Delete bibert_multitask_classification.py

bibert_multitask_classification.py

@@ -1,53 +0,0 @@
-from transformers import Pipeline
-import numpy as np
-import torch
-
-def softmax(_outputs):
-    maxes = np.max(_outputs, axis=-1, keepdims=True)
-    shifted_exp = np.exp(_outputs - maxes)
-    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
-
-class BiBert_MultiTaskPipeline(Pipeline):
-
-
-    def _sanitize_parameters(self, **kwargs):
-
-        preprocess_kwargs = {}
-        if "task_id" in kwargs:
-            preprocess_kwargs["task_id"] = kwargs["task_id"]
-        
-
-        postprocess_kwargs = {}
-        if "top_k" in kwargs:
-            postprocess_kwargs["top_k"] = kwargs["top_k"]
-            postprocess_kwargs["_legacy"] = False
-        return preprocess_kwargs, {}, postprocess_kwargs
-
-        
-
-    def preprocess(self, inputs, task_id):
-        return_tensors = self.framework
-        feature = self.tokenizer(inputs, padding = True, return_tensors=return_tensors).to(self.device)
-        task_ids = np.full(shape=1,fill_value=task_id, dtype=int)
-        feature["task_ids"] = torch.IntTensor(task_ids)
-        return feature
-
-    def _forward(self, model_inputs):
-        return self.model(**model_inputs)
-
-    def postprocess(self, model_outputs, top_k=1, _legacy=True):
-        outputs = model_outputs["logits"][0]
-        outputs = outputs.numpy()
-        scores = softmax(outputs)
-
-        if top_k == 1 and _legacy:
-            return {"label": self.model.config.id2label[scores.argmax().item()], "probability": scores.max().item()}
-
-        dict_scores = [
-            {"label": self.model.config.id2label[i], "probability": score.item()} for i, score in enumerate(scores)
-        ]
-        if not _legacy:
-            dict_scores.sort(key=lambda x: x["probability"], reverse=True)
-            if top_k is not None:
-                dict_scores = dict_scores[:top_k]
-        return dict_scores