You can deploy this model and start earning money today!

Amir13/
bert-base-parsbert-uncased-ncbi_disease

Name: bert-base-parsbert-uncased-ncbi_disease
Rating: 2.5 (7 reviews)

generated_from_trainer

2302.09611

autotrain_compatible

endpoints_compatible

Views

No views yet

bert-base-parsbert-uncased-ncbi_disease

This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the ncbi-persian dataset. It achieves the following results on the evaluation set:

Loss: 0.1018
Precision: 0.8192
Recall: 0.8645
F1: 0.8412
Accuracy: 0.9862

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	169	0.0648	0.7154	0.8237	0.7657	0.9813
No log	2.0	338	0.0573	0.7870	0.8263	0.8062	0.9853
0.0596	3.0	507	0.0639	0.7893	0.8776	0.8312	0.9858
0.0596	4.0	676	0.0678	0.8150	0.8461	0.8302	0.9860
0.0596	5.0	845	0.0737	0.8070	0.8474	0.8267	0.9862
0.0065	6.0	1014	0.0834	0.8052	0.8592	0.8313	0.9856
0.0065	7.0	1183	0.0918	0.8099	0.8355	0.8225	0.9859
0.0065	8.0	1352	0.0882	0.8061	0.8697	0.8367	0.9857
0.0021	9.0	1521	0.0903	0.8045	0.85	0.8266	0.9860
0.0021	10.0	1690	0.0965	0.8303	0.85	0.8401	0.9866
0.0021	11.0	1859	0.0954	0.8182	0.8645	0.8407	0.9860
0.0008	12.0	2028	0.0998	0.8206	0.8605	0.8401	0.9862
0.0008	13.0	2197	0.0995	0.82	0.8632	0.8410	0.9862
0.0008	14.0	2366	0.1015	0.8214	0.8592	0.8399	0.9861
0.0004	15.0	2535	0.1018	0.8192	0.8645	0.8412	0.9862

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}
}

Amir13/bert-base-parsbert-uncased-ncbi_disease

bert-base-parsbert-uncased-ncbi_disease

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
bert-base-parsbert-uncased-ncbi_disease