---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: hatespeech_distilbert
  results: []
widget:
- text: "Democrats using African-Americans again."
  example_title: "Non-Hate Speech Example"
- text: "Holy fuck this girl's trash, what a cunt."
  example_title: "Hate Speech Example"
---

<!-- 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. -->

# hatespeech_distilbert

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: 0.9977
- Accuracy: 0.7737
- Recall: 0.8118
- Precision: 0.7526
- F1: 0.7811


And the following results on the test set:
- Loss: 1.0640
- Accuracy: 0.7544
- Recall: 0.7930
- Precision: 0.7406
- F1: 0.7659

## 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: 8e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4863        | 0.9935 | 77   | 0.4678          | 0.7701   | 0.7421 | 0.7841    | 0.7625 |
| 0.3935        | 2.0    | 155  | 0.4595          | 0.7834   | 0.7340 | 0.8124    | 0.7712 |
| 0.2792        | 2.9935 | 232  | 0.5285          | 0.7850   | 0.7291 | 0.8188    | 0.7713 |
| 0.1408        | 4.0    | 310  | 0.7130          | 0.7785   | 0.7940 | 0.7684    | 0.7810 |
| 0.0945        | 4.9935 | 387  | 0.8230          | 0.7806   | 0.7551 | 0.7937    | 0.7739 |
| 0.0541        | 6.0    | 465  | 0.9977          | 0.7737   | 0.8118 | 0.7526    | 0.7811 |
| 0.0331        | 6.9935 | 542  | 1.1107          | 0.7753   | 0.7859 | 0.7678    | 0.7768 |
| 0.0151        | 8.0    | 620  | 1.1703          | 0.7789   | 0.7543 | 0.7915    | 0.7724 |
| 0.0106        | 8.9935 | 697  | 1.2741          | 0.7785   | 0.7616 | 0.7864    | 0.7738 |
| 0.0051        | 9.9355 | 770  | 1.2964          | 0.7753   | 0.7851 | 0.7683    | 0.7766 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1