bert-base-parsbert-uncased-ontonotesv5

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bert-base-parsbert-uncased-ontonotesv5

This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the ontonotes5-persian dataset. It achieves the following results on the evaluation set:

• Loss: 0.2169
• Precision: 0.8145
• Recall: 0.8287
• F1: 0.8215
• Accuracy: 0.9741

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2

Citation

If you used the datasets and models in this repository, please cite it.

@misc{https://doi.org/10.48550/arxiv.2302.09611,
  doi = {10.48550/ARXIV.2302.09611},
  url = {https://arxiv.org/abs/2302.09611},
  author = {Sartipi, Amir and Fatemi, Afsaneh},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual, non-exclusive license}
}