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predict.py
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predict.py
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import torch
from model import ColaModel
from data import DataModule
class ColaPredictor:
def __init__(self, model_path):
self.model_path = model_path
# load the best model
self.model = ColaModel.load_from_checkpoint(model_path)
# put the model in eval mode
self.model.eval()
# freeze the params
self.model.freeze()
self.processor = DataModule()
self.softmax = torch.nn.Softmax(dim=0)
self.labels = ["unacceptable", "acceptable"]
def predict(self, text):
inference_sample = {"sentence": text}
processed = self.processor.tokenize_data(inference_sample)
logits = self.model(
torch.tensor([processed["input_ids"]]),
torch.tensor([processed["attention_mask"]]),
)
scores = self.softmax(logits[0]).tolist()
predictions = []
for score, label in zip(scores, self.labels):
predictions.append({"label": label, "score": score})
return predictions
if __name__ == "__main__":
sentence = "The boy is sitting on a bench"
predictor = ColaPredictor("./models/epoch=0-step=267.ckpt")
print(predictor.predict(sentence))