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poojadamavarapu123
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layoutlm-funsd

Token Classification
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layoutlm
Generated from Trainer
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  1. README.md +80 -0
  2. config.json +43 -0
  3. logs/events.out.tfevents.1763995492.14188b08a788.688.0 +3 -0
  4. model.safetensors +3 -0
  5. preprocessor_config.json +13 -0
  6. special_tokens_map.json +37 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +81 -0
  9. training_args.bin +3 -0
  10. 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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+ 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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6945
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+ - Answer: {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}
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+ - Header: {'precision': 0.3656716417910448, 'recall': 0.4117647058823529, 'f1': 0.3873517786561265, 'number': 119}
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+ - Question: {'precision': 0.7918552036199095, 'recall': 0.8215962441314554, 'f1': 0.8064516129032258, 'number': 1065}
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+ - Overall Precision: 0.7275
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+ - Overall Recall: 0.7878
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+ - Overall F1: 0.7564
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+ - Overall Accuracy: 0.8052
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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+ | 1.8328 | 1.0 | 10 | 1.6269 | {'precision': 0.012106537530266344, 'recall': 0.012360939431396786, 'f1': 0.012232415902140673, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2, 'recall': 0.15492957746478872, 'f1': 0.17460317460317462, 'number': 1065} | 0.1060 | 0.0878 | 0.0960 | 0.3555 |
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+ | 1.4745 | 2.0 | 20 | 1.2810 | {'precision': 0.1425339366515837, 'recall': 0.1557478368355995, 'f1': 0.1488481984642646, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.44803982576228996, 'recall': 0.676056338028169, 'f1': 0.5389221556886227, 'number': 1065} | 0.3394 | 0.4245 | 0.3772 | 0.5679 |
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+ | 1.0973 | 3.0 | 30 | 0.9149 | {'precision': 0.48193916349809884, 'recall': 0.6266996291718171, 'f1': 0.5448683503492746, 'number': 809} | {'precision': 0.06521739130434782, 'recall': 0.025210084033613446, 'f1': 0.03636363636363636, 'number': 119} | {'precision': 0.5712250712250713, 'recall': 0.7530516431924883, 'f1': 0.6496557310652086, 'number': 1065} | 0.5244 | 0.6583 | 0.5838 | 0.7085 |
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+ | 0.8318 | 4.0 | 40 | 0.7626 | {'precision': 0.5760151085930123, 'recall': 0.754017305315204, 'f1': 0.6531049250535332, 'number': 809} | {'precision': 0.21621621621621623, 'recall': 0.13445378151260504, 'f1': 0.16580310880829016, 'number': 119} | {'precision': 0.6803418803418804, 'recall': 0.7474178403755869, 'f1': 0.7123042505592841, 'number': 1065} | 0.6175 | 0.7135 | 0.6620 | 0.7648 |
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+ | 0.6741 | 5.0 | 50 | 0.7031 | {'precision': 0.630457933972311, 'recall': 0.7317676143386898, 'f1': 0.6773455377574371, 'number': 809} | {'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119} | {'precision': 0.6975060337892196, 'recall': 0.8140845070422535, 'f1': 0.7512998266897747, 'number': 1065} | 0.6531 | 0.7461 | 0.6965 | 0.7841 |
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+ | 0.5677 | 6.0 | 60 | 0.6814 | {'precision': 0.6348884381338742, 'recall': 0.7737948084054388, 'f1': 0.6974930362116991, 'number': 809} | {'precision': 0.3125, 'recall': 0.21008403361344538, 'f1': 0.25125628140703515, 'number': 119} | {'precision': 0.7495479204339964, 'recall': 0.7784037558685446, 'f1': 0.7637033625057578, 'number': 1065} | 0.6814 | 0.7426 | 0.7107 | 0.7808 |
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+ | 0.4939 | 7.0 | 70 | 0.6524 | {'precision': 0.6795698924731183, 'recall': 0.7812113720642769, 'f1': 0.7268545140885566, 'number': 809} | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119} | {'precision': 0.76657824933687, 'recall': 0.8140845070422535, 'f1': 0.7896174863387978, 'number': 1065} | 0.7068 | 0.7692 | 0.7367 | 0.7968 |
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+ | 0.4355 | 8.0 | 80 | 0.6496 | {'precision': 0.6659707724425887, 'recall': 0.788627935723115, 'f1': 0.7221279003961517, 'number': 809} | {'precision': 0.3055555555555556, 'recall': 0.2773109243697479, 'f1': 0.2907488986784141, 'number': 119} | {'precision': 0.7633851468048359, 'recall': 0.8300469483568075, 'f1': 0.7953216374269007, 'number': 1065} | 0.6992 | 0.7802 | 0.7375 | 0.8021 |
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+ | 0.3922 | 9.0 | 90 | 0.6662 | {'precision': 0.6965442764578834, 'recall': 0.7972805933250927, 'f1': 0.7435158501440923, 'number': 809} | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119} | {'precision': 0.7817028985507246, 'recall': 0.8103286384976526, 'f1': 0.7957584140156754, 'number': 1065} | 0.7185 | 0.7762 | 0.7463 | 0.8034 |
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+ | 0.3788 | 10.0 | 100 | 0.6630 | {'precision': 0.7004357298474946, 'recall': 0.7948084054388134, 'f1': 0.7446438911407064, 'number': 809} | {'precision': 0.36283185840707965, 'recall': 0.3445378151260504, 'f1': 0.35344827586206895, 'number': 119} | {'precision': 0.7727674624226348, 'recall': 0.8206572769953052, 'f1': 0.795992714025501, 'number': 1065} | 0.7206 | 0.7817 | 0.7499 | 0.8053 |
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+ | 0.3263 | 11.0 | 110 | 0.6684 | {'precision': 0.6940540540540541, 'recall': 0.7935723114956736, 'f1': 0.740484429065744, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.35294117647058826, 'f1': 0.34285714285714286, 'number': 119} | {'precision': 0.7744755244755245, 'recall': 0.831924882629108, 'f1': 0.8021729289271163, 'number': 1065} | 0.7153 | 0.7878 | 0.7498 | 0.8053 |
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+ | 0.3098 | 12.0 | 120 | 0.6795 | {'precision': 0.7033805888767721, 'recall': 0.7972805933250927, 'f1': 0.7473928157589804, 'number': 809} | {'precision': 0.359375, 'recall': 0.3865546218487395, 'f1': 0.3724696356275304, 'number': 119} | {'precision': 0.7887579329102448, 'recall': 0.8169014084507042, 'f1': 0.8025830258302583, 'number': 1065} | 0.7267 | 0.7832 | 0.7539 | 0.8073 |
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+ | 0.2976 | 13.0 | 130 | 0.6857 | {'precision': 0.6913183279742765, 'recall': 0.7972805933250927, 'f1': 0.74052812858783, 'number': 809} | {'precision': 0.3524590163934426, 'recall': 0.36134453781512604, 'f1': 0.35684647302904565, 'number': 119} | {'precision': 0.7831858407079646, 'recall': 0.8309859154929577, 'f1': 0.806378132118451, 'number': 1065} | 0.7199 | 0.7893 | 0.7530 | 0.8042 |
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+ | 0.277 | 14.0 | 140 | 0.6918 | {'precision': 0.7022900763358778, 'recall': 0.796044499381953, 'f1': 0.7462340672074159, 'number': 809} | {'precision': 0.36363636363636365, 'recall': 0.40336134453781514, 'f1': 0.38247011952191234, 'number': 119} | {'precision': 0.7848214285714286, 'recall': 0.8253521126760563, 'f1': 0.8045766590389016, 'number': 1065} | 0.7243 | 0.7883 | 0.7549 | 0.8038 |
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+ | 0.2706 | 15.0 | 150 | 0.6945 | {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809} | {'precision': 0.3656716417910448, 'recall': 0.4117647058823529, 'f1': 0.3873517786561265, 'number': 119} | {'precision': 0.7918552036199095, 'recall': 0.8215962441314554, 'f1': 0.8064516129032258, 'number': 1065} | 0.7275 | 0.7878 | 0.7564 | 0.8052 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.1
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+ - Pytorch 2.9.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.1
config.json ADDED
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+ "LayoutLMForTokenClassification"
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+ "1": "B-HEADER",
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+ "2": "I-HEADER",
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+ "3": "B-QUESTION",
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+ "max_position_embeddings": 512,
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+ "model_type": "layoutlm",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "position_embedding_type": "absolute",
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+ "type_vocab_size": 2,
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+ }
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+ {
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tokenizer.json ADDED
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