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from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers_interpret import SequenceClassificationExplainer
import torch
import pandas as pd
class EmotionDetection:
"""
Emotion Detection on text data.
Attributes:
tokenizer: An instance of Hugging Face Tokenizer
model: An instance of Hugging Face Model
explainer: An instance of SequenceClassificationExplainer from Transformers interpret
"""
def __init__(self):
hub_location = 'cardiffnlp/twitter-roberta-base-emotion'
self.tokenizer = AutoTokenizer.from_pretrained(hub_location)
self.model = AutoModelForSequenceClassification.from_pretrained(hub_location)
self.explainer = SequenceClassificationExplainer(self.model, self.tokenizer)
def justify(self, text):
"""
Get html annotation for displaying emotion justification over text.
Parameters:
text (str): The user input string to emotion justification
Returns:
html (hmtl): html object for plotting emotion prediction justification
"""
word_attributions = self.explainer(text)
html = self.explainer.visualize("example.html")
return html
def classify(self, text):
"""
Recognize Emotion in text.
Parameters:
text (str): The user input string to perform emotion classification on
Returns:
predictions (str): The predicted probabilities for emotion classes
"""
tokens = self.tokenizer.encode_plus(text, add_special_tokens=False, return_tensors='pt')
outputs = self.model(**tokens)
probs = torch.nn.functional.softmax(outputs[0], dim=-1)
probs = probs.mean(dim=0).detach().numpy()
labels = list(self.model.config.id2label.values())
preds = pd.Series(probs, index=labels, name='Predicted Probability')
return preds
def run(self, text):
"""
Classify and Justify Emotion in text.
Parameters:
text (str): The user input string to perform emotion classification on
Returns:
predictions (str): The predicted probabilities for emotion classes
html (hmtl): html object for plotting emotion prediction justification
"""
preds = self.classify(text)
html = self.justify(text)
return preds, html |