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 pytorch_model.bin from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 18.5 MB
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Adya662/bert-tiny-amd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/pytorch_model.bin
18.5 MB
- Xet hash:
- 221fe70b9d455dfc265b06beba55bac2513ca3c9ef00e037aaa8b1a947bff44a
- Size of remote file:
- 18.5 MB
- SHA256:
- ad78e66f593f90cb4c185ca79d87eff358815a94e8fbc373278971a9ae9eec37
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