|
|
| import torch
|
|
|
| def predict_sentiment(text, tokenizer, model, device):
|
| inputs = tokenizer(
|
| text,
|
| padding="max_length",
|
| truncation=True,
|
| max_length=256,
|
| return_tensors="pt"
|
| )
|
|
|
| input_ids = inputs["input_ids"].to(device)
|
| attention_mask = inputs["attention_mask"].to(device)
|
|
|
| with torch.no_grad():
|
| outputs = model(input_ids, attention_mask)
|
| probs = torch.softmax(outputs, dim=1)
|
| pred = torch.argmax(probs, dim=1).item()
|
|
|
| label_map = {0: "Negative", 1: "Positive"}
|
| return label_map[pred], probs[0][pred].item()
|
|
|
| def batch_predict(texts, tokenizer, model, device):
|
| inputs = tokenizer(
|
| texts,
|
| padding=True,
|
| truncation=True,
|
| max_length=256,
|
| return_tensors="pt"
|
| )
|
|
|
| input_ids = inputs["input_ids"].to(device)
|
| attention_mask = inputs["attention_mask"].to(device)
|
|
|
| with torch.no_grad():
|
| outputs = model(input_ids, attention_mask)
|
| probs = torch.softmax(outputs, dim=1)
|
| preds = torch.argmax(probs, dim=1)
|
|
|
| label_map = {0: "Negative", 1: "Positive"}
|
|
|
| return [
|
| {
|
| "text": text,
|
| "label": label_map[p.item()],
|
| "confidence": probs[i][p].item()
|
| }
|
| for i, (text, p) in enumerate(zip(texts, preds))
|
| ]
|
|
|
