Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use MiKinSwan/fake-review-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiKinSwan/fake-review-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiKinSwan/fake-review-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiKinSwan/fake-review-detector") model = AutoModelForSequenceClassification.from_pretrained("MiKinSwan/fake-review-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fake-review-detector
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0940
- Accuracy: 0.9768
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1116 | 1.0 | 885 | 0.0629 | 0.9785 |
| 0.0240 | 2.0 | 1770 | 0.0940 | 0.9768 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2
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Model tree for MiKinSwan/fake-review-detector
Base model
distilbert/distilbert-base-uncased