https://github.com/lzw108/fmd
This is a continuous project on Financial Misinformation Detection (FMD).
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.4%) to scientific vocabulary
Repository
This is a continuous project on Financial Misinformation Detection (FMD).
Basic Info
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Financial Misinformation Detection
This work also supported Financial Misinformation Detection (FMD) challenge at COLING 2025
News
📢 Jan. 20, 2025 Our FMDLlama paper has been accepted by WWW 2025 as a short paper.
📢 Jan. 20, 2025 The Financial Misinformation Detection Challenge has successfully wrapped up at COLING 2025. Learn more about the challenge.
📢 Sep. 26, 2024 New preprint paper related to this work: "FMDLlama: Financial Misinformation Detection based on Large Language Models" at arXiv.
Datasets
Usage
Data preprocess
You can follow the practicedatapreprocess.ipynb file to get instruction train/val/test data in ./data/practicedata/instructdata/ path. The default is an instruction example, change accordingly as need.
Convert data format
```python
train
python src/converttoconvdata.py --origdata ./data/practicedata/instructdata/FMDtrain.json --writedata ./data/practicedata/instructdata/train.json --dataset_name fmd
val
python src/converttoconvdata.py --origdata ./data/practicedata/instructdata/FMDval.json --writedata ./data/practicedata/instructdata/val.json --dataset_name fmd ```
The commands above are to convert the data into dialogue data format for LLMs training. The current format is used for the LLaMA2 series (i.e. "Human": "sentence", "Assistant": "sentence" ). If you need to switch to other LLMs, please make the corresponding modifications.
Fine-tune
python
bash ./src/run_sft.sh
Inference
python
bash src/run_inference.sh
Evaluation
Follow the evaluation.ipynb file to get F1, rouge, bertscore, and final score.
License
This project is licensed under [MIT]. Please find more details in the MIT file.
Citation
@article{liu2024fmdllama,
title={FMDLlama: Financial Misinformation Detection based on Large Language Models},
author={Liu, Zhiwei and Zhang, Xin and Yang, Kailai and Xie, Qianqian and Huang, Jimin and Ananiadou, Sophia},
journal={arXiv preprint arXiv:2409.16452},
year={2024}
}
GitHub Events
Total
- Push event: 4
Last Year
- Push event: 4
Dependencies
- bert-score *
- datasets *
- deepspeed *
- flash-attn *
- gradio_client *
- peft *
- rouge_score *
- sentencepiece *
- textblob *
- torch *
- transformers *
- wandb *