Create README.md

README.md

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+# Sentiment Analysis of English Tweets
+
+**BERTsent**: A finetuned **BERT** based **sent**iment classifier for English language tweets.
+
+BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on [bertweet-base](https://github.com/VinAIResearch/BERTweet) that was trained on 850M English Tweets (cased) and additional 23M COVID-19 English Tweets (cased). The base model used [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.
+
+Output labels:
+
+ - 0 represents "negative" sentiment
+ - 1 represents "neutral" sentiment
+ - 2 represents "positive" sentiment
+
+## Using the model
+
+Install transformers, if already not installed:
+
+    terminal: pip install transformers
+    notebooks (Colab, Kaggle): !pip install transformers
+
+Import BERTsent from the transformers library:
+
+    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+      
+    tokenizer = AutoTokenizer.from_pretrained("rabindralamsal/finetuned-bertweet-sentiment-analysis")
+    
+    model = TFAutoModelForSequenceClassification.from_pretrained("rabindralamsal/finetuned-bertweet-sentiment-analysis")
+
+Import TensorFlow:
+
+    import tensorflow as tf
+
+We have installed and imported everything that's needed for the sentiment analysis. Let's predict sentiment of an example tweet:
+
+    example_tweet = "The NEET exams show our Govt in a poor light: unresponsiveness to genuine concerns; admit cards not delivered to aspirants in time; failure to provide centres in towns they reside, thus requiring unnecessary & risky travels. What a disgrace to treat our #Covid warriors like this!"
+    #this tweet resides on Twitter with an identifier-1435793872588738560
+        
+    input = tokenizer.encode(example_tweet, return_tensors="tf")
+    output = model.predict(input)[0]
+    prediction = tf.nn.softmax(output, axis=1).numpy()
+    sentiment = np.argmax(prediction)
+        
+    print(prediction)
+    print(sentiment)
+
+Output: 
+
+    [[0.9862386  0.01050556 0.00325586]]
+    0