soleimanian
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financial-roberta-large-sentiment

Text Classification

Financial Statements

Earnings Call Transcripts

Corporate governance

Inference Endpoints

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soleimanian commited on May 16, 2022

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@@ -19,5 +19,12 @@ license: apache-2.0
 <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>
 <p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p>
 <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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 <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>
 <p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p>
 <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>
+<pre>
+  <code>
+from transformers import pipeline
+sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english")
+print(sentiment_analysis("I love this!"))
+  </code>
+</pre>
 </body>
 </html>