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.onnx from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/onnx/model.onnx
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/onnx/model.onnx
1.42 GB
- Xet hash:
- 90e3c8500db2a395c108a1ece356179c3333130ebdbc00fde3f5419060c32e49
- Size of remote file:
- 1.42 GB
- SHA256:
- ddd1173f2ef517ad499965e5029fae8099a8054a2bc76d8134e5889cc4ed1b3e
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