layoutlmv3-finetuned-cord_5
Browse files- README.md +99 -0
- pytorch_model.bin +1 -1
README.md
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---
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license: cc-by-nc-sa-4.0
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base_model: microsoft/layoutlmv3-base
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tags:
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- generated_from_trainer
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datasets:
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- cord-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-cord_100
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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: cord-layoutlmv3
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type: cord-layoutlmv3
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config: cord
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split: test
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args: cord
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metrics:
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- name: Precision
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type: precision
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value: 0.596078431372549
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- name: Recall
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type: recall
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value: 0.6826347305389222
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- name: F1
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type: f1
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value: 0.636427076064201
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- name: Accuracy
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type: accuracy
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value: 0.684634974533107
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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-cord_100
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9357
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- Precision: 0.5961
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- Recall: 0.6826
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- F1: 0.6364
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- Accuracy: 0.6846
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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: 5
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- eval_batch_size: 5
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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: 2500
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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 | 250.0 | 250 | 1.5298 | 0.5778 | 0.6781 | 0.6240 | 0.6825 |
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| 0.6654 | 500.0 | 500 | 1.6175 | 0.5942 | 0.6849 | 0.6363 | 0.6880 |
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| 0.6654 | 750.0 | 750 | 1.7087 | 0.5947 | 0.6841 | 0.6363 | 0.6876 |
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| 0.0208 | 1000.0 | 1000 | 1.7729 | 0.5948 | 0.6834 | 0.6360 | 0.6859 |
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| 0.0208 | 1250.0 | 1250 | 1.8273 | 0.5949 | 0.6826 | 0.6358 | 0.6851 |
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| 0.0099 | 1500.0 | 1500 | 1.8693 | 0.5957 | 0.6826 | 0.6362 | 0.6846 |
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| 0.0099 | 1750.0 | 1750 | 1.8969 | 0.5950 | 0.6819 | 0.6355 | 0.6842 |
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| 0.0066 | 2000.0 | 2000 | 1.9196 | 0.5972 | 0.6826 | 0.6371 | 0.6842 |
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| 0.0066 | 2250.0 | 2250 | 1.9312 | 0.5946 | 0.6819 | 0.6353 | 0.6838 |
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| 0.0054 | 2500.0 | 2500 | 1.9357 | 0.5961 | 0.6826 | 0.6364 | 0.6846 |
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### Framework versions
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- Transformers 4.34.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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pytorch_model.bin
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