---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ontonotesv5-en
  results: []
---

<!-- 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-ontonotesv5-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5/viewer/english_v4/train) 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.
```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}
}
```