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| 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 |