transformers pytorch xlm-roberta token-classification generated_from_trainer dataset:ncbi_disease arxiv:2302.09611 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ncbi_disease
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-ncbi_disease-en
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.8562421185372006
      name: Precision
    - type: recall
      value: 0.8627700127064803
      name: Recall
    - type: f1
      value: 0.859493670886076
      name: F1
    - type: accuracy
      value: 0.9868991989319092
      name: Accuracy
---

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# 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}
}
```
