Text Classification
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distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ningrumdaud/distilbert-small-offensive-classification-test2025")
model = AutoModelForSequenceClassification.from_pretrained("ningrumdaud/distilbert-small-offensive-classification-test2025")Quick Links
distilbert-small-offensive-classification-test2025
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset.
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 80 | 0.6683 | 0.6125 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ningrumdaud/distilbert-small-offensive-classification-test2025")