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")# 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-runtime.txt from CoderBak/editlens_roberta_modelkit: direct link, hf CLI and curl.
- Browser
- Download file 192 Bytes
-
https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/requirements-runtime.txt
- Command line
-
hf download hf://CoderBak/editlens_roberta_modelkit/requirements-runtime.txt
-
curl -L -o requirements-runtime.txt https://huggingface.co/CoderBak/editlens_roberta_modelkit/resolve/main/requirements-runtime.txt
192 Bytes
| # Validated ONNX CPU example environment; no torch/transformers required. | |
| # These versions have platform-specific availability; see README. | |
| onnxruntime==1.30.0 | |
| tokenizers==0.23.2 | |
| numpy==2.5.3 | |