Create README.md

README.md

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+---
+datasets:
+- amazon_reviews_multi
+language:
+- en
+library_name: transformers
+tags:
+- Text Classification
+- Pytorch
+- Sentiment_Analysis
+- Deberta
+---
+# Deberta for Sentiment Analysis
+
+This model has been trained on over 400k reviews from Amazon's multi-reviews dataset.
+
+## How to use the model
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def get_sentiment(sentence):
+    bert_dict = {}
+    vectors = tokenizer(sentence, return_tensors='pt').to(device)
+    outputs = bert_model(**vectors).logits
+    probs = torch.nn.functional.softmax(outputs, dim = 1)[0]
+    bert_dict['neg'] = round(probs[0].item(), 3)
+    bert_dict['neu'] = round(probs[1].item(), 3)
+    bert_dict['pos'] = round(probs[2].item(), 3)
+    return bert_dict
+
+MODEL_NAME = 'RashidNLP/Amazon-Deberta-Base-Sentiment'
+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("This is quite a mess you have made")
+
+```
+