mp-02/cord
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How to use mp-02/layoutlmv3-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="mp-02/layoutlmv3-finetuned-cord") # Load model directly
from transformers import AutoProcessor, AutoModelForTokenClassification
processor = AutoProcessor.from_pretrained("mp-02/layoutlmv3-finetuned-cord")
model = AutoModelForTokenClassification.from_pretrained("mp-02/layoutlmv3-finetuned-cord")This model is a fine-tuned version of layoutlmv3 on the mp-02/cord dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 3.125 | 250 | 0.6018 | 0.8218 | 0.8633 | 0.8420 | 0.8577 |
| 1.0098 | 6.25 | 500 | 0.2695 | 0.9205 | 0.9495 | 0.9347 | 0.9451 |
| 1.0098 | 9.375 | 750 | 0.1813 | 0.9528 | 0.9693 | 0.9610 | 0.9639 |
| 0.1993 | 12.5 | 1000 | 0.1557 | 0.9616 | 0.9743 | 0.9679 | 0.9739 |
| 0.1993 | 15.625 | 1250 | 0.1749 | 0.9608 | 0.9743 | 0.9675 | 0.9703 |
| 0.0787 | 18.75 | 1500 | 0.1482 | 0.9616 | 0.9743 | 0.9679 | 0.9730 |
| 0.0787 | 21.875 | 1750 | 0.1288 | 0.9640 | 0.9751 | 0.9695 | 0.9762 |
| 0.0433 | 25.0 | 2000 | 0.1292 | 0.9672 | 0.9776 | 0.9724 | 0.9767 |
| 0.0433 | 28.125 | 2250 | 0.1372 | 0.9623 | 0.9735 | 0.9679 | 0.9735 |
| 0.031 | 31.25 | 2500 | 0.1408 | 0.9631 | 0.9743 | 0.9687 | 0.9730 |