The Clarifai platform has now been updated to Release 8.3! We've added 3 more NLP models for financial text classification and summarization.
This model reads financial news and gives scores based on its sentiment. Is it good news? Bad? Neutral? See the examples below and try your own!
This model is a fine-tuned version of distilroberta-base on the financial_phrasebank dataset.
FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium.
The model will give softmax outputs for three labels: positive, negative or neutral. Try it here!
This uses the Pegasus model, more info on Google's Blog.
This model summarizes financial texts and creates abstractive summaries of them. "Abstractive" means that the model generates its own sentences summarizing the content, not just selectively choosing sentences which best summarize the text (which would be "extractive" summarization).