Text Classification
Transformers
ONNX
Safetensors
English
reflex_decision
feature-extraction
dual-encoder
multiple-choice
dynamic-labels
research
custom_code
reflex
Eval Results (legacy)
Instructions to use gai-labs/reflex-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gai-labs/reflex-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gai-labs/reflex-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gai-labs/reflex-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download context/tokenizer.json from gai-labs/reflex-1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/gai-labs/reflex-1/resolve/main/context/tokenizer.json
- Command line
-
hf download hf://gai-labs/reflex-1/context/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gai-labs/reflex-1/resolve/main/context/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.