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---
datasets:
- financial_phrasebank
- chiapudding/kaggle-financial-sentiment
- zeroshot/twitter-financial-news-sentiment
- FinanceInc/auditor_sentiment
language:
- en
library_name: transformers
tags:
- Sentiment Classification
- Finance
- Deberta-v2
license: mit
---
# Deberta for Financial Sentiment Classification
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.
The datasets I use are
1) financial_phrasebank
2) chiapudding/kaggle-financial-sentiment
3) zeroshot/twitter-financial-news-sentiment
4) FinanceInc/auditor_sentiment
## How to use the model
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def get_sentiment(sentences):
bert_dict = {}
vectors = tokenizer(sentences, padding = True, max_length = 65, return_tensors='pt').to(device)
outputs = bert_model(**vectors).logits
probs = torch.nn.functional.softmax(outputs, dim = 1)
for prob in probs:
bert_dict['neg'] = round(prob[0].item(), 3)
bert_dict['neu'] = round(prob[1].item(), 3)
bert_dict['pos'] = round(prob[2].item(), 3)
print (bert_dict)
MODEL_NAME = 'RashidNLP/Finance-Sentiment-Classification'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
get_sentiment(["The stock market will struggle until debt ceiling is increased", "ChatGPT is boosting Microsoft's search engine market share"])
``` |