| | --- |
| | language: "en" |
| | tags: |
| | - financial-text-analysis |
| | - forward-looking-statement |
| | widget: |
| | - text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. " |
| | --- |
| | |
| | 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. |
| | ```python |
| | # 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](https://finbert.ai/) for more details on the recent development of FinBERT. |
