metadata

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

xlm-roberta-base-wnut2017-en

This model is a fine-tuned version of xlm-roberta-base on 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.

@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}
}