Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("chingoduc/spam-classifier")
model = AutoModelForSequenceClassification.from_pretrained("chingoduc/spam-classifier")Quick Links
spam-classifier
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: 0.0539
- Accuracy: 0.9914
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 262 | 0.0424 | 0.9899 |
| 0.053 | 2.0 | 524 | 0.0473 | 0.9914 |
| 0.053 | 3.0 | 786 | 0.0497 | 0.9907 |
| 0.0062 | 4.0 | 1048 | 0.0522 | 0.9914 |
| 0.0062 | 5.0 | 1310 | 0.0539 | 0.9914 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chingoduc/spam-classifier")