Feature Extraction
Transformers
ONNX
Safetensors
English
bert
retrieval
constbert
colbert
multi-vector
embedding
custom_code
text-embeddings-inference
Instructions to use anubhavg97/constbert-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anubhavg97/constbert-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="anubhavg97/constbert-onnx", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anubhavg97/constbert-onnx", trust_remote_code=True) model = AutoModel.from_pretrained("anubhavg97/constbert-onnx", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from anubhavg97/constbert-onnx: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/anubhavg97/constbert-onnx/resolve/main/model.onnx
- Command line
-
hf download hf://anubhavg97/constbert-onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/anubhavg97/constbert-onnx/resolve/main/model.onnx
436 MB
- Xet hash:
- cf1bf6ce1306e983a51dea6986c62f42c2ef2035ad1518c9afbdbeb360a4901c
- Size of remote file:
- 436 MB
- SHA256:
- d515b85a59a302d13d04b3a45c6211b3e1893a2718c13598231acc18825f0f02
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