Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use wsqstar/bert-finetuned-weibo-luobokuaipao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wsqstar/bert-finetuned-weibo-luobokuaipao with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wsqstar/bert-finetuned-weibo-luobokuaipao")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") model = AutoModelForSequenceClassification.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesto link to paper page
README.md
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@@ -61,3 +61,15 @@ The following hyperparameters were used during training:
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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```
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@misc{wang2024recentsurgepublictransportation,
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title={Recent Surge in Public Interest in Transportation: Sentiment Analysis of Baidu Apollo Go Using Weibo Data},
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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},
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year={2024},
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eprint={2408.10088},
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archivePrefix={arXiv},
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primaryClass={cs.SI},
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url={https://arxiv.org/abs/2408.10088},
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}
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```
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