transformers safetensors distilbert text-classification generated_from_trainer dataset:hatexplain base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- hatexplain
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-hatexplain
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: hatexplain
      type: hatexplain
      config: plain_text
      split: validation
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.6990644490644491
      name: Accuracy
    - type: precision
      value: 0.6974890380019948
      name: Precision
    - type: recall
      value: 0.6990644490644491
      name: Recall
    - type: f1
      value: 0.6978790945993021
      name: F1
---

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# distilbert-hatexplain

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hatexplain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8165
- Accuracy: 0.6991
- Precision: 0.6975
- Recall: 0.6991
- F1: 0.6979

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6131        | 1.0   | 1923 | 0.7399          | 0.6925   | 0.6877    | 0.6925 | 0.6847 |
| 0.7386        | 2.0   | 3846 | 0.7254          | 0.7040   | 0.7033    | 0.7040 | 0.7036 |
| 0.6471        | 3.0   | 5769 | 0.8259          | 0.7019   | 0.6995    | 0.7019 | 0.7005 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0
