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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: gbert_synset_classifier_amdi_tiny |
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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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# gbert_synset_classifier_amdi_tiny |
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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: 0.6469 |
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- Accuracy: 0.8376 |
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- F1: 0.8366 |
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- Precision: 0.8446 |
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- Recall: 0.8376 |
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- F1 Macro: 0.8202 |
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- Precision Macro: 0.7900 |
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- Recall Macro: 0.8625 |
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- F1 Micro: 0.8376 |
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- Precision Micro: 0.8376 |
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- Recall Micro: 0.8376 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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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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| 2.7014 | 0.8584 | 100 | 1.1834 | 0.6919 | 0.6499 | 0.6573 | 0.6919 | 0.4964 | 0.5670 | 0.4997 | 0.6919 | 0.6919 | 0.6919 | |
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| 0.7696 | 1.7167 | 200 | 0.5970 | 0.8241 | 0.8188 | 0.8227 | 0.8241 | 0.7784 | 0.7603 | 0.8062 | 0.8241 | 0.8241 | 0.8241 | |
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| 0.4739 | 2.5751 | 300 | 0.5408 | 0.8321 | 0.8290 | 0.8381 | 0.8321 | 0.8082 | 0.7815 | 0.8508 | 0.8321 | 0.8321 | 0.8321 | |
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| 0.3743 | 3.4335 | 400 | 0.5343 | 0.8426 | 0.8385 | 0.8480 | 0.8426 | 0.8269 | 0.8046 | 0.8598 | 0.8426 | 0.8426 | 0.8426 | |
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| 0.2931 | 4.2918 | 500 | 0.5188 | 0.8469 | 0.8465 | 0.8516 | 0.8469 | 0.8312 | 0.8133 | 0.8552 | 0.8469 | 0.8469 | 0.8469 | |
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| 0.2175 | 5.1502 | 600 | 0.5697 | 0.8426 | 0.8419 | 0.8506 | 0.8426 | 0.8295 | 0.8093 | 0.8572 | 0.8426 | 0.8426 | 0.8426 | |
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| 0.1689 | 6.0086 | 700 | 0.5781 | 0.8426 | 0.8421 | 0.8470 | 0.8426 | 0.8322 | 0.8164 | 0.8540 | 0.8426 | 0.8426 | 0.8426 | |
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| 0.1174 | 6.8670 | 800 | 0.6469 | 0.8376 | 0.8366 | 0.8446 | 0.8376 | 0.8202 | 0.7900 | 0.8625 | 0.8376 | 0.8376 | 0.8376 | |
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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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