Text Classification
Transformers
PyTorch
Safetensors
bert
voicemail-detection
text-embeddings-inference
Instructions to use Adya662/bert-tiny-amd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adya662/bert-tiny-amd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adya662/bert-tiny-amd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adya662/bert-tiny-amd") model = AutoModelForSequenceClassification.from_pretrained("Adya662/bert-tiny-amd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 626 Bytes
e14b87b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"model_name": "prajjwal1/bert-tiny",
"max_length": 128,
"optimal_threshold": 0.11530892550945282,
"val_metrics": {
"accuracy": 0.9702797202797203,
"precision": 0.9004739336492891,
"recall": 0.979381443298969,
"f1": 0.9382716049382717
},
"test_metrics": {
"accuracy": 0.8353344768439108,
"precision": 0.6678445229681979,
"recall": 0.9895287958115183,
"f1": 0.7974683544303798,
"confusion_matrix": [
[
298,
94
],
[
2,
189
]
]
},
"dropout_rate": 0.2,
"training_date": "2025-10-04 02:43:10",
"hidden_size": 128
} |