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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bert-crossencoder-kl_divergence |
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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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# bert-crossencoder-kl_divergence |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9919 |
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- Accuracy: 0.6084 |
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- Precision: 0.6124 |
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- Recall: 0.6084 |
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- F1: 0.6099 |
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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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- 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: 100 |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.282 | 1.0 | 78 | 1.2172 | 0.4951 | 0.3948 | 0.4951 | 0.4061 | |
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| 1.0377 | 2.0 | 156 | 1.0246 | 0.5793 | 0.6114 | 0.5793 | 0.5550 | |
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| 0.9037 | 3.0 | 234 | 0.9440 | 0.6084 | 0.6178 | 0.6084 | 0.6015 | |
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| 0.7861 | 4.0 | 312 | 0.9381 | 0.6343 | 0.6425 | 0.6343 | 0.6356 | |
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| 0.5607 | 5.0 | 390 | 0.9718 | 0.6052 | 0.6114 | 0.6052 | 0.6034 | |
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| 0.4532 | 6.0 | 468 | 0.9680 | 0.6278 | 0.6290 | 0.6278 | 0.6275 | |
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| 0.3763 | 7.0 | 546 | 0.9919 | 0.6084 | 0.6124 | 0.6084 | 0.6099 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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