Instructions to use tkazusa/lilt-en-funsd-org with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkazusa/lilt-en-funsd-org with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tkazusa/lilt-en-funsd-org")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tkazusa/lilt-en-funsd-org") model = AutoModelForTokenClassification.from_pretrained("tkazusa/lilt-en-funsd-org") - Notebooks
- Google Colab
- Kaggle
lilt-en-funsd-org
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 1.8428
- Answer: {'precision': 0.047225501770956316, 'recall': 0.09791921664626684, 'f1': 0.06371963361210674, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.08554412560909583, 'recall': 0.2934076137418756, 'f1': 0.13246698805281912, 'number': 1077}
- Overall Precision: 0.0730
- Overall Recall: 0.1967
- Overall F1: 0.1065
- Overall Accuracy: 0.2652
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:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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