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
Download model_metadata.json from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 626 Bytes
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/model_metadata.json
- Command line
-
hf download hf://Adya662/bert-tiny-amd/model_metadata.json
-
curl -L -o model_metadata.json https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/model_metadata.json
626 Bytes
| { | |
| "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 | |
| } |