metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- hate_speech18
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned_on_hata_dateset
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: hate_speech18
type: hate_speech18
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9243697478991597
- name: F1
type: f1
value: 0.9223806548670374
distilbert-base-uncased-finetuned_on_hata_dateset
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the hate_speech18 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2221
- Accuracy: 0.9244
- F1: 0.9224
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|
| 0.2718 | 1.0 | 268 | 0.2050 | 0.9104 | 0.9070 |
| 0.1697 | 2.0 | 536 | 0.2039 | 0.9188 | 0.9094 |
| 0.1143 | 3.0 | 804 | 0.2221 | 0.9244 | 0.9224 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1