Spaces:
Sleeping
Sleeping
File size: 3,028 Bytes
b2fbe3d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers_interpret import SequenceClassificationExplainer
import torch
import pandas as pd
class SentimentAnalysis:
"""
Sentiment 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):
# Load Tokenizer & Model
hub_location = 'cardiffnlp/twitter-roberta-base-sentiment'
#hub_location = 'dccuchile/bert-base-spanish-wwm-uncased'
self.tokenizer = AutoTokenizer.from_pretrained(hub_location)
self.model = AutoModelForSequenceClassification.from_pretrained(hub_location)
# Change model labels in config
self.model.config.id2label[0] = "Negative"
self.model.config.id2label[1] = "Neutral"
self.model.config.id2label[2] = "Positive"
self.model.config.label2id["Negative"] = self.model.config.label2id.pop("LABEL_0")
self.model.config.label2id["Neutral"] = self.model.config.label2id.pop("LABEL_1")
self.model.config.label2id["Positive"] = self.model.config.label2id.pop("LABEL_2")
# Instantiate explainer
self.explainer = SequenceClassificationExplainer(self.model, self.tokenizer)
def justify(self, text):
"""
Get html annotation for displaying sentiment justification over text.
Parameters:
text (str): The user input string to sentiment justification
Returns:
html (hmtl): html object for plotting sentiment prediction justification
"""
word_attributions = self.explainer(text)
html = self.explainer.visualize("example.html")
return html
def classify(self, text):
"""
Recognize Sentiment in text.
Parameters:
text (str): The user input string to perform sentiment classification on
Returns:
predictions (str): The predicted probabilities for sentiment 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()
predictions = pd.Series(probs, index=["Negative", "Neutral", "Positive"], name='Predicted Probability')
return predictions
def run(self, text):
"""
Classify and Justify Sentiment in text.
Parameters:
text (str): The user input string to perform sentiment classification on
Returns:
predictions (str): The predicted probabilities for sentiment classes
html (hmtl): html object for plotting sentiment prediction justification
"""
predictions = self.classify(text)
html = self.justify(text)
return predictions, html |