881, 'number': 1077} | 0.8876 | 0.8942 | 0.8909 | 0.8104 |
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+ | 0.0001 | 189.47 | 3600 | 1.7467 | {'precision': 0.8645833333333334, 'recall': 0.9143206854345165, 'f1': 0.8887566924449734, 'number': 817} | {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119} | {'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077} | 0.8741 | 0.8902 | 0.8821 | 0.8054 |
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+ | 0.0002 | 200.0 | 3800 | 1.7730 | {'precision': 0.8631090487238979, 'recall': 0.9106487148102815, 'f1': 0.8862418106015486, 'number': 817} | {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119} | {'precision': 0.8952205882352942, 'recall': 0.904363974001857, 'f1': 0.899769053117783, 'number': 1077} | 0.8695 | 0.8838 | 0.8766 | 0.8024 |
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+ | 0.0001 | 210.53 | 4000 | 1.7735 | {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817} | {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119} | {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077} | 0.8760 | 0.8882 | 0.8821 | 0.8027 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.12.1
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.2
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