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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_pair |
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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_pair |
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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: 0.4826 |
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- Accuracy: 0.8551 |
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- F1: 0.8501 |
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- Precision: 0.8590 |
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- Recall: 0.8551 |
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- F1 Macro: 0.7419 |
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- Precision Macro: 0.7334 |
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- Recall Macro: 0.7673 |
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- F1 Micro: 0.8551 |
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- Precision Micro: 0.8551 |
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- Recall Micro: 0.8551 |
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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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| 2.7743 | 0.3891 | 100 | 1.0270 | 0.7995 | 0.7723 | 0.7757 | 0.7995 | 0.5034 | 0.5166 | 0.5282 | 0.7995 | 0.7995 | 0.7995 | |
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| 0.8957 | 0.7782 | 200 | 0.6587 | 0.8291 | 0.8176 | 0.8179 | 0.8291 | 0.5871 | 0.5863 | 0.6064 | 0.8291 | 0.8291 | 0.8291 | |
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| 0.6278 | 1.1673 | 300 | 0.5440 | 0.8457 | 0.8363 | 0.8395 | 0.8457 | 0.6407 | 0.6380 | 0.6622 | 0.8457 | 0.8457 | 0.8457 | |
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| 0.5132 | 1.5564 | 400 | 0.5295 | 0.8362 | 0.8275 | 0.8355 | 0.8362 | 0.6593 | 0.6525 | 0.6857 | 0.8362 | 0.8362 | 0.8362 | |
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| 0.4843 | 1.9455 | 500 | 0.4930 | 0.8511 | 0.8439 | 0.8500 | 0.8511 | 0.6777 | 0.6675 | 0.7028 | 0.8511 | 0.8511 | 0.8511 | |
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| 0.39 | 2.3346 | 600 | 0.4827 | 0.8564 | 0.8521 | 0.8555 | 0.8564 | 0.7073 | 0.6989 | 0.7287 | 0.8564 | 0.8564 | 0.8564 | |
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| 0.3536 | 2.7237 | 700 | 0.4818 | 0.8551 | 0.8492 | 0.8576 | 0.8551 | 0.7314 | 0.7421 | 0.7476 | 0.8551 | 0.8551 | 0.8551 | |
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| 0.3462 | 3.1128 | 800 | 0.4826 | 0.8551 | 0.8501 | 0.8590 | 0.8551 | 0.7419 | 0.7334 | 0.7673 | 0.8551 | 0.8551 | 0.8551 | |
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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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