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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: flausch_span_gbert-large_all |
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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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# flausch_span_gbert-large_all |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2622 |
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- Model Preparation Time: 0.0328 |
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- Precision: 0.4348 |
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- Recall: 0.5821 |
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- F1: 0.4978 |
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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: Use OptimizerNames.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: 3 |
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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 | Model Preparation Time | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:----------------------:|:---------:|:------:|:------:| |
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| 0.9007 | 0.2822 | 500 | 0.7977 | 0.0328 | 0.0 | 0.0 | 0.0 | |
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| 0.7223 | 0.5643 | 1000 | 0.6214 | 0.0328 | 0.2047 | 0.1514 | 0.1741 | |
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| 0.5327 | 0.8465 | 1500 | 0.4041 | 0.0328 | 0.2262 | 0.3583 | 0.2773 | |
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| 0.3691 | 1.1287 | 2000 | 0.3349 | 0.0328 | 0.2819 | 0.4330 | 0.3415 | |
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| 0.3021 | 1.4108 | 2500 | 0.3013 | 0.0328 | 0.3222 | 0.4524 | 0.3764 | |
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| 0.2548 | 1.6930 | 3000 | 0.2655 | 0.0328 | 0.3821 | 0.5263 | 0.4428 | |
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| 0.2744 | 1.9752 | 3500 | 0.2666 | 0.0328 | 0.3072 | 0.4037 | 0.3489 | |
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| 0.1874 | 2.2573 | 4000 | 0.2803 | 0.0328 | 0.4124 | 0.5461 | 0.4700 | |
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| 0.1767 | 2.5395 | 4500 | 0.2625 | 0.0328 | 0.4421 | 0.5802 | 0.5018 | |
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| 0.1708 | 2.8217 | 5000 | 0.2622 | 0.0328 | 0.4348 | 0.5821 | 0.4978 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.1 |
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