Instructions to use Ssid7647/pos_bi_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ssid7647/pos_bi_quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ssid7647/pos_bi_quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ssid7647/pos_bi_quantized") model = AutoModelForTokenClassification.from_pretrained("Ssid7647/pos_bi_quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_quantized.onnx from Ssid7647/pos_bi_quantized: direct link, hf CLI and curl.
- Browser
- Download file 127 MB
-
https://huggingface.co/Ssid7647/pos_bi_quantized/resolve/main/model_quantized.onnx
- Command line
-
hf download hf://Ssid7647/pos_bi_quantized/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/Ssid7647/pos_bi_quantized/resolve/main/model_quantized.onnx
127 MB
- Xet hash:
- 794d78a1b39b79350c84f6c087638ee7e29b7e7c92f365464a7c8c0d9230feb1
- Size of remote file:
- 127 MB
- SHA256:
- 5a3a08edd3b8b5948edf1172ed3222eea83ff924418d291df1d45635870c94b2
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