Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("godsofheaven/final_model_toxicity_classification")
model = AutoModelForSequenceClassification.from_pretrained("godsofheaven/final_model_toxicity_classification")Quick Links
final_model_toxicity_classification
This model is a fine-tuned version of distilbert-base-uncased on the jigsaw_toxicity_pred dataset. It achieves the following results on the evaluation set:
- Loss: 0.2702
- F1: 0.6776
- Accuracy: 0.921
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: 4.910748967246961e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.0959 | 1.0 | 4987 | 0.1663 | 0.7248 | 0.94 |
| 0.0527 | 2.0 | 9974 | 0.2702 | 0.6776 | 0.921 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for godsofheaven/final_model_toxicity_classification
Base model
distilbert/distilbert-base-uncasedDataset used to train godsofheaven/final_model_toxicity_classification
Evaluation results
- F1 on jigsaw_toxicity_predtest set self-reported0.678
- Accuracy on jigsaw_toxicity_predtest set self-reported0.921
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="godsofheaven/final_model_toxicity_classification")