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--- |
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library_name: transformers |
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license: mit |
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base_model: deepset/gbert-large |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5650 |
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- Accuracy: 0.8403 |
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- F1: 0.8328 |
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- Precision: 0.8416 |
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- Recall: 0.8403 |
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- F1 Macro: 0.6886 |
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- Precision Macro: 0.6871 |
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- Recall Macro: 0.7119 |
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- F1 Micro: 0.8403 |
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- Precision Micro: 0.8403 |
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- Recall Micro: 0.8403 |
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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: 20 |
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- eval_batch_size: 20 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 80 |
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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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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 5 |
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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 | F1 | Precision | Recall | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:| |
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| 3.1687 | 0.3891 | 100 | 1.7221 | 0.6317 | 0.5563 | 0.5358 | 0.6317 | 0.2779 | 0.2937 | 0.2938 | 0.6317 | 0.6317 | 0.6317 | |
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| 1.2239 | 0.7782 | 200 | 0.8836 | 0.7856 | 0.7633 | 0.7696 | 0.7856 | 0.5175 | 0.5077 | 0.5567 | 0.7856 | 0.7856 | 0.7856 | |
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| 0.7758 | 1.1673 | 300 | 0.7089 | 0.8107 | 0.7922 | 0.7939 | 0.8107 | 0.5917 | 0.5889 | 0.6185 | 0.8107 | 0.8107 | 0.8107 | |
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| 0.6436 | 1.5564 | 400 | 0.6498 | 0.8250 | 0.8136 | 0.8220 | 0.8250 | 0.6330 | 0.6331 | 0.6563 | 0.8250 | 0.8250 | 0.8250 | |
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| 0.5815 | 1.9455 | 500 | 0.6037 | 0.8300 | 0.8227 | 0.8338 | 0.8300 | 0.6583 | 0.6478 | 0.6890 | 0.8300 | 0.8300 | 0.8300 | |
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| 0.4695 | 2.3346 | 600 | 0.5771 | 0.8389 | 0.8319 | 0.8409 | 0.8389 | 0.6729 | 0.6688 | 0.6984 | 0.8389 | 0.8389 | 0.8389 | |
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| 0.4336 | 2.7237 | 700 | 0.5724 | 0.8362 | 0.8280 | 0.8395 | 0.8362 | 0.6753 | 0.6682 | 0.7038 | 0.8362 | 0.8362 | 0.8362 | |
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| 0.4135 | 3.1128 | 800 | 0.5650 | 0.8403 | 0.8328 | 0.8416 | 0.8403 | 0.6886 | 0.6871 | 0.7119 | 0.8403 | 0.8403 | 0.8403 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.20.3 |
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