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---
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
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