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---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ncbi_disease-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8562421185372006
- name: Recall
type: recall
value: 0.8627700127064803
- name: F1
type: f1
value: 0.859493670886076
- name: Accuracy
type: accuracy
value: 0.9868991989319092
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-ncbi_disease-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ncbi_disease](https://huggingface.co/datasets/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.
```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}
}
```
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