Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Management Discussion and Analysis section of annual reports of Russell 3000 firms.
Input: A financial text.
Output: Specific-FLS , Non-specific FLS, or Not-FLS.
How to use
You can use this model with Transformers pipeline for forward-looking statement classification.
# tested in transformers==4.18.0
from transformers import BertTokenizer, BertForSequenceClassification, pipeline
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-fls',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-fls')
nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
results = nlp('We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.')
print(results) # [{'label': 'Specific FLS', 'score': 0.77278733253479}]
Visit FinBERT.AI for more details on the recent development of FinBERT.
- Downloads last month
- 75
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.