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
File size: 1,252 Bytes
1a7ee60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"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
} |