# Load model directly
from transformers import AutoProcessor, AutoModelForTokenClassification
processor = AutoProcessor.from_pretrained("Sennodipoi/LayoutLMv2-FUNSD-ft")
model = AutoModelForTokenClassification.from_pretrained("Sennodipoi/LayoutLMv2-FUNSD-ft")Quick Links
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Check out the documentation for more information.
LayoutLMv2 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained with seqeval in strict mode:
| Precision | Recall | F1-score | Variance (F1) | |
|---|---|---|---|---|
| ANSWER | 0.82 | 0.83 | 0.82 | 4e-4 |
| HEADER | 0.65 | 0.58 | 0.62 | 9e-4 |
| QUESTION | 0.87 | 0.83 | 0.85 | 3e-5 |
| Micro avg | 0.84 | 0.82 | 0.82 | 1e-4 |
| Macro avg | 0.79 | 0.75 | 0.76 | 4e-4 |
| Weighted avg | 0.84 | 0.82 | 0.82 | 1e-4 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sennodipoi/LayoutLMv2-FUNSD-ft")