# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Sennodipoi/LayoutLMv1-FUNSD-ft")
model = AutoModelForTokenClassification.from_pretrained("Sennodipoi/LayoutLMv1-FUNSD-ft")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
LayoutLMv1 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained using seqeval in strict mode:
| Precision | Recall | F1-score | Variance (F1) | |
|---|---|---|---|---|
| ANSWER | 0.80 | 0.78 | 0.80 | 1e-4 |
| HEADER | 0.62 | 0.47 | 0.53 | 2e-4 |
| QUESTION | 0.85 | 0.71 | 0.83 | 3e-5 |
| Micro avg | 0.83 | 0.77 | 0.81 | 1e-4 |
| Macro avg | 0.77 | 0.56 | 0.72 | 3e-5 |
| Weighted avg | 0.83 | 0.78 | 0.80 | 1e-4 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sennodipoi/LayoutLMv1-FUNSD-ft")