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--- |
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license: apache-2.0 |
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language: |
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- "List of ISO 639-1 code for your language" |
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- English |
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tags: |
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- text-classification |
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- RoBERTa |
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- Sentiment |
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--- |
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<!DOCTYPE html> |
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<html> |
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<body> |
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<h1><b>Financial-RoBERTa</b></h1> |
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<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p> |
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<ul style="PADDING-LEFT: 40px"> |
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<li>Financial Statements,</li> |
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<li>Earnings Announcements,</li> |
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<li>Earnings Call Transcripts,</li> |
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<li>Corporate Social Responsibility (CSR) Reports,</li> |
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<li>Environmental, Social, and Governance (ESG) News,</li> |
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<li>Financial News,</li> |
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<li>Etc.</li> |
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</ul> |
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<p>Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.</p> |
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<p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p> |
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<p><b>How to perform sentiment analysis:</b></p> |
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<p>The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:</p> |
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<pre> |
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<code> |
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from transformers import pipeline |
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sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta") |
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print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020.")) |
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</code> |
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</pre> |
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</body> |
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</html> |
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