End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# layoutlm-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7085
+- Answer: {'precision': 0.721081081081081, 'recall': 0.8244746600741656, 'f1': 0.7693194925028833, 'number': 809}
+- Header: {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}
+- Question: {'precision': 0.7871772039180766, 'recall': 0.8300469483568075, 'f1': 0.8080438756855575, 'number': 1065}
+- Overall Precision: 0.7328
+- Overall Recall: 0.7983
+- Overall F1: 0.7642
+- Overall Accuracy: 0.8112
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                         | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.792         | 1.0   | 10   | 1.5932          | {'precision': 0.03648648648648649, 'recall': 0.03337453646477132, 'f1': 0.034861200774693346, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.3541114058355438, 'recall': 0.2507042253521127, 'f1': 0.29356789444749865, 'number': 1065} | 0.1968            | 0.1475         | 0.1686     | 0.3760           |
+| 1.4339        | 2.0   | 20   | 1.2410          | {'precision': 0.2177121771217712, 'recall': 0.21878862793572312, 'f1': 0.218249075215783, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.43688639551192143, 'recall': 0.5849765258215962, 'f1': 0.5002007226013649, 'number': 1065} | 0.3573            | 0.4014         | 0.3781     | 0.5877           |
+| 1.0937        | 3.0   | 30   | 0.9505          | {'precision': 0.45005149330587024, 'recall': 0.5401730531520396, 'f1': 0.4910112359550562, 'number': 809}    | {'precision': 0.045454545454545456, 'recall': 0.008403361344537815, 'f1': 0.014184397163120567, 'number': 119} | {'precision': 0.6046141607000796, 'recall': 0.7136150234741784, 'f1': 0.6546080964685616, 'number': 1065}  | 0.5324            | 0.6011         | 0.5647     | 0.7057           |
+| 0.835         | 4.0   | 40   | 0.7870          | {'precision': 0.6255274261603375, 'recall': 0.7330037082818294, 'f1': 0.6750142287990893, 'number': 809}     | {'precision': 0.19298245614035087, 'recall': 0.09243697478991597, 'f1': 0.125, 'number': 119}                  | {'precision': 0.6779220779220779, 'recall': 0.7352112676056338, 'f1': 0.7054054054054054, 'number': 1065}  | 0.6421            | 0.6959         | 0.6680     | 0.7601           |
+| 0.6644        | 5.0   | 50   | 0.7063          | {'precision': 0.6771739130434783, 'recall': 0.7700865265760197, 'f1': 0.7206477732793521, 'number': 809}     | {'precision': 0.2857142857142857, 'recall': 0.2184873949579832, 'f1': 0.24761904761904763, 'number': 119}      | {'precision': 0.6783161239078633, 'recall': 0.8018779342723005, 'f1': 0.7349397590361446, 'number': 1065}  | 0.6621            | 0.7541         | 0.7051     | 0.7872           |
+| 0.5612        | 6.0   | 60   | 0.6880          | {'precision': 0.6639593908629442, 'recall': 0.8084054388133498, 'f1': 0.7290969899665551, 'number': 809}     | {'precision': 0.26262626262626265, 'recall': 0.2184873949579832, 'f1': 0.23853211009174313, 'number': 119}     | {'precision': 0.7401229148375769, 'recall': 0.7915492957746478, 'f1': 0.76497277676951, 'number': 1065}    | 0.6851            | 0.7642         | 0.7225     | 0.7937           |
+| 0.4819        | 7.0   | 70   | 0.6610          | {'precision': 0.6937697993664202, 'recall': 0.8121137206427689, 'f1': 0.7482915717539863, 'number': 809}     | {'precision': 0.30097087378640774, 'recall': 0.2605042016806723, 'f1': 0.27927927927927926, 'number': 119}     | {'precision': 0.7568766637089619, 'recall': 0.8009389671361502, 'f1': 0.7782846715328468, 'number': 1065}  | 0.7079            | 0.7732         | 0.7391     | 0.8034           |
+| 0.4299        | 8.0   | 80   | 0.6725          | {'precision': 0.6850152905198776, 'recall': 0.830655129789864, 'f1': 0.7508379888268155, 'number': 809}      | {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}      | {'precision': 0.7534364261168385, 'recall': 0.8234741784037559, 'f1': 0.7868999551368328, 'number': 1065}  | 0.7012            | 0.7923         | 0.7439     | 0.7950           |
+| 0.3801        | 9.0   | 90   | 0.6654          | {'precision': 0.7142857142857143, 'recall': 0.8158220024721878, 'f1': 0.7616849394114252, 'number': 809}     | {'precision': 0.3047619047619048, 'recall': 0.2689075630252101, 'f1': 0.28571428571428575, 'number': 119}      | {'precision': 0.7697715289982425, 'recall': 0.8225352112676056, 'f1': 0.7952791647753064, 'number': 1065}  | 0.7236            | 0.7868         | 0.7538     | 0.8092           |
+| 0.3757        | 10.0  | 100  | 0.6709          | {'precision': 0.7082452431289641, 'recall': 0.8281829419035847, 'f1': 0.7635327635327636, 'number': 809}     | {'precision': 0.34, 'recall': 0.2857142857142857, 'f1': 0.31050228310502287, 'number': 119}                    | {'precision': 0.7769028871391076, 'recall': 0.8338028169014085, 'f1': 0.8043478260869565, 'number': 1065}  | 0.7273            | 0.7988         | 0.7614     | 0.8145           |
+| 0.3165        | 11.0  | 110  | 0.6781          | {'precision': 0.723726977248104, 'recall': 0.8257107540173053, 'f1': 0.7713625866050808, 'number': 809}      | {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}               | {'precision': 0.7736842105263158, 'recall': 0.828169014084507, 'f1': 0.7999999999999999, 'number': 1065}   | 0.7252            | 0.7973         | 0.7596     | 0.8077           |
+| 0.2993        | 12.0  | 120  | 0.6894          | {'precision': 0.71875, 'recall': 0.8244746600741656, 'f1': 0.7679907887161773, 'number': 809}                | {'precision': 0.3247863247863248, 'recall': 0.31932773109243695, 'f1': 0.3220338983050848, 'number': 119}      | {'precision': 0.7823008849557522, 'recall': 0.8300469483568075, 'f1': 0.8054669703872438, 'number': 1065}  | 0.7306            | 0.7973         | 0.7625     | 0.8117           |
+| 0.2822        | 13.0  | 130  | 0.7039          | {'precision': 0.7195652173913043, 'recall': 0.8182941903584673, 'f1': 0.7657605552342395, 'number': 809}     | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}                  | {'precision': 0.7823008849557522, 'recall': 0.8300469483568075, 'f1': 0.8054669703872438, 'number': 1065}  | 0.7282            | 0.7958         | 0.7605     | 0.8095           |
+| 0.2595        | 14.0  | 140  | 0.7045          | {'precision': 0.72, 'recall': 0.823238566131026, 'f1': 0.7681660899653979, 'number': 809}                    | {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}      | {'precision': 0.7912578055307761, 'recall': 0.8328638497652582, 'f1': 0.8115279048490394, 'number': 1065}  | 0.7365            | 0.7993         | 0.7666     | 0.8118           |
+| 0.2617        | 15.0  | 150  | 0.7085          | {'precision': 0.721081081081081, 'recall': 0.8244746600741656, 'f1': 0.7693194925028833, 'number': 809}      | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}      | {'precision': 0.7871772039180766, 'recall': 0.8300469483568075, 'f1': 0.8080438756855575, 'number': 1065}  | 0.7328            | 0.7983         | 0.7642     | 0.8112           |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

logs/events.out.tfevents.1717252354.280ef54c6982.3413.0

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preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
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+    "apply_ocr",
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special_tokens_map.json

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tokenizer_config.json

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+}