metadata

base_model: bert-base-chinese
metrics:
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: bert-finetuned-weibo-luobokuaipao
    results: []

bert-finetuned-weibo-luobokuaipao

This model is a fine-tuned version of bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1020
Accuracy: 0.5981

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	243	1.0453	0.5519
No log	2.0	486	0.9954	0.5796
0.9964	3.0	729	1.0374	0.6074
0.9964	4.0	972	1.0489	0.6019
0.6111	5.0	1215	1.1020	0.5981

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

@misc{wang2024recentsurgepublictransportation,
      title={Recent Surge in Public Interest in Transportation: Sentiment Analysis of Baidu Apollo Go Using Weibo Data}, 
      author={Shiqi Wang and Zhouye Zhao and Yuhang Xie and Mingchuan Ma and Zirui Chen and Zeyu Wang and Bohao Su and Wenrui Xu and Tianyi Li},
      year={2024},
      eprint={2408.10088},
      archivePrefix={arXiv},
      primaryClass={cs.SI},
      url={https://arxiv.org/abs/2408.10088}, 
}