| tokenizer: PreTrainedTokenizerBase | |
| padding: Union[bool, str, PaddingStrategy] = True | |
| max_length: Optional[int] = None | |
| pad_to_multiple_of: Optional[int] = None | |
| def call(self, features): | |
| label_name = "label" if "label" in features[0].keys() else "labels" | |
| labels = [feature.pop(label_name) for feature in features] | |
| batch_size = len(features) | |
| num_choices = len(features[0]["input_ids"]) | |
| flattened_features = [ | |
| [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features | |
| ] | |
| flattened_features = sum(flattened_features, []) | |
| batch = self.tokenizer.pad( | |
| flattened_features, | |
| padding=self.padding, | |
| max_length=self.max_length, | |
| pad_to_multiple_of=self.pad_to_multiple_of, | |
| return_tensors="tf", | |
| ) | |
| batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()} | |
| batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64) | |
| return batch | |
| Evaluate | |
| Including a metric during training is often helpful for evaluating your model's performance. |