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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: OR_finetuned_classification
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. -->
# OR_finetuned_classification
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: 0.6007
- F1: 0.6667
- Roc Auc: 0.8095
- Accuracy: 0.6667
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.0016 | 79.0 | 790 | 0.4859 | 0.6667 | 0.8095 | 0.6667 |
| 0.0006 | 158.0 | 1580 | 0.5649 | 0.6667 | 0.8095 | 0.6667 |
| 0.0004 | 237.0 | 2370 | 0.6007 | 0.6667 | 0.8095 | 0.6667 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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