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 comprehensive_model_analysis.png from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 559 kB
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/comprehensive_model_analysis.png
- Command line
-
hf download hf://Adya662/bert-tiny-amd/comprehensive_model_analysis.png
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curl -L -o comprehensive_model_analysis.png https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/comprehensive_model_analysis.png
559 kB

- Xet hash:
- 7a54deefb9a0ca181d7fb9a9ac6af12d49e44995f7ee361c7c60af18ea7ab245
- Size of remote file:
- 559 kB
- SHA256:
- e2aaeb9693f9eb222b368ff314cef0871bef070b807367a1f3af5faa32e28a3a
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