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
- 4,589
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.