xlm-roberta-base-ontonotesv5

This model is a fine-tuned version of xlm-roberta-base on the ontonotes5-persian dataset. It achieves the following results on the evaluation set:

• Loss: 0.1693
• Precision: 0.8336
• Recall: 0.8360
• F1: 0.8348
• Accuracy: 0.9753

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
0.1145	1.0	2310	0.1174	0.7717	0.7950	0.7832	0.9697
0.0793	2.0	4620	0.1084	0.8129	0.8108	0.8118	0.9729
0.0627	3.0	6930	0.1078	0.8227	0.8102	0.8164	0.9735
0.047	4.0	9240	0.1132	0.8105	0.8223	0.8164	0.9731
0.0347	5.0	11550	0.1190	0.8185	0.8315	0.8250	0.9742
0.0274	6.0	13860	0.1282	0.8088	0.8387	0.8235	0.9734
0.0202	7.0	16170	0.1329	0.8219	0.8354	0.8286	0.9745
0.0167	8.0	18480	0.1423	0.8147	0.8376	0.8260	0.9742
0.0134	9.0	20790	0.1520	0.8259	0.8308	0.8284	0.9745
0.0097	10.0	23100	0.1627	0.8226	0.8377	0.8300	0.9745
0.0084	11.0	25410	0.1693	0.8336	0.8360	0.8348	0.9753
0.0066	12.0	27720	0.1744	0.8317	0.8359	0.8338	0.9751
0.0053	13.0	30030	0.1764	0.8247	0.8409	0.8327	0.9750
0.004	14.0	32340	0.1797	0.8280	0.8378	0.8328	0.9751
0.004	15.0	34650	0.1809	0.8310	0.8382	0.8346	0.9754

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}
}