xlm-roberta-base-ontonotesv5-en

This model is a fine-tuned version of xlm-roberta-base on the conll2012_ontonotesv5 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1381
• Precision: 0.8637
• Recall: 0.8785
• F1: 0.8710
• Accuracy: 0.9804

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
0.0787	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.

@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}
}