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.

bibtex

@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}
}

Amir13/xlm-roberta-base-ncbi_disease-en

xlm-roberta-base-ncbi_disease-en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
xlm-roberta-base-ncbi_disease-en