xlm-roberta-base-ncbi_disease-en

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

• Loss: 0.0496
• Precision: 0.8562
• Recall: 0.8628
• F1: 0.8595
• Accuracy: 0.9869

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	170	0.0555	0.7949	0.7980	0.7964	0.9833
No log	2.0	340	0.0524	0.7404	0.8551	0.7936	0.9836
0.0803	3.0	510	0.0484	0.7932	0.8869	0.8374	0.9849
0.0803	4.0	680	0.0496	0.8562	0.8628	0.8595	0.9869
0.0803	5.0	850	0.0562	0.7976	0.8615	0.8283	0.9848
0.0152	6.0	1020	0.0606	0.8086	0.8856	0.8454	0.9846
0.0152	7.0	1190	0.0709	0.8412	0.8412	0.8412	0.9866
0.0152	8.0	1360	0.0735	0.8257	0.8666	0.8456	0.9843
0.0059	9.0	1530	0.0730	0.8343	0.8767	0.8550	0.9866
0.0059	10.0	1700	0.0855	0.8130	0.8895	0.8495	0.9843
0.0059	11.0	1870	0.0868	0.8263	0.8767	0.8508	0.9860
0.0026	12.0	2040	0.0862	0.8273	0.8767	0.8513	0.9858
0.0026	13.0	2210	0.0875	0.8329	0.8806	0.8561	0.9859
0.0026	14.0	2380	0.0889	0.8287	0.8793	0.8533	0.9859
0.0013	15.0	2550	0.0884	0.8321	0.8755	0.8533	0.9861

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}
}