| from transformers import Pipeline | |
| from sentence_transformers import SentenceTransformer | |
| import torch | |
| class SentimentModelPipe(Pipeline): | |
| def __init__(self, **kwargs): | |
| Pipeline.__init__(self, **kwargs) | |
| self.smodel = SentenceTransformer( | |
| kwargs.get("embedding_model", "sentence-transformers/all-MiniLM-L6-v2") | |
| ) | |
| self.class_map = kwargs.get( | |
| "class_map", | |
| {0: "sad", 1: "joy", 2: "love", 3: "anger", 4: "fear", 5: "surprise"}, | |
| ) | |
| def _sanitize_parameters(self, **kw): | |
| return {}, {}, {} | |
| def preprocess(self, inputs): | |
| return self.smodel.encode(inputs, convert_to_tensor=True) | |
| def postprocess(self, outputs): | |
| if isinstance(outputs, torch.Tensor): | |
| outputs = [outputs] | |
| results = [] | |
| for out in outputs: | |
| r = [] | |
| for i, l in enumerate(out): | |
| r.append({"label": self.class_map[i], "score": l.item()}) | |
| results.append(r) | |
| return results | |
| def _forward(self, tensor): | |
| with torch.no_grad(): | |
| out = self.model(tensor) | |
| return out | |