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 models/classifier/metadata.json from mrrobot2610/IDP-Machine-learning: direct link, hf CLI and curl.
- Browser
- Download file 298 Bytes
-
https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/models/classifier/metadata.json
- Command line
-
hf download hf://mrrobot2610/IDP-Machine-learning/models/classifier/metadata.json
-
curl -L -o metadata.json https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/models/classifier/metadata.json
298 Bytes
| { | |
| "model_name": "nreimers/MiniLM-L6-H384-uncased", | |
| "num_labels": 4, | |
| "label2id": { | |
| "INVOICE": 0, | |
| "RECEIPT": 1, | |
| "FORM": 2, | |
| "OTHER": 3 | |
| }, | |
| "id2label": { | |
| "0": "INVOICE", | |
| "1": "RECEIPT", | |
| "2": "FORM", | |
| "3": "OTHER" | |
| }, | |
| "best_val_accuracy": 1.0 | |
| } |