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 training_metadata.json from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 740 Bytes
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/training_metadata.json
- Command line
-
hf download hf://Adya662/bert-tiny-amd/training_metadata.json
-
curl -L -o training_metadata.json https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/training_metadata.json
740 Bytes
| { | |
| "model_name": "bert-tiny-amd", | |
| "base_model": "prajjwal1/bert-tiny", | |
| "task": "text-classification", | |
| "dataset": "ElevateNow call center transcripts", | |
| "language": "en", | |
| "license": "mit", | |
| "pipeline_tag": "text-classification", | |
| "tags": [ | |
| "text-classification", | |
| "answering-machine-detection", | |
| "bert-tiny", | |
| "binary-classification", | |
| "call-center", | |
| "voice-processing" | |
| ], | |
| "performance": { | |
| "validation_accuracy": 0.9394, | |
| "precision": 0.9275, | |
| "recall": 0.8727, | |
| "f1_score": 0.8993 | |
| }, | |
| "training_details": { | |
| "total_samples": 3548, | |
| "training_samples": 2838, | |
| "validation_samples": 710, | |
| "epochs": 15, | |
| "batch_size": 32, | |
| "learning_rate": 3e-05, | |
| "device": "mps" | |
| } | |
| } |