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/ner/metadata.json from mrrobot2610/IDP-Machine-learning: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/models/ner/metadata.json
- Command line
-
hf download hf://mrrobot2610/IDP-Machine-learning/models/ner/metadata.json
-
curl -L -o metadata.json https://huggingface.co/mrrobot2610/IDP-Machine-learning/resolve/main/models/ner/metadata.json
1.25 kB
| { | |
| "model_name": "distilbert-base-uncased", | |
| "num_labels": 23, | |
| "label2id": { | |
| "O": 0, | |
| "B-INVOICE_NUMBER": 1, | |
| "I-INVOICE_NUMBER": 2, | |
| "B-DATE": 3, | |
| "I-DATE": 4, | |
| "B-TOTAL_AMOUNT": 5, | |
| "I-TOTAL_AMOUNT": 6, | |
| "B-TAX_AMOUNT": 7, | |
| "I-TAX_AMOUNT": 8, | |
| "B-VENDOR_NAME": 9, | |
| "I-VENDOR_NAME": 10, | |
| "B-CUSTOMER_NAME": 11, | |
| "I-CUSTOMER_NAME": 12, | |
| "B-ADDRESS": 13, | |
| "I-ADDRESS": 14, | |
| "B-GST_ID": 15, | |
| "I-GST_ID": 16, | |
| "B-HEADER": 17, | |
| "I-HEADER": 18, | |
| "B-QUESTION": 19, | |
| "I-QUESTION": 20, | |
| "B-ANSWER": 21, | |
| "I-ANSWER": 22 | |
| }, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-INVOICE_NUMBER", | |
| "2": "I-INVOICE_NUMBER", | |
| "3": "B-DATE", | |
| "4": "I-DATE", | |
| "5": "B-TOTAL_AMOUNT", | |
| "6": "I-TOTAL_AMOUNT", | |
| "7": "B-TAX_AMOUNT", | |
| "8": "I-TAX_AMOUNT", | |
| "9": "B-VENDOR_NAME", | |
| "10": "I-VENDOR_NAME", | |
| "11": "B-CUSTOMER_NAME", | |
| "12": "I-CUSTOMER_NAME", | |
| "13": "B-ADDRESS", | |
| "14": "I-ADDRESS", | |
| "15": "B-GST_ID", | |
| "16": "I-GST_ID", | |
| "17": "B-HEADER", | |
| "18": "I-HEADER", | |
| "19": "B-QUESTION", | |
| "20": "I-QUESTION", | |
| "21": "B-ANSWER", | |
| "22": "I-ANSWER" | |
| }, | |
| "best_val_f1": 0.6247139588100686 | |
| } |