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| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers_interpret import SequenceClassificationExplainer | |
| import torch | |
| import pandas as pd | |
| class SentimentAnalysis(): | |
| def __init__(self): | |
| # Load Tokenizer & Model | |
| hub_location = 'cardiffnlp/twitter-roberta-base-sentiment' | |
| 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): | |
| """""" | |
| word_attributions = self.explainer(text) | |
| html = self.explainer.visualize("example.html") | |
| return html | |
| def classify(self, text): | |
| """""" | |
| 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() | |
| preds = pd.Series(probs, index=["Negative", "Neutral", "Positive"], name='Predicted Probability') | |
| return preds | |
| def run(self, text): | |
| """""" | |
| preds = self.classify(text) | |
| html = self.justify(text) | |
| return preds, html |