Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Sentiment Classification
Finance
Deberta-v2
Inference Endpoints
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@@ -13,7 +13,7 @@ tags:
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  - Deberta-v2
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  ---
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- # Deberta for Financial Sentiment Analysis
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  I use a Deberta model trained on over 1 million reviews from Amazon's multi-reviews dataset and finetune it on 4 finance datasets that are categorized with Sentiment labels.
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  The datasets I use are
@@ -41,7 +41,7 @@ def get_sentiment(sentences):
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  bert_dict['pos'] = round(prob[2].item(), 3)
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  print (bert_dict)
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- MODEL_NAME = 'RashidNLP/Finance_Multi_Sentiment'
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  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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  bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)
 
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  - Deberta-v2
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  ---
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+ # Deberta for Financial Sentiment Classification
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  I use a Deberta model trained on over 1 million reviews from Amazon's multi-reviews dataset and finetune it on 4 finance datasets that are categorized with Sentiment labels.
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  The datasets I use are
 
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  bert_dict['pos'] = round(prob[2].item(), 3)
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  print (bert_dict)
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+ MODEL_NAME = 'RashidNLP/Finance-Sentiment-Classification'
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  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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  bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)