bert-base-parsbert-uncased-ncbi_disease

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

• Loss: 0.1018
• Precision: 0.8192
• Recall: 0.8645
• F1: 0.8412
• Accuracy: 0.9862

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

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	169	0.0648	0.7154	0.8237	0.7657	0.9813
No log	2.0	338	0.0573	0.7870	0.8263	0.8062	0.9853
0.0596	3.0	507	0.0639	0.7893	0.8776	0.8312	0.9858
0.0596	4.0	676	0.0678	0.8150	0.8461	0.8302	0.9860
0.0596	5.0	845	0.0737	0.8070	0.8474	0.8267	0.9862
0.0065	6.0	1014	0.0834	0.8052	0.8592	0.8313	0.9856
0.0065	7.0	1183	0.0918	0.8099	0.8355	0.8225	0.9859
0.0065	8.0	1352	0.0882	0.8061	0.8697	0.8367	0.9857
0.0021	9.0	1521	0.0903	0.8045	0.85	0.8266	0.9860
0.0021	10.0	1690	0.0965	0.8303	0.85	0.8401	0.9866
0.0021	11.0	1859	0.0954	0.8182	0.8645	0.8407	0.9860
0.0008	12.0	2028	0.0998	0.8206	0.8605	0.8401	0.9862
0.0008	13.0	2197	0.0995	0.82	0.8632	0.8410	0.9862
0.0008	14.0	2366	0.1015	0.8214	0.8592	0.8399	0.9861
0.0004	15.0	2535	0.1018	0.8192	0.8645	0.8412	0.9862

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• 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}
}