Text Classification
Transformers
Safetensors
GGUF
English
modernbert
feature-extraction
falconsproof
intfalcon
decision-model
zero-shot-classification
multiple-choice
intent-classification
customer-support
code
code-understanding
natural-language-inference
knowledge-distillation
cross-encoder
calibration
selective-prediction
falconsai
model-surgeon
attested-lineage
text-embeddings-inference
Instructions to use Falconsai/proof_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/proof_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Falconsai/proof_v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Falconsai/proof_v2") model = AutoModel.from_pretrained("Falconsai/proof_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Falconsai/proof_v2: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/Falconsai/proof_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Falconsai/proof_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Falconsai/proof_v2/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.