Upload DemoT5QAPipeline

custom_pipeline.py

@@ -23,27 +23,27 @@ class DemoT5QAPipeline(QuestionAnsweringPipeline):
     #         raise ValueError("Inputs must be a dictionary with 'question' and 'context' keys.")
       
     
-    def _forward(self, model_inputs, **generate_kwargs):
-        if self.framework == "pt":
-            in_b, input_length = model_inputs["input_ids"].shape
-        elif self.framework == "tf":
-            in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
+    # def _forward(self, model_inputs, **generate_kwargs):
+    #     if self.framework == "pt":
+    #         in_b, input_length = model_inputs["input_ids"].shape
+    #     elif self.framework == "tf":
+    #         in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
         
-        self.check_inputs(
-            input_length,
-            generate_kwargs.get("min_length", self.model.config.min_length),
-            generate_kwargs.get("max_length", self.model.config.max_length),
-        )
+    #     self.check_inputs(
+    #         input_length,
+    #         generate_kwargs.get("min_length", self.model.config.min_length),
+    #         generate_kwargs.get("max_length", self.model.config.max_length),
+    #     )
         
-        outputs = self.model.generate(**model_inputs, **generate_kwargs, return_dict_in_generate=True, output_scores=True)
-        output_ids = outputs.sequences
-        out_b = output_ids.shape[0]
-        if self.framework == "pt":
-            output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
-        elif self.framework == "tf":
-            output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
+    #     outputs = self.model.generate(**model_inputs, **generate_kwargs, return_dict_in_generate=True, output_scores=True)
+    #     output_ids = outputs.sequences
+    #     out_b = output_ids.shape[0]
+    #     if self.framework == "pt":
+    #         output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
+    #     elif self.framework == "tf":
+    #         output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
         
-        return {"output_ids": output_ids, "output_sequences": outputs.sequences, "output_scores": outputs.scores}
+    #     return {"output_ids": output_ids, "output_sequences": outputs.sequences, "output_scores": outputs.scores}
     
     def postprocess(self, model_outputs):
         guess_text = super().postprocess(model_outputs)[0]['generated_text']