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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- nielsr/funsd-layoutlmv3 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-finetuned-funsd |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: nielsr/funsd-layoutlmv3 |
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type: nielsr/funsd-layoutlmv3 |
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args: funsd |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9026198714780029 |
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- name: Recall |
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type: recall |
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value: 0.913 |
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- name: F1 |
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type: f1 |
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value: 0.9077802634849614 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8330271015158475 |
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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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# layoutlmv3-finetuned-funsd |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1164 |
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- Precision: 0.9026 |
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- Recall: 0.913 |
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- F1: 0.9078 |
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- Accuracy: 0.8330 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 10.0 | 100 | 0.5238 | 0.8366 | 0.886 | 0.8606 | 0.8410 | |
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| No log | 20.0 | 200 | 0.6930 | 0.8751 | 0.8965 | 0.8857 | 0.8322 | |
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| No log | 30.0 | 300 | 0.7784 | 0.8902 | 0.908 | 0.8990 | 0.8414 | |
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| No log | 40.0 | 400 | 0.9056 | 0.8916 | 0.905 | 0.8983 | 0.8364 | |
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| 0.2429 | 50.0 | 500 | 1.0016 | 0.8954 | 0.9075 | 0.9014 | 0.8298 | |
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| 0.2429 | 60.0 | 600 | 1.0097 | 0.8899 | 0.897 | 0.8934 | 0.8294 | |
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| 0.2429 | 70.0 | 700 | 1.0722 | 0.9035 | 0.9085 | 0.9060 | 0.8315 | |
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| 0.2429 | 80.0 | 800 | 1.0884 | 0.8905 | 0.9105 | 0.9004 | 0.8269 | |
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| 0.2429 | 90.0 | 900 | 1.1292 | 0.8938 | 0.909 | 0.9013 | 0.8279 | |
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| 0.0098 | 100.0 | 1000 | 1.1164 | 0.9026 | 0.913 | 0.9078 | 0.8330 | |
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| No log | 10.0 | 100 | 0.5238 | 0.8366 | 0.886 | 0.8606 | 0.8410 | |
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| No log | 20.0 | 200 | 0.6930 | 0.8751 | 0.8965 | 0.8857 | 0.8322 | |
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| No log | 30.0 | 300 | 0.7784 | 0.8902 | 0.908 | 0.8990 | 0.8414 | |
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| No log | 40.0 | 400 | 0.9056 | 0.8916 | 0.905 | 0.8983 | 0.8364 | |
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| 0.2429 | 50.0 | 500 | 1.0016 | 0.8954 | 0.9075 | 0.9014 | 0.8298 | |
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| 0.2429 | 60.0 | 600 | 1.0097 | 0.8899 | 0.897 | 0.8934 | 0.8294 | |
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| 0.2429 | 70.0 | 700 | 1.0722 | 0.9035 | 0.9085 | 0.9060 | 0.8315 | |
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| 0.2429 | 80.0 | 800 | 1.0884 | 0.8905 | 0.9105 | 0.9004 | 0.8269 | |
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| 0.2429 | 90.0 | 900 | 1.1292 | 0.8938 | 0.909 | 0.9013 | 0.8279 | |
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| 0.0098 | 100.0 | 1000 | 1.1164 | 0.9026 | 0.913 | 0.9078 | 0.8330 | |
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[4000/4000 20:34, Epoch 53/54] |
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Step Training Loss Validation Loss Precision Recall F1 Accuracy |
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250 No log 0.435449 0.854588 0.902136 0.877719 0.835968 |
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500 0.505800 0.611310 0.869822 0.876304 0.873051 0.839177 |
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750 0.505800 0.635022 0.879886 0.917039 0.898078 0.853085 |
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1000 0.097000 0.765935 0.900818 0.929459 0.914914 0.860097 |
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1250 0.097000 0.887739 0.885533 0.903130 0.894245 0.842625 |
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1500 0.029900 0.948754 0.898018 0.923000 0.910338 0.843575 |
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1750 0.029900 1.102811 0.900433 0.929955 0.914956 0.840128 |
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2000 0.009700 1.039040 0.901415 0.917536 0.909404 0.852728 |
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2250 0.009700 1.044235 0.904716 0.924491 0.914496 0.849519 |
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2500 0.002500 1.013194 0.913086 0.918530 0.915800 0.849637 |
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2750 0.002500 1.017520 0.908605 0.928465 0.918428 0.854986 |
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3000 0.000900 1.029559 0.914216 0.926478 0.920306 0.859384 |
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3250 0.000900 1.038318 0.918177 0.930949 0.924519 0.859979 |
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3500 0.000800 1.045578 0.914216 0.926478 0.920306 0.858552 |
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3750 0.000800 1.040568 0.913894 0.927968 0.920877 0.858433 |
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4000 0.000700 1.041146 0.913894 0.927968 0.920877 0.8585528552 |
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### Framework versions |
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- Transformers 4.19.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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