import torch from transformers import DistilBertForSequenceClassification import os # # Get the directory path of the current script # script_dir = os.path.dirname(os.path.abspath(__file__)) # model = DistilBertForSequenceClassification.from_pretrained("model.safetensors") # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim") model = AutoModelForSequenceClassification.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim") # from transformers import DistilBertTokenizerFast # tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased') # Move the model to the GPU if available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) def sentiment_class(summarized_text): ''' # 1 = non-depressed # 0 = depressed returns: example:- array([[0.00493283, 0.9950671 ]], dtype=float32) ''' inputs = tokenizer(summarized_text, padding = True, truncation = True, return_tensors='pt').to('cuda') outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) predictions = predictions.cpu().detach().numpy() return predictions def pattern_classification(): return result def corelation_analysis(): return result