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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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model-index: |
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- name: task2_flausch_classification_gbert-large_span_classifier_with_nonspan |
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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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# task2_flausch_classification_gbert-large_span_classifier_with_nonspan |
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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: 0.2307 |
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- Accuracy: 0.9479 |
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- F1: 0.9467 |
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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 | Accuracy | F1 | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:| |
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| 0.3749 | 0.2697 | 1000 | 0.3099 | 0.9149 | 0.9174 | |
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| 0.2804 | 0.5394 | 2000 | 0.2330 | 0.9371 | 0.9305 | |
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| 0.2672 | 0.8091 | 3000 | 0.2369 | 0.9391 | 0.9339 | |
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| 0.2248 | 1.0787 | 4000 | 0.2295 | 0.9427 | 0.9406 | |
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| 0.1942 | 1.3484 | 5000 | 0.2244 | 0.9452 | 0.9422 | |
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| 0.1873 | 1.6181 | 6000 | 0.2310 | 0.9423 | 0.9393 | |
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| 0.1768 | 1.8878 | 7000 | 0.2155 | 0.9469 | 0.9450 | |
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| 0.1443 | 2.1575 | 8000 | 0.2295 | 0.9454 | 0.9449 | |
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| 0.1198 | 2.4272 | 9000 | 0.2295 | 0.9484 | 0.9474 | |
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| 0.1238 | 2.6969 | 10000 | 0.2278 | 0.9479 | 0.9469 | |
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| 0.1166 | 2.9666 | 11000 | 0.2307 | 0.9479 | 0.9467 | |
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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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