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--- |
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library_name: transformers |
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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: visobert_v1 |
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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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# visobert_v1 |
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This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4766 |
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- Accuracy: 0.9337 |
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- Precision Macro: 0.8527 |
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- Recall Macro: 0.8055 |
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- F1 Macro: 0.8251 |
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- F1 Weighted: 0.9316 |
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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: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:| |
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| 0.377 | 1.0 | 90 | 0.2037 | 0.9406 | 0.9012 | 0.7694 | 0.8068 | 0.9350 | |
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| 0.1659 | 2.0 | 180 | 0.2094 | 0.9356 | 0.8396 | 0.8232 | 0.8309 | 0.9348 | |
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| 0.0966 | 3.0 | 270 | 0.2278 | 0.9381 | 0.8463 | 0.8165 | 0.8298 | 0.9367 | |
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| 0.0696 | 4.0 | 360 | 0.2619 | 0.9318 | 0.8438 | 0.7756 | 0.8003 | 0.9280 | |
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| 0.0468 | 5.0 | 450 | 0.3120 | 0.9324 | 0.8362 | 0.8128 | 0.8234 | 0.9313 | |
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| 0.0337 | 6.0 | 540 | 0.3576 | 0.9311 | 0.8376 | 0.7912 | 0.8103 | 0.9287 | |
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| 0.0244 | 7.0 | 630 | 0.3796 | 0.9292 | 0.8428 | 0.7816 | 0.8051 | 0.9261 | |
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| 0.019 | 8.0 | 720 | 0.4309 | 0.9349 | 0.8612 | 0.8070 | 0.8286 | 0.9327 | |
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| 0.0094 | 9.0 | 810 | 0.4022 | 0.9337 | 0.8565 | 0.8134 | 0.8318 | 0.9319 | |
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| 0.0098 | 10.0 | 900 | 0.4181 | 0.9349 | 0.8534 | 0.8062 | 0.8259 | 0.9329 | |
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| 0.0039 | 11.0 | 990 | 0.4484 | 0.9330 | 0.8542 | 0.8091 | 0.8281 | 0.9311 | |
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| 0.0028 | 12.0 | 1080 | 0.4580 | 0.9349 | 0.8554 | 0.8106 | 0.8294 | 0.9330 | |
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| 0.0028 | 13.0 | 1170 | 0.4554 | 0.9318 | 0.8613 | 0.7998 | 0.8242 | 0.9292 | |
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| 0.0031 | 14.0 | 1260 | 0.4575 | 0.9330 | 0.8579 | 0.8009 | 0.8237 | 0.9306 | |
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| 0.0018 | 15.0 | 1350 | 0.4547 | 0.9356 | 0.8617 | 0.8068 | 0.8291 | 0.9333 | |
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| 0.0004 | 16.0 | 1440 | 0.4631 | 0.9343 | 0.8455 | 0.8182 | 0.8305 | 0.9331 | |
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| 0.0006 | 17.0 | 1530 | 0.4642 | 0.9356 | 0.8542 | 0.8152 | 0.8319 | 0.9339 | |
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| 0.0008 | 18.0 | 1620 | 0.4736 | 0.9343 | 0.8534 | 0.8141 | 0.8311 | 0.9326 | |
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| 0.0014 | 19.0 | 1710 | 0.4753 | 0.9337 | 0.8527 | 0.8055 | 0.8251 | 0.9316 | |
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| 0.0009 | 20.0 | 1800 | 0.4766 | 0.9337 | 0.8527 | 0.8055 | 0.8251 | 0.9316 | |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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