Spaces:
Sleeping
Sleeping
| 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' | |
| self.tokenizer = AutoTokenizer.from_pretrained(hub_location) | |
| self.model = AutoModelForSequenceClassification.from_pretrained(hub_location) | |
| hub_location_sp = 'finiteautomata/beto-sentiment-analysis' | |
| self.tokenizer_sp = AutoTokenizer.from_pretrained(hub_location_sp) | |
| self.model_sp = AutoModelForSequenceClassification.from_pretrained(hub_location_sp) | |
| # 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) | |
| self.explainer_sp = SequenceClassificationExplainer(self.model_sp, self.tokenizer_sp) | |
| def justify(self, text, lang): | |
| """ | |
| 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 | |
| """ | |
| if lang == 'es': | |
| word_attributions = self.explainer_sp(text) | |
| html = self.explainer_sp.visualize("example.html") | |
| else: | |
| word_attributions = self.explainer(text) | |
| html = self.explainer.visualize("example.html") | |
| return html | |
| def classify(self, text, lang): | |
| """ | |
| 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 | |
| """ | |
| if lang == 'es': | |
| tokens = self.tokenizer_sp.encode_plus(text, add_special_tokens=False, return_tensors='pt') | |
| outputs = self.model_sp(**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') | |
| else: | |
| 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, lang): | |
| """ | |
| 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, lang) | |
| html = self.justify(text, lang) | |
| return predictions, html |