FinTwitBERT sentiment FinTwitBERT sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses FinTwitBERT as a base model, which has been pre trained on 10 million financial tweets. 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. Therefore this model performs great on informal financial texts, seen on social media. Intended Uses FinTwitBERT sentiment is intended for classifying financial tweets or other financial social media texts. Dataset FinTwitBERT sentiment has been trained on two datasets. One being a collection of several financial tweet datasets and the other a synthetic dataset created out of the first. TimKoornstra/financial tweets sentiment: 38,091 human labeled tweets TimKoornstra/synthetic financial tweets sentiment: 1,428,771 synethtic tweets More Information For a comprehensive overview, including the training setup and analysis of the model, visit the FinTwitBERT GitHub repository. Usage Using HuggingFace's transformers library the model and tokenizers can be converted into a pipeline for text classification.…
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