Text Classification
Transformers
ONNX
Safetensors
English
roberta
editlens
ai-detection
quantization
local-inference
text-embeddings-inference
Instructions to use CoderBak/editlens_roberta_modelkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CoderBak/editlens_roberta_modelkit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CoderBak/editlens_roberta_modelkit")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CoderBak/editlens_roberta_modelkit") model = AutoModelForSequenceClassification.from_pretrained("CoderBak/editlens_roberta_modelkit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_int8.onnx from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 514 MB
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/onnx/model_int8.onnx
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/onnx/model_int8.onnx
-
curl -L -o model_int8.onnx https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/onnx/model_int8.onnx
514 MB
- Xet hash:
- 4dc75450950a6c1c54747bbfeb9ba452eb710738bbbf7809f0b9889150c28a3a
- Size of remote file:
- 514 MB
- SHA256:
- bc1b9cb5a7a63fb7c5b67de3556e9e43cb4537bd6ce6bae3e6ad7cfce9552d23
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