Text Classification
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PyTorch
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distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dafqi/DistilBERT-Hoax-Detection")
model = AutoModelForSequenceClassification.from_pretrained("dafqi/DistilBERT-Hoax-Detection")Quick Links
DistilBERT-Hoax-Detection
This model is a fine-tuned version of cahya/distilbert-base-indonesian on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5261
- Accuracy: 0.8441
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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6644 | 1.0 | 93 | 0.6368 | 0.6237 |
| 0.4151 | 2.0 | 186 | 0.5300 | 0.7258 |
| 0.3645 | 3.0 | 279 | 0.5003 | 0.7688 |
| 0.3283 | 4.0 | 372 | 0.4585 | 0.7957 |
| 0.2557 | 5.0 | 465 | 0.4599 | 0.8065 |
| 0.3993 | 6.0 | 558 | 0.5004 | 0.8065 |
| 0.0536 | 7.0 | 651 | 0.4658 | 0.8387 |
| 0.1944 | 8.0 | 744 | 0.5264 | 0.8280 |
| 0.0612 | 9.0 | 837 | 0.5195 | 0.8387 |
| 0.0602 | 10.0 | 930 | 0.5261 | 0.8441 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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Model tree for dafqi/DistilBERT-Hoax-Detection
Base model
cahya/distilbert-base-indonesian
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dafqi/DistilBERT-Hoax-Detection")