Document Question Answering
Transformers
PyTorch
English
document-processing
ocr
ner
text-classification
information-extraction
invoice
receipt
form
Instructions to use mrrobot2610/IDP-Machine-learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrrobot2610/IDP-Machine-learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="mrrobot2610/IDP-Machine-learning")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrrobot2610/IDP-Machine-learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download frontend/components/ui/GradientText.tsx from mrrobot2610/IDP-Machine-learning: direct link, hf CLI and curl.
- Browser
- Download file 763 Bytes
-
https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/frontend/components/ui/GradientText.tsx
- Command line
-
hf download hf://mrrobot2610/IDP-Machine-learning/frontend/components/ui/GradientText.tsx
-
curl -L -o GradientText.tsx https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/frontend/components/ui/GradientText.tsx
763 Bytes
| 'use client' | |
| import { motion } from 'framer-motion' | |
| import { cn } from '@/lib/utils' | |
| interface GradientTextProps { | |
| children: React.ReactNode | |
| className?: string | |
| } | |
| export default function GradientText({ children, className }: GradientTextProps) { | |
| return ( | |
| <motion.span | |
| initial={{ backgroundPosition: '0% 50%' }} | |
| animate={{ backgroundPosition: '100% 50%' }} | |
| transition={{ duration: 5, repeat: Infinity, repeatType: 'reverse', ease: 'linear' }} | |
| className={cn( | |
| "bg-clip-text text-transparent bg-gradient-to-r from-indigo-600 via-purple-600 to-cyan-600 bg-[length:200%_auto]", | |
| className | |
| )} | |
| > | |
| {children} | |
| </motion.span> | |
| ) | |
| } | |