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Ammar-alhaj-ali
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LayoutLMv3-Fine-Tuning-FUNSD

Token Classification
Transformers
PyTorch
layoutlmv3
Generated from Trainer
Eval Results
Inference Endpoints
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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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+
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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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+ # layoutlmv3-finetuned-funsd
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+
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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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+
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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: 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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+
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+ ### Training results
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+
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+ Step|Training Loss |Validation Loss| Precision| Recal|l F1| Accuracy
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+ |:-------------:||:-------------:||:-------------:||:-------------:||:-------------:|
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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| 0.858552
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+
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+
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+ ### Framework versions
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+
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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