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--- |
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datasets: |
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- amazon_reviews_multi |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- Text Classification |
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- Pytorch |
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- Sentiment_Analysis |
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- Deberta |
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license: mit |
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--- |
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# Deberta for Sentiment Analysis |
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This is a Deberta model finetuned on over 1 million reviews from Amazon's multi-reviews dataset. |
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## How to use the model |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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def get_sentiment(sentence): |
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bert_dict = {} |
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vectors = tokenizer(sentence, return_tensors='pt').to(device) |
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outputs = bert_model(**vectors).logits |
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probs = torch.nn.functional.softmax(outputs, dim = 1)[0] |
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bert_dict['neg'] = round(probs[0].item(), 3) |
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bert_dict['neu'] = round(probs[1].item(), 3) |
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bert_dict['pos'] = round(probs[2].item(), 3) |
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return bert_dict |
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MODEL_NAME = 'RashidNLP/Amazon-Deberta-Base-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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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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get_sentiment("This is quite a mess you have made") |
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``` |
