# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("pr28416/layoutlm-funsd-tf")
model = AutoModelForTokenClassification.from_pretrained("pr28416/layoutlm-funsd-tf")Quick Links
layoutlm-funsd-tf
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2400
- Validation Loss: 0.6581
- Epoch: 7
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.7416 | 1.4933 | 0 |
| 1.2494 | 0.9719 | 1 |
| 0.8035 | 0.7523 | 2 |
| 0.5954 | 0.6298 | 3 |
| 0.4596 | 0.6250 | 4 |
| 0.3674 | 0.6055 | 5 |
| 0.2889 | 0.6418 | 6 |
| 0.2400 | 0.6581 | 7 |
Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for pr28416/layoutlm-funsd-tf
Base model
microsoft/layoutlm-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pr28416/layoutlm-funsd-tf")