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 best_enhanced_progressive_amd.pth from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/best_enhanced_progressive_amd.pth
- Command line
-
hf download hf://Adya662/bert-tiny-amd/best_enhanced_progressive_amd.pth
-
curl -L -o best_enhanced_progressive_amd.pth https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/best_enhanced_progressive_amd.pth
17.6 MB
- Xet hash:
- 0e0a44b79031d869968e34e539883bfdf1ee0e6c565ab716c55a9f5be91897e7
- Size of remote file:
- 17.6 MB
- SHA256:
- 5f8c3c949e8963d27748803fe785af04652da64704533cfcdcdeae7505f0d328
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