added readme with example

README.md

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+# Vent-roBERTa-emotion
+
+This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see https://arxiv.org/abs/1901.04856). <br/>
+The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
+   
+The model was trained to recognize 5 emotions ("Affection", "Anger", "Fear", "Happiness", "Sadness") on 7 million posts from the dataset. <br/>
+
+Example of how to use the classifier on single texts. <br/>
+
+```` 
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("lumalik/vent-roberta-emotion")
+model = AutoModelForSequenceClassification.from_pretrained("lumalik/vent-roberta-emotion")
+model.eval()
+
+texts = ["I love her sooo much", "I hate you!"]
+
+for text in texts:
+    encoded_text = tokenizer.encode_plus(text,
+                                         add_special_tokens=True,
+                                         max_length=128,
+                                         return_token_type_ids=True,
+                                         padding="max_length",
+                                         truncation=True,
+                                         return_attention_mask=True)
+
+    output = model(input_ids=torch.tensor(encoded_text['input_ids'], dtype=torch.long).unsqueeze(0),
+                   token_type_ids=torch.tensor(encoded_text['token_type_ids'], dtype=torch.long).unsqueeze(0),
+                   attention_mask=torch.tensor(encoded_text['attention_mask'], dtype=torch.long).unsqueeze(0))
+
+    output = softmax(output[0].detach().numpy(), axis=1)
+
+    print("======================")
+    print(text)
+    print("Affection: {}".format(output[0][0]))
+    print("Anger: {}".format(output[0][1]))
+    print("Fear: {}".format(output[0][2]))
+    print("Happiness: {}".format(output[0][3]))
+    print("Sadness: {}".format(output[0][4]))
+````