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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| title = "Forward Looking Statement Classification with FinBERT" | |
| description = "This model classifies a sentence into one of the three categories: Specific FLS, Non- Specific FLS, and Not-FLS. We label a sentence as Specific FLS if it is about the future of the company, as Non-Specific FLS if it is future-oriented but could be said of any company (e.g., cautionary language or risk disclosure), and as Not-FLS if it is not about the future." | |
| examples =[['we expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.'], | |
| ['on an equivalent unit of production basis, general and administrative expenses declined 24 percent from 1994 to $.67 per boe.'], | |
| ['we will continue to assess the need for a valuation allowance against deferred tax assets considering all available evidence obtained in future reporting periods.']] | |
| tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-fls") | |
| finbert = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-fls") | |
| nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) | |
| def get_sentiment(input_text): | |
| return nlp(input_text) | |
| iface = gr.Interface(fn=get_sentiment, | |
| inputs="text", | |
| outputs=["text"], | |
| title=title, | |
| description=description, | |
| examples=examples) | |
| iface.launch(debug=True) | |