Upload MyTestPipeline

new_task.py

@@ -16,7 +16,7 @@ class MyTestPipeline(TextGenerationPipeline):
         elif self.framework == "tf":
             in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
 
-        outputs = self.model.generate(**model_inputs, **generate_kwargs, return_dict_in_generate=True, output_scores=True, max_new_tokens=10, temperature=0)
+        outputs = self.model.generate(**model_inputs, **generate_kwargs, return_dict_in_generate=True, output_scores=True, max_new_tokens=10, do_sample=False)
 
         output_ids = outputs.sequences
         out_b = output_ids.shape[0]
@@ -32,9 +32,10 @@ class MyTestPipeline(TextGenerationPipeline):
     def postprocess(self, model_outputs, **kwargs):
         guess_text = super().postprocess(model_outputs)[0]['generated_text'].split('\n')[-1].strip()
 
-        if guess_text[-1] == '.':
+        # verifying that the model did generate something (protects against indexing errors)
+        if len(guess_text) > 0 and guess_text[-1] == '.':
           guess_text = guess_text[:-1]
-
+          
         transition_scores = self.model.compute_transition_scores(model_outputs['generated_sequence'][0], model_outputs['output_scores'], normalize_logits=True)
         log_probs = np.round(np.exp(transition_scores.cpu().numpy()), 3)[0]
         guess_prob = np.product(log_probs)
@@ -43,5 +44,5 @@ class MyTestPipeline(TextGenerationPipeline):
 
         confidence = max(min(confidence, 1.0), 0.0)
 
-        return {'guess': guess_text, 'confidence': 1.0}
+        return {'guess': guess_text, 'confidence': confidence}