Datasets:

---

MENGYEWEIWU

/

gcn

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
total_scenes: int64
similarity_threshold: double
top_k_similar: int64
emb_dim: int64
processing_method: string
bert_embeddings_dir: string
scenes: struct<0: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 1: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 2: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 3: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 4: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>>
device: string
training_config: struct<epochs: int64, learning_rate: double, early_stop_patience: int64, max_train_samples: null>
vs
total_scenes: int64
emb_dim: int64
model_type: string
processing_method: string
scenes: struct<0: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 1: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 2: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 3: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 4: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>>
device: string
training_config: struct<epochs: int64, learning_rate: double, early_stop_patience: int64, max_train_samples: null>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              total_scenes: int64
              similarity_threshold: double
              top_k_similar: int64
              emb_dim: int64
              processing_method: string
              bert_embeddings_dir: string
              scenes: struct<0: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 1: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 2: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 3: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>, 4: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, similarity_threshold: double, bert_embedding_dim: int64, top_k_similar: int64, processing_method: string>>
              device: string
              training_config: struct<epochs: int64, learning_rate: double, early_stop_patience: int64, max_train_samples: null>
              vs
              total_scenes: int64
              emb_dim: int64
              model_type: string
              processing_method: string
              scenes: struct<0: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 1: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 2: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 3: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>, 4: struct<scene: string, num_samples: int64, num_users: int64, num_items: int64, positive_samples: int64, negative_samples: int64, best_loss: double, final_epoch: int64, user_emb_shape: list<item: int64>, item_emb_shape: list<item: int64>, model_type: string, processing_method: string>>
              device: string
              training_config: struct<epochs: int64, learning_rate: double, early_stop_patience: int64, max_train_samples: null>