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 rule_based_vs_bert_comparison.png from Adya662/bert-tiny-amd: direct link, hf CLI and curl.
- Browser
- Download file 428 kB
-
https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/rule_based_vs_bert_comparison.png
- Command line
-
hf download hf://Adya662/bert-tiny-amd/rule_based_vs_bert_comparison.png
-
curl -L -o rule_based_vs_bert_comparison.png https://huggingface.co/Adya662/bert-tiny-amd/resolve/main/rule_based_vs_bert_comparison.png
428 kB

- Xet hash:
- bd240aca69802e7688840abda36d6a12609eb89eed9074146e2313da69922eda
- Size of remote file:
- 428 kB
- SHA256:
- 4e0fc7295dfe5c2be6696dcb223771ff37dd2b9992f166ed6b791c437fc506c7
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