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--- |
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language: "en" |
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tags: |
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- financial-text-analysis |
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- forward-looking-statement |
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widget: |
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- text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. " |
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--- |
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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. |
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**Input**: A financial text. |
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**Output**: Specific-FLS , Non-specific FLS, or Not-FLS. |
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# How to use |
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You can use this model with Transformers pipeline for forward-looking statement classification. |
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```python |
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# tested in transformers==4.18.0 |
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from transformers import BertTokenizer, BertForSequenceClassification, pipeline |
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finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-fls',num_labels=3) |
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tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-fls') |
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nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) |
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results = nlp('We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.') |
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print(results) # [{'label': 'Specific FLS', 'score': 0.77278733253479}] |
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``` |
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Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT. |
