Zero-Shot Classification
Transformers
GGUF
English
decision-model
system-one
falcondec
calibrated-decisions
multiple-choice
intent-classification
customer-support
natural-language-inference
code
guardrails
selective-prediction
falconsai
model-surgeon
attested-lineage
Instructions to use Falconsai/proof_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/proof_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Falconsai/proof_v3")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Falconsai/proof_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer.json from Falconsai/proof_v3: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/Falconsai/proof_v3/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://Falconsai/proof_v3/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Falconsai/proof_v3/resolve/main/tokenizer/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.