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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-mae |
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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-mae |
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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: 0.2200 |
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- Mse: 0.0781 |
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- Mae: 0.2200 |
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- Pearson Corr: 0.3461 |
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- Spearman Corr: 0.3129 |
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- Cosine Sim: 0.9050 |
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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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| 0.2886 | 1.0 | 41 | 0.2213 | 0.0742 | 0.2213 | 0.0650 | 0.0604 | 0.9037 | |
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| 0.2582 | 2.0 | 82 | 0.2223 | 0.0714 | 0.2223 | 0.1319 | 0.1417 | 0.9052 | |
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| 0.2615 | 3.0 | 123 | 0.2094 | 0.0670 | 0.2094 | 0.2859 | 0.2753 | 0.9113 | |
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| 0.2247 | 4.0 | 164 | 0.2152 | 0.0733 | 0.2152 | 0.3126 | 0.2705 | 0.9075 | |
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| 0.1942 | 5.0 | 205 | 0.2363 | 0.0890 | 0.2363 | 0.3631 | 0.3424 | 0.9112 | |
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| 0.1758 | 6.0 | 246 | 0.2193 | 0.0776 | 0.2193 | 0.3528 | 0.3247 | 0.9106 | |
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| 0.166 | 7.0 | 287 | 0.2200 | 0.0781 | 0.2200 | 0.3461 | 0.3129 | 0.9050 | |
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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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