---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-reg-crossencoder-mse
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-reg-crossencoder-mse

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Mse: 0.0752
- Mae: 0.2120
- Pearson Corr: 0.3937
- Spearman Corr: 0.3178
- Cosine Sim: 0.9163

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | Pearson Corr | Spearman Corr | Cosine Sim |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------------:|:-------------:|:----------:|
| 0.1034        | 1.0   | 41   | 0.0704          | 0.0704 | 0.2198 | 0.1914       | 0.2429        | 0.9070     |
| 0.097         | 2.0   | 82   | 0.0739          | 0.0739 | 0.2161 | 0.2185       | 0.2208        | 0.9059     |
| 0.0877        | 3.0   | 123  | 0.0663          | 0.0663 | 0.2154 | 0.3214       | 0.2426        | 0.9133     |
| 0.0679        | 4.0   | 164  | 0.0723          | 0.0723 | 0.2054 | 0.3722       | 0.3382        | 0.9175     |
| 0.0569        | 5.0   | 205  | 0.0644          | 0.0644 | 0.2058 | 0.3867       | 0.3552        | 0.9155     |
| 0.0408        | 6.0   | 246  | 0.0773          | 0.0773 | 0.2102 | 0.4045       | 0.3105        | 0.9190     |
| 0.0317        | 7.0   | 287  | 0.0752          | 0.0752 | 0.2120 | 0.3937       | 0.3178        | 0.9163     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1