Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0787	1.0	2350	0.0831	0.8119	0.8611	0.8358	0.9765
0.0565	2.0	4700	0.0756	0.8513	0.8708	0.8609	0.9794
0.0415	3.0	7050	0.0763	0.8530	0.8739	0.8633	0.9801
0.0347	4.0	9400	0.0820	0.8558	0.8810	0.8682	0.9804
0.0252	5.0	11750	0.0913	0.8683	0.8607	0.8645	0.9791
0.0201	6.0	14100	0.0923	0.86	0.8763	0.8681	0.9804
0.0172	7.0	16450	0.1023	0.8617	0.8788	0.8702	0.9800
0.0118	8.0	18800	0.1083	0.8579	0.8756	0.8667	0.9799
0.0101	9.0	21150	0.1162	0.8583	0.8766	0.8674	0.9803
0.009	10.0	23500	0.1189	0.8623	0.8772	0.8697	0.9804
0.0074	11.0	25850	0.1259	0.8642	0.8757	0.8699	0.9804
0.0053	12.0	28200	0.1303	0.8601	0.8765	0.8682	0.9800
0.0046	13.0	30550	0.1345	0.8619	0.8755	0.8686	0.9799
0.004	14.0	32900	0.1381	0.8637	0.8785	0.8710	0.9804
0.0029	15.0	35250	0.1405	0.8616	0.8788	0.8701	0.9803

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.

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-ontonotesv5-en

xlm-roberta-base-ontonotesv5-en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
xlm-roberta-base-ontonotesv5-en