---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-based_uncased-finetuned-binary_hate_speech
  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-based_uncased-finetuned-binary_hate_speech

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: 0.5093
- Accuracy: 0.8300
- F1: 0.8383
- Precision: 0.7992
- Recall: 0.8815

## 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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4752        | 1.0   | 1941 | 0.4343          | 0.8135   | 0.8171 | 0.8017    | 0.8331 |
| 0.3456        | 2.0   | 3882 | 0.4221          | 0.8305   | 0.8394 | 0.7974    | 0.8861 |
| 0.2434        | 3.0   | 5823 | 0.5093          | 0.8300   | 0.8383 | 0.7992    | 0.8815 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1