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---
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-wnut2017-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: validation
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.7219662058371735
- name: Recall
type: recall
value: 0.562200956937799
- name: F1
type: f1
value: 0.6321452589105581
- name: Accuracy
type: accuracy
value: 0.9589398080467807
---
<!-- 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-wnut2017-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on [wnut_17](https://huggingface.co/datasets/wnut_17) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2319
- Precision: 0.7220
- Recall: 0.5622
- F1: 0.6321
- Accuracy: 0.9589
## 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 | 107 | 0.2789 | 0.4679 | 0.3397 | 0.3936 | 0.9408 |
| No log | 2.0 | 214 | 0.2092 | 0.6875 | 0.5 | 0.5789 | 0.9518 |
| No log | 3.0 | 321 | 0.1968 | 0.6194 | 0.5431 | 0.5787 | 0.9567 |
| No log | 4.0 | 428 | 0.2172 | 0.7212 | 0.5383 | 0.6164 | 0.9586 |
| 0.1523 | 5.0 | 535 | 0.2319 | 0.7220 | 0.5622 | 0.6321 | 0.9589 |
| 0.1523 | 6.0 | 642 | 0.2023 | 0.6180 | 0.5514 | 0.5828 | 0.9577 |
| 0.1523 | 7.0 | 749 | 0.2494 | 0.6895 | 0.5419 | 0.6068 | 0.9589 |
| 0.1523 | 8.0 | 856 | 0.2844 | 0.7018 | 0.5263 | 0.6015 | 0.9578 |
| 0.1523 | 9.0 | 963 | 0.2568 | 0.6808 | 0.5562 | 0.6122 | 0.9592 |
| 0.0294 | 10.0 | 1070 | 0.2453 | 0.6718 | 0.5754 | 0.6198 | 0.9596 |
| 0.0294 | 11.0 | 1177 | 0.2538 | 0.6933 | 0.5706 | 0.6260 | 0.9600 |
| 0.0294 | 12.0 | 1284 | 0.2638 | 0.6865 | 0.5658 | 0.6203 | 0.9593 |
| 0.0294 | 13.0 | 1391 | 0.2744 | 0.6764 | 0.5526 | 0.6083 | 0.9587 |
| 0.0294 | 14.0 | 1498 | 0.2714 | 0.6812 | 0.5622 | 0.6160 | 0.9590 |
| 0.0135 | 15.0 | 1605 | 0.2724 | 0.6830 | 0.5670 | 0.6196 | 0.9593 |
### 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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