---
library_name: transformers
license: apache-2.0
base_model: deepvk/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-spam-detection
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-uncased-finetuned-spam-detection

This model is a fine-tuned version of [deepvk/bert-base-uncased](https://huggingface.co/deepvk/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0508
- Accuracy: 0.9862
- F1: 0.9862

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0626        | 1.0   | 967  | 0.0504          | 0.9838   | 0.9838 |
| 0.0258        | 2.0   | 1934 | 0.0508          | 0.9862   | 0.9862 |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.4.1
- Datasets 3.0.2
- Tokenizers 0.20.1