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--- |
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license: mit |
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datasets: |
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- TimKoornstra/financial-tweets-sentiment |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: text-classification |
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tags: |
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- sentiment |
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- finance |
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- sentiment-analysis |
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- financial-sentiment-analysis |
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- twitter |
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- tweets |
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- stocks |
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- stock-market |
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- crypto |
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- cryptocurrency |
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base_model: StephanAkkerman/FinTwitBERT |
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widget: |
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- text: Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑 |
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example_title: Bullish Crypto Tweet |
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- text: It is about damn time that my $ARB and $ETH bags pump FFS. 🚀 |
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example_title: Bullish Crypto Tweet 2 |
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- text: $SPY $SPX closed higher 8th consecutive weeks. Last time it closed 9th straight was 20 years ago. |
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example_title: Bullish Stock Tweet |
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- text: $TCBP Lowest float stock in the market. Float just 325k. Don’t sell for pennies, this one will be a monster. Still early |
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example_title: Bullish Stock Tweet 2 |
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- text: Italian companies braced for more political uncertainty |
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example_title: Bearish News |
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--- |
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# FinTwitBERT-sentiment |
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FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses [FinTwitBERT](https://huggingface.co/StephanAkkerman/FinTwitBERT) as a base model, which has been pre-trained on 1 million financial tweets. |
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This approach ensures that the FinTwitBERT-sentiment has seen enough financial tweets, which have an informal nature, compared to other financial texts, such as news headlines. |
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Therefore this model performs great on informal financial texts, seen on social media. |
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## Intended Uses |
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FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts. |
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## More Information |
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For a comprehensive overview, including the training setup and analysis of the model, visit the [FinTwitBERT GitHub repository](https://github.com/TimKoornstra/FinTwitBERT). |
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## Usage |
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Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model and tokenizers can be converted into a pipeline for text classification. |
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```python |
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from transformers import BertForSequenceClassification, AutoTokenizer, pipeline |
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model = BertForSequenceClassification.from_pretrained( |
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"StephanAkkerman/FinTwitBERT-sentiment", |
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num_labels=3, |
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id2label={0: "NEUTRAL", 1: "BULLISH", 2: "BEARISH"}, |
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label2id={"NEUTRAL": 0, "BULLISH": 1, "BEARISH": 2}, |
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) |
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model.config.problem_type = "single_label_classification" |
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tokenizer = AutoTokenizer.from_pretrained( |
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"StephanAkkerman/FinTwitBERT-sentiment" |
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) |
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model.eval() |
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pipeline = pipeline( |
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"text-classification", model=model, tokenizer=tokenizer |
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) |
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# Sentences we want the sentiment for |
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sentence = ["Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑"] |
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# Get the predicted sentiment |
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print(pipeline(sentence)) |
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``` |
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## Training |
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The model was trained with the following parameters: |
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## Citing & Authors |
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If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work: |
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```bibtex |
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@misc{FinTwitBERT, |
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author = {Stephan Akkerman, Tim Koornstra}, |
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title = {FinTwitBERT: A Specialized Language Model for Financial Tweets}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/TimKoornstra/FinTwitBERT}} |
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} |
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``` |
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## License |
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This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details. |
