fine-tuned-cardiffnlp-twitter-roberta-base-sentiment-finance-dataset

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an twitter finance news sentiment dataset. It achieves the following results on the evaluation set:

• Loss: 0.3123
• Accuracy: 0.8559

10 examples in Inference API are gathered from https://twitter.com/ftfinancenews in early may 2024

Colab Notebook for fine tuning : https://colab.research.google.com/drive/1gvpFbazlxg3AdSldH3w6TYjGUByxqCrh?usp=sharing

Training Data

https://huggingface.co/datasets/CJCJ3030/twitter-financial-news-sentiment/viewer/default/train

Evaluation Data

https://huggingface.co/datasets/CJCJ3030/twitter-financial-news-sentiment/viewer/default/validation

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 120
• eval_batch_size: 120
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Epoch	Step	Validation Loss	Accuracy
1.0	80	0.3123	0.8559
2.0	160	0.3200	0.8576
3.0	240	0.3538	0.8819
4.0	320	0.3695	0.8882
5.0	400	0.4108	0.8869

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1

Citation

@inproceedings{barbieri-etal-2020-tweeteval,
    title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
    author = "Barbieri, Francesco  and
      Camacho-Collados, Jose  and
      Espinosa Anke, Luis  and
      Neves, Leonardo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.148",
    doi = "10.18653/v1/2020.findings-emnlp.148",
    pages = "1644--1650"
}