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}
}
- Downloads last month
- 5
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train Amir13/xlm-roberta-base-ncbi_disease-en
Evaluation results
- Precision on ncbi_diseasevalidation set self-reported0.856
- Recall on ncbi_diseasevalidation set self-reported0.863
- F1 on ncbi_diseasevalidation set self-reported0.859
- Accuracy on ncbi_diseasevalidation set self-reported0.987