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: 1.0307
+- Answer: {'precision': 0.3855302279484638, 'recall': 0.48084054388133496, 'f1': 0.4279427942794279, 'number': 809}
+- Header: {'precision': 0.34782608695652173, 'recall': 0.2689075630252101, 'f1': 0.3033175355450237, 'number': 119}
+- Question: {'precision': 0.48268238761974946, 'recall': 0.6150234741784038, 'f1': 0.5408753096614369, 'number': 1065}
+- Overall Precision: 0.4378
+- Overall Recall: 0.5399
+- Overall F1: 0.4835
+- Overall Accuracy: 0.6393
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7508        | 1.0   | 10   | 1.5163          | {'precision': 0.07105263157894737, 'recall': 0.10012360939431397, 'f1': 0.08311954848640328, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2490566037735849, 'recall': 0.18591549295774648, 'f1': 0.2129032258064516, 'number': 1065}   | 0.1442            | 0.1400         | 0.1421     | 0.3638           |
+| 1.4483        | 2.0   | 20   | 1.3842          | {'precision': 0.19585898153329603, 'recall': 0.4326328800988875, 'f1': 0.2696456086286595, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.27010309278350514, 'recall': 0.36901408450704226, 'f1': 0.3119047619047619, 'number': 1065}  | 0.2286            | 0.3728         | 0.2834     | 0.4135           |
+| 1.3068        | 3.0   | 30   | 1.2439          | {'precision': 0.2390092879256966, 'recall': 0.47713226205191595, 'f1': 0.3184818481848185, 'number': 809}   | {'precision': 0.03125, 'recall': 0.01680672268907563, 'f1': 0.02185792349726776, 'number': 119}             | {'precision': 0.32887189292543023, 'recall': 0.48450704225352115, 'f1': 0.39179954441913445, 'number': 1065} | 0.2783            | 0.4536         | 0.3450     | 0.4631           |
+| 1.1868        | 4.0   | 40   | 1.1443          | {'precision': 0.25613802256138024, 'recall': 0.47713226205191595, 'f1': 0.33333333333333337, 'number': 809} | {'precision': 0.1797752808988764, 'recall': 0.13445378151260504, 'f1': 0.15384615384615385, 'number': 119}  | {'precision': 0.3619233268356075, 'recall': 0.5230046948356808, 'f1': 0.42780337941628266, 'number': 1065}   | 0.3059            | 0.4812         | 0.3740     | 0.5267           |
+| 1.0837        | 5.0   | 50   | 1.1479          | {'precision': 0.27571728481455565, 'recall': 0.48702101359703337, 'f1': 0.3521000893655049, 'number': 809}  | {'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119}  | {'precision': 0.3705616526791478, 'recall': 0.5389671361502347, 'f1': 0.4391736801836266, 'number': 1065}    | 0.3234            | 0.4977         | 0.3921     | 0.5252           |
+| 1.0102        | 6.0   | 60   | 1.1154          | {'precision': 0.29912810194500333, 'recall': 0.5512978986402967, 'f1': 0.3878260869565217, 'number': 809}   | {'precision': 0.2604166666666667, 'recall': 0.21008403361344538, 'f1': 0.23255813953488375, 'number': 119}  | {'precision': 0.44872918492550395, 'recall': 0.4807511737089202, 'f1': 0.4641885766092475, 'number': 1065}   | 0.3603            | 0.4932         | 0.4164     | 0.5831           |
+| 0.9349        | 7.0   | 70   | 1.0180          | {'precision': 0.3333333333333333, 'recall': 0.4289245982694685, 'f1': 0.37513513513513513, 'number': 809}   | {'precision': 0.32558139534883723, 'recall': 0.23529411764705882, 'f1': 0.2731707317073171, 'number': 119}  | {'precision': 0.42487046632124353, 'recall': 0.615962441314554, 'f1': 0.5028746646224608, 'number': 1065}    | 0.3860            | 0.5173         | 0.4421     | 0.6121           |
+| 0.8786        | 8.0   | 80   | 1.0198          | {'precision': 0.3177723177723178, 'recall': 0.4796044499381953, 'f1': 0.3822660098522168, 'number': 809}    | {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119}  | {'precision': 0.4321808510638298, 'recall': 0.6103286384976526, 'f1': 0.5060334760607241, 'number': 1065}    | 0.3773            | 0.5354         | 0.4426     | 0.6088           |
+| 0.8204        | 9.0   | 90   | 1.0123          | {'precision': 0.3665987780040733, 'recall': 0.44499381953028433, 'f1': 0.40201005025125625, 'number': 809}  | {'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119}    | {'precision': 0.45675482487491065, 'recall': 0.6, 'f1': 0.5186688311688312, 'number': 1065}                  | 0.4147            | 0.5148         | 0.4594     | 0.6320           |
+| 0.8126        | 10.0  | 100  | 1.0461          | {'precision': 0.37877312560856863, 'recall': 0.48084054388133496, 'f1': 0.42374727668845313, 'number': 809} | {'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119}                   | {'precision': 0.4764521193092622, 'recall': 0.5699530516431925, 'f1': 0.5190252244548953, 'number': 1065}    | 0.4279            | 0.5133         | 0.4667     | 0.6288           |
+| 0.7357        | 11.0  | 110  | 1.0160          | {'precision': 0.3771839671120247, 'recall': 0.453646477132262, 'f1': 0.4118967452300786, 'number': 809}     | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}  | {'precision': 0.4672639558924879, 'recall': 0.6366197183098592, 'f1': 0.5389507154213037, 'number': 1065}    | 0.4252            | 0.5404         | 0.4759     | 0.6369           |
+| 0.7249        | 12.0  | 120  | 1.0246          | {'precision': 0.38046795523906407, 'recall': 0.4622991347342398, 'f1': 0.4174107142857143, 'number': 809}   | {'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119} | {'precision': 0.4727403156384505, 'recall': 0.6187793427230047, 'f1': 0.5359902399349329, 'number': 1065}    | 0.4288            | 0.5334         | 0.4754     | 0.6387           |
+| 0.7015        | 13.0  | 130  | 1.0335          | {'precision': 0.36654135338345867, 'recall': 0.4820766378244747, 'f1': 0.416444207154298, 'number': 809}    | {'precision': 0.31521739130434784, 'recall': 0.24369747899159663, 'f1': 0.27488151658767773, 'number': 119} | {'precision': 0.4788104089219331, 'recall': 0.6046948356807512, 'f1': 0.5344398340248964, 'number': 1065}    | 0.4250            | 0.5334         | 0.4731     | 0.6326           |
+| 0.6696        | 14.0  | 140  | 1.0364          | {'precision': 0.3841121495327103, 'recall': 0.5080346106304079, 'f1': 0.43746673762639704, 'number': 809}   | {'precision': 0.32941176470588235, 'recall': 0.23529411764705882, 'f1': 0.2745098039215686, 'number': 119}  | {'precision': 0.48804934464148036, 'recall': 0.5943661971830986, 'f1': 0.5359864521591872, 'number': 1065}   | 0.4372            | 0.5379         | 0.4823     | 0.6394           |
+| 0.6661        | 15.0  | 150  | 1.0307          | {'precision': 0.3855302279484638, 'recall': 0.48084054388133496, 'f1': 0.4279427942794279, 'number': 809}   | {'precision': 0.34782608695652173, 'recall': 0.2689075630252101, 'f1': 0.3033175355450237, 'number': 119}   | {'precision': 0.48268238761974946, 'recall': 0.6150234741784038, 'f1': 0.5408753096614369, 'number': 1065}   | 0.4378            | 0.5399         | 0.4835     | 0.6393           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1709878071.533af087a84b.2561.0

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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+}