Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use saberbounehas/Toxic-Not-Toxic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saberbounehas/Toxic-Not-Toxic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saberbounehas/Toxic-Not-Toxic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("saberbounehas/Toxic-Not-Toxic") model = AutoModelForSequenceClassification.from_pretrained("saberbounehas/Toxic-Not-Toxic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Toxic-Not-Toxic
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0169
- Accuracy: 0.91
- Precision: 0.9301
- Recall: 0.8867
- F1: 0.9078
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0000 | 1.0 | 88 | 1.1921 | 0.8733 | 0.8077 | 0.98 | 0.8855 |
| 0.0000 | 2.0 | 176 | 0.9924 | 0.8933 | 0.9538 | 0.8267 | 0.8857 |
| 0.0000 | 3.0 | 264 | 0.8227 | 0.9267 | 0.9156 | 0.94 | 0.9276 |
| 0.0000 | 4.0 | 352 | 0.8310 | 0.9267 | 0.9156 | 0.94 | 0.9276 |
| 0.0000 | 5.0 | 440 | 0.8379 | 0.9267 | 0.9156 | 0.94 | 0.9276 |
| 0.0000 | 6.0 | 528 | 0.8446 | 0.9267 | 0.9156 | 0.94 | 0.9276 |
| 0.0000 | 7.0 | 616 | 1.0147 | 0.91 | 0.9301 | 0.8867 | 0.9078 |
| 0.0000 | 8.0 | 704 | 1.0174 | 0.91 | 0.9301 | 0.8867 | 0.9078 |
| 0.0000 | 9.0 | 792 | 1.0165 | 0.91 | 0.9301 | 0.8867 | 0.9078 |
| 0.0000 | 10.0 | 880 | 1.0169 | 0.91 | 0.9301 | 0.8867 | 0.9078 |
Framework versions
- Transformers 5.19.0
- Pytorch 2.14.1
- Datasets 5.1.0
- Tokenizers 0.23.2
- Downloads last month
- 24
Model tree for saberbounehas/Toxic-Not-Toxic
Base model
distilbert/distilroberta-base