Text Classification
Transformers
ONNX
English
roberta
int8
optimum
multi-class-classification
ONNXRuntime
text-embeddings-inference
Instructions to use minuva/MiniLMv2-userflow-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-userflow-v2-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-userflow-v2-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-userflow-v2-onnx") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-userflow-v2-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_optimized_quantized.onnx from minuva/MiniLMv2-userflow-v2-onnx: direct link, hf CLI and curl.
- Browser
- Download file 30.5 MB
-
https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx/resolve/main/model_optimized_quantized.onnx
- Command line
-
hf download hf://minuva/MiniLMv2-userflow-v2-onnx/model_optimized_quantized.onnx
-
curl -L -o model_optimized_quantized.onnx https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx/resolve/main/model_optimized_quantized.onnx
30.5 MB
- Xet hash:
- 2e25817a7755ea19652ee054adce0e13a8985bd1045f15b2b78e30c53cf88051
- Size of remote file:
- 30.5 MB
- SHA256:
- 1f0228740a10c2c7788ee554be779a63aa246fee20736b9a1d713660e41a78f3
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