---
license: apache-2.0
base_model: distilbert-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
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# bert-based_uncased-finetuned-binary_hate_speech

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4500
- Accuracy: 0.8179
- F1: 0.8255
- Precision: 0.7924
- Recall: 0.8614

## 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: 1e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4844        | 1.0   | 1941 | 0.4541          | 0.8002   | 0.8043 | 0.7877    | 0.8217 |
| 0.3952        | 2.0   | 3882 | 0.4360          | 0.8138   | 0.8184 | 0.7985    | 0.8393 |
| 0.3438        | 3.0   | 5823 | 0.4500          | 0.8179   | 0.8255 | 0.7924    | 0.8614 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1