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},
}