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--- |
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base_model: uitnlp/visobert |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: VP_ViSoBERT_syl_ViWikiFC |
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results: [] |
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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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# VP_ViSoBERT_syl_ViWikiFC |
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This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9243 |
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- Accuracy: 0.6364 |
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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: 2e-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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.1299 | 0.1 | 100 | 1.1182 | 0.3411 | |
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| 1.0816 | 0.19 | 200 | 1.0678 | 0.3976 | |
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| 1.0181 | 0.29 | 300 | 1.0163 | 0.4823 | |
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| 1.0121 | 0.38 | 400 | 0.9956 | 0.5072 | |
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| 0.9617 | 0.48 | 500 | 0.9718 | 0.5048 | |
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| 0.9297 | 0.57 | 600 | 0.9665 | 0.5239 | |
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| 0.9332 | 0.67 | 700 | 0.9252 | 0.5646 | |
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| 0.9057 | 0.76 | 800 | 0.9667 | 0.5421 | |
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| 0.8756 | 0.86 | 900 | 0.8884 | 0.5871 | |
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| 0.879 | 0.96 | 1000 | 0.8907 | 0.5718 | |
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| 0.8249 | 1.05 | 1100 | 0.8793 | 0.5981 | |
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| 0.7177 | 1.15 | 1200 | 0.8951 | 0.5957 | |
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| 0.7145 | 1.24 | 1300 | 0.9523 | 0.6062 | |
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| 0.7469 | 1.34 | 1400 | 0.9001 | 0.5986 | |
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| 0.7358 | 1.43 | 1500 | 0.8865 | 0.6081 | |
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| 0.7112 | 1.53 | 1600 | 0.9099 | 0.6057 | |
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| 0.7299 | 1.62 | 1700 | 0.8496 | 0.6144 | |
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| 0.6949 | 1.72 | 1800 | 0.8580 | 0.6124 | |
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| 0.6988 | 1.81 | 1900 | 0.8840 | 0.6215 | |
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| 0.6524 | 1.91 | 2000 | 0.8753 | 0.6134 | |
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| 0.6914 | 2.01 | 2100 | 0.8729 | 0.6330 | |
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| 0.5427 | 2.1 | 2200 | 0.9494 | 0.6431 | |
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| 0.5628 | 2.2 | 2300 | 0.9531 | 0.6120 | |
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| 0.5607 | 2.29 | 2400 | 0.9050 | 0.6340 | |
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| 0.5396 | 2.39 | 2500 | 0.9149 | 0.6335 | |
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| 0.5178 | 2.48 | 2600 | 0.9848 | 0.6124 | |
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| 0.5322 | 2.58 | 2700 | 0.9198 | 0.6330 | |
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| 0.5406 | 2.67 | 2800 | 0.9206 | 0.6364 | |
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| 0.5183 | 2.77 | 2900 | 0.9150 | 0.6392 | |
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| 0.5369 | 2.87 | 3000 | 0.9200 | 0.6340 | |
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| 0.5105 | 2.96 | 3100 | 0.9243 | 0.6364 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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