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
File size: 371 Bytes
f7cb4b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Exact versions used to build and validate this release on Python 3.13 / macOS arm64. # Runtime packages for other platforms may need different supported versions. torch==2.14.0 transformers==5.17.0 tokenizers==0.23.2 huggingface-hub==1.31.0 safetensors==0.8.0 numpy==2.5.3 onnx==1.23.0 onnxruntime==1.30.0 onnxconverter-common==1.16.0 protobuf==7.36.2 ml-dtypes==0.6.0 |