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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: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-reg-crossencoder-mse |
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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-reg-crossencoder-mse |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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: 0.0752 |
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- Mse: 0.0752 |
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- Mae: 0.2120 |
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- Pearson Corr: 0.3937 |
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- Spearman Corr: 0.3178 |
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- Cosine Sim: 0.9163 |
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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: 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 | Mse | Mae | Pearson Corr | Spearman Corr | Cosine Sim | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------------:|:-------------:|:----------:| |
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| 0.1034 | 1.0 | 41 | 0.0704 | 0.0704 | 0.2198 | 0.1914 | 0.2429 | 0.9070 | |
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| 0.097 | 2.0 | 82 | 0.0739 | 0.0739 | 0.2161 | 0.2185 | 0.2208 | 0.9059 | |
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| 0.0877 | 3.0 | 123 | 0.0663 | 0.0663 | 0.2154 | 0.3214 | 0.2426 | 0.9133 | |
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| 0.0679 | 4.0 | 164 | 0.0723 | 0.0723 | 0.2054 | 0.3722 | 0.3382 | 0.9175 | |
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| 0.0569 | 5.0 | 205 | 0.0644 | 0.0644 | 0.2058 | 0.3867 | 0.3552 | 0.9155 | |
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| 0.0408 | 6.0 | 246 | 0.0773 | 0.0773 | 0.2102 | 0.4045 | 0.3105 | 0.9190 | |
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| 0.0317 | 7.0 | 287 | 0.0752 | 0.0752 | 0.2120 | 0.3937 | 0.3178 | 0.9163 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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