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 requirements-build.txt from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 371 Bytes
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/requirements-build.txt
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/requirements-build.txt
-
curl -L -o requirements-build.txt https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/requirements-build.txt
371 Bytes
| # 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 | |