diff --git a/fastNLP/__init__.py b/fastNLP/__init__.py
index b856faff..915e382d 100644
--- a/fastNLP/__init__.py
+++ b/fastNLP/__init__.py
@@ -1,3 +1,3 @@
 
 from fastNLP.envs import *
-from fastNLP.core import Trainer, Evaluator
\ No newline at end of file
+from fastNLP.core import *
\ No newline at end of file
diff --git a/fastNLP/core/__init__.py b/fastNLP/core/__init__.py
index f1421c38..439f5886 100644
--- a/fastNLP/core/__init__.py
+++ b/fastNLP/core/__init__.py
@@ -57,9 +57,37 @@ __all__ = [
     "TorchPaddleDriver",
 
     # log
-    "logger"
+    "logger",
+    "print",
 
-    # 
+    # metrics
+    "Metric",
+    "Accuracy",
+    'SpanFPreRecMetric',
+    'ClassifyFPreRecMetric',
+
+    # samplers
+    'ReproducibleSampler',
+    'RandomSampler',
+    "SequentialSampler",
+    "SortedSampler",
+    'UnrepeatedSampler',
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSortedSampler",
+    "UnrepeatedSequentialSampler",
+    "ReproduceBatchSampler",
+    "BucketedBatchSampler",
+    "ReproducibleBatchSampler",
+    "RandomBatchSampler",
+
+    # utils
+    "cache_results",
+    "f_rich_progress",
+    "auto_param_call",
+    "seq_len_to_mask",
+
+    # vocabulary.py
+    'Vocabulary'
 ]
 from .callbacks import *
 from .collators import *
@@ -68,4 +96,7 @@ from .dataloaders import *
 from .dataset import *
 from .drivers import *
 from .log import *
-from .utils import *
\ No newline at end of file
+from .metrics import *
+from .samplers import *
+from .utils import *
+from .vocabulary import Vocabulary
\ No newline at end of file
diff --git a/fastNLP/core/callbacks/topk_saver.py b/fastNLP/core/callbacks/topk_saver.py
index 8c3f3811..25e66cb9 100644
--- a/fastNLP/core/callbacks/topk_saver.py
+++ b/fastNLP/core/callbacks/topk_saver.py
@@ -7,7 +7,7 @@ from copy import deepcopy
 from pathlib import Path
 from typing import Optional, Dict, Tuple, Callable, Union
 
-from fastNLP.core.utils import rank_zero_rm
+from ...envs.distributed import rank_zero_rm
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_LAUNCH_TIME
 from fastNLP.envs import rank_zero_call
diff --git a/fastNLP/core/collators/__init__.py b/fastNLP/core/collators/__init__.py
index 1e508689..3033c37e 100644
--- a/fastNLP/core/collators/__init__.py
+++ b/fastNLP/core/collators/__init__.py
@@ -8,6 +8,7 @@ __all__ = [
     "NullPadder",
     "RawNumberPadder",
     "RawSequencePadder",
+    "RawTensorPadder",
     'TorchNumberPadder',
     'TorchSequencePadder',
     'TorchTensorPadder',
diff --git a/fastNLP/core/collators/collator.py b/fastNLP/core/collators/collator.py
index 5c5abda4..9ea08d95 100644
--- a/fastNLP/core/collators/collator.py
+++ b/fastNLP/core/collators/collator.py
@@ -67,7 +67,7 @@ def _get_backend() -> str:
     # 方式 (2)
     for backend in CHECK_BACKEND:
         if backend in sys.modules:
-            logger.debug(f"sys.modules contains backend:{catch_backend[0]}.")
+            logger.debug(f"sys.modules contains backend:{backend}.")
             return backend
     for key, module in sys.modules.items():
         catch_backend = _check_module(module)
diff --git a/fastNLP/core/collators/padders/__init__.py b/fastNLP/core/collators/padders/__init__.py
index 09a5ca8d..11ffc07b 100644
--- a/fastNLP/core/collators/padders/__init__.py
+++ b/fastNLP/core/collators/padders/__init__.py
@@ -9,6 +9,7 @@ __all__ = [
 
     "RawNumberPadder",
     "RawSequencePadder",
+    "RawTensorPadder",
 
     'TorchNumberPadder',
     'TorchSequencePadder',
diff --git a/fastNLP/core/collators/padders/numpy_padder.py b/fastNLP/core/collators/padders/numpy_padder.py
index 4d507f2e..1113c91a 100644
--- a/fastNLP/core/collators/padders/numpy_padder.py
+++ b/fastNLP/core/collators/padders/numpy_padder.py
@@ -79,7 +79,7 @@ class NumpyTensorPadder(Padder):
     def pad(batch_field, pad_val, dtype):
         try:
             if not isinstance(batch_field[0], np.ndarray):
-                batch_field = [np.array(field.tolist()) for field in batch_field]
+                batch_field = [np.array(field.tolist(), dtype=dtype) for field in batch_field]
         except AttributeError:
             raise RuntimeError(f"If the field is not a np.ndarray (it is {type(batch_field[0])}), "
                                f"it must have tolist() method.")
diff --git a/fastNLP/core/collators/padders/paddle_padder.py b/fastNLP/core/collators/padders/paddle_padder.py
index 10d5a385..f7db6534 100644
--- a/fastNLP/core/collators/padders/paddle_padder.py
+++ b/fastNLP/core/collators/padders/paddle_padder.py
@@ -131,7 +131,7 @@ class PaddleTensorPadder(Padder):
     def pad(batch_field, pad_val, dtype):
         try:
             if not isinstance(batch_field[0], paddle.Tensor):
-                batch_field = [paddle.to_tensor(field.tolist()) for field in batch_field]
+                batch_field = [paddle.to_tensor(field.tolist(), dtype=dtype) for field in batch_field]
         except AttributeError:
             raise RuntimeError(f"If the field is not a paddle.Tensor (it is {type(batch_field[0])}), "
                                f"it must have tolist() method.")
@@ -143,8 +143,6 @@ class PaddleTensorPadder(Padder):
         tensor = paddle.full(max_shape, fill_value=pad_val, dtype=dtype)
         for i, field in enumerate(batch_field):
             slices = (i, ) + tuple(slice(0, s) for s in shapes[i])
-            if isinstance(field, np.ndarray):
-                field = paddle.to_tensor(field)
             tensor[slices] = field
         return tensor
 
diff --git a/fastNLP/core/collators/padders/torch_padder.py b/fastNLP/core/collators/padders/torch_padder.py
index 18f414e8..f1940380 100644
--- a/fastNLP/core/collators/padders/torch_padder.py
+++ b/fastNLP/core/collators/padders/torch_padder.py
@@ -114,7 +114,7 @@ class TorchTensorPadder(Padder):
     def pad(batch_field, pad_val, dtype):
         try:
             if not isinstance(batch_field[0], torch.Tensor):
-                batch_field = [torch.tensor(field.tolist()) for field in batch_field]
+                batch_field = [torch.tensor(field.tolist(), dtype=dtype) for field in batch_field]
         except AttributeError:
             raise RuntimeError(f"If the field is not a torch.Tensor (it is {type(batch_field[0])}), "
                                f"it must have tolist() method.")
@@ -124,8 +124,6 @@ class TorchTensorPadder(Padder):
         tensor = torch.full(max_shape, fill_value=pad_val, dtype=dtype)
         for i, field in enumerate(batch_field):
             slices = (i, ) + tuple(slice(0, s) for s in shapes[i])
-            if isinstance(field, np.ndarray):
-                field = torch.from_numpy(field)
             tensor[slices] = field
         return tensor
 
diff --git a/fastNLP/core/drivers/paddle_driver/fleet.py b/fastNLP/core/drivers/paddle_driver/fleet.py
index 73342748..f3a739f0 100644
--- a/fastNLP/core/drivers/paddle_driver/fleet.py
+++ b/fastNLP/core/drivers/paddle_driver/fleet.py
@@ -18,9 +18,9 @@ from fastNLP.core.utils import (
     auto_param_call,
     check_user_specific_params,
     paddle_move_data_to_device,
-    is_in_paddle_dist,
-    rank_zero_rm
+    is_in_paddle_dist
 )
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.core.samplers import (
     ReproduceBatchSampler,
     ReproducibleSampler,
diff --git a/fastNLP/core/log/__init__.py b/fastNLP/core/log/__init__.py
index 3cb6d4dc..d1d95f20 100644
--- a/fastNLP/core/log/__init__.py
+++ b/fastNLP/core/log/__init__.py
@@ -1,6 +1,8 @@
 __all__ = [
-    'logger'
+    'logger',
+    "print"
 ]
 
 from .logger import logger
+from .print import print
 
diff --git a/fastNLP/core/metrics/__init__.py b/fastNLP/core/metrics/__init__.py
index 82bca331..f7d60606 100644
--- a/fastNLP/core/metrics/__init__.py
+++ b/fastNLP/core/metrics/__init__.py
@@ -1,16 +1,11 @@
 __all__ = [
     "Metric",
     "Accuracy",
-    'Backend',
-    'AutoBackend',
-    'PaddleBackend',
-    'TorchBackend',
     'SpanFPreRecMetric',
     'ClassifyFPreRecMetric',
 ]
 
 from .metric import Metric
 from .accuracy import Accuracy
-from .backend import Backend, AutoBackend, PaddleBackend, TorchBackend
 from .span_f1_pre_rec_metric import SpanFPreRecMetric
 from .classify_f1_pre_rec_metric import ClassifyFPreRecMetric
diff --git a/fastNLP/core/utils/__init__.py b/fastNLP/core/utils/__init__.py
index 9fb538a9..4af6a24a 100644
--- a/fastNLP/core/utils/__init__.py
+++ b/fastNLP/core/utils/__init__.py
@@ -23,8 +23,6 @@ __all__ = [
     'Option',
     'deprecated',
     'seq_len_to_mask',
-    'rank_zero_rm',
-    'rank_zero_mkdir'
 ]
 
 from .cache_results import cache_results
@@ -36,7 +34,6 @@ from .torch_paddle_utils import torch_paddle_move_data_to_device
 from .torch_utils import torch_move_data_to_device
 from .utils import get_fn_arg_names, auto_param_call, check_user_specific_params, \
     dataclass_to_dict, match_and_substitute_params, apply_to_collection, nullcontext, pretty_table_printer, Option, \
-    deprecated, seq_len_to_mask, rank_zero_rm, rank_zero_mkdir
-from ..dataloaders.utils import indice_collate_wrapper
+    deprecated, seq_len_to_mask
 
 
diff --git a/fastNLP/core/utils/utils.py b/fastNLP/core/utils/utils.py
index 91b3c8f6..93f38e2a 100644
--- a/fastNLP/core/utils/utils.py
+++ b/fastNLP/core/utils/utils.py
@@ -22,8 +22,6 @@ import numpy as np
 from pathlib import Path
 
 from fastNLP.core.log import logger
-from fastNLP.envs import FASTNLP_GLOBAL_RANK
-
 
 __all__ = [
     'get_fn_arg_names',
@@ -37,8 +35,6 @@ __all__ = [
     'Option',
     'deprecated',
     'seq_len_to_mask',
-    'rank_zero_rm',
-    'rank_zero_mkdir'
 ]
 
 
@@ -609,54 +605,6 @@ def wait_filepath(path, exist=True):
             logger.warning(f"Waiting path:{path} to {msg} for {count*0.01} seconds...")
 
 
-
-def rank_zero_rm(path: Optional[Union[str, Path]]):
-    """
-    这个是因为在分布式文件系统中可能会发生错误，rank0下发删除成功后就运行走了，但实际的删除需要rank0的机器发送到远程文件系统再去执行，这个时候
-        在rank0那里，确实已经删除成功了，但是在远程文件系统那里这个操作还没完成，rank1读取的时候还是读取到存在这个文件；
-    该函数会保证所有进程都检测到 path 删除之后才退出，请保证不同进程上 path 是完全一样的，否则会陷入死锁状态。
-
-    :param path:
-    :return:
-    """
-    if int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) == 0:
-        if path is None:
-            return
-        if isinstance(path, str):
-            path = Path(path)
-        if not path.exists():
-            return
-        _recursive_rm(path)
-
-
-def _recursive_rm(path: Path):
-    if path.is_file() or path.is_symlink():
-        if path.exists():
-            try:
-                path.unlink()
-            except Exception:
-                pass
-        return
-    for sub_path in list(path.iterdir()):
-        _recursive_rm(sub_path)
-    path.rmdir()
-
-
-def rank_zero_mkdir(path: Optional[Union[str, Path]]):
-    """
-    注意该函数是用来创建文件夹，如果需要创建一个文件，不要使用该函数；
-    该函数会保证所有进程都检测到 path 创建之后才退出，请保证不同进程上 path 是完全一样的，否则会陷入死锁状态。
-
-    """
-    if int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) == 0:
-        if path is None:
-            return
-        if isinstance(path, str):
-            path = Path(path)
-
-        path.mkdir(parents=True, exist_ok=True)
-
-
 def get_class_that_defined_method(method):
     """
     给定一个method，返回这个 method 的 class 的对象
diff --git a/fastNLP/envs/__init__.py b/fastNLP/envs/__init__.py
index bc09c33b..6c5e857e 100644
--- a/fastNLP/envs/__init__.py
+++ b/fastNLP/envs/__init__.py
@@ -3,12 +3,17 @@ r"""
 """
 __all__ = [
     'dump_fastnlp_backend',
-    'is_cur_env_distributed',
-    'get_global_rank',
+
+    # utils
+    'get_gpu_count',
+
+    # distributed
+    "rank_zero_rm",
     'rank_zero_call',
+    'get_global_rank',
+    'fastnlp_no_sync_context',
     'all_rank_call_context',
-    'get_gpu_count',
-    'fastnlp_no_sync_context'
+    'is_cur_env_distributed',
 ]
 
 
diff --git a/fastNLP/envs/distributed.py b/fastNLP/envs/distributed.py
index 34515c2c..3d87c8b2 100644
--- a/fastNLP/envs/distributed.py
+++ b/fastNLP/envs/distributed.py
@@ -1,6 +1,7 @@
 import os
 from functools import wraps
-from typing import Callable, Any, Optional
+from pathlib import Path
+from typing import Callable, Any, Optional, Union
 from contextlib import contextmanager
 
 __all__ = [
@@ -8,7 +9,8 @@ __all__ = [
     'get_global_rank',
     'rank_zero_call',
     'all_rank_call_context',
-    'fastnlp_no_sync_context'
+    'fastnlp_no_sync_context',
+    "rank_zero_rm"
 ]
 
 from fastNLP.envs.env import FASTNLP_GLOBAL_RANK, FASTNLP_NO_SYNC
@@ -96,3 +98,35 @@ def all_rank_call_context():
         os.environ[FASTNLP_GLOBAL_RANK] = old_fastnlp_global_rank
     else:
         os.environ.pop(FASTNLP_GLOBAL_RANK)
+
+
+def rank_zero_rm(path: Optional[Union[str, Path]]):
+    """
+    这个是因为在分布式文件系统中可能会发生错误，rank0下发删除成功后就运行走了，但实际的删除需要rank0的机器发送到远程文件系统再去执行，这个时候
+        在rank0那里，确实已经删除成功了，但是在远程文件系统那里这个操作还没完成，rank1读取的时候还是读取到存在这个文件；
+    该函数会保证所有进程都检测到 path 删除之后才退出，请保证不同进程上 path 是完全一样的，否则会陷入死锁状态。
+
+    :param path:
+    :return:
+    """
+    if int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) == 0:
+        if path is None:
+            return
+        if isinstance(path, str):
+            path = Path(path)
+        if not path.exists():
+            return
+        _recursive_rm(path)
+
+
+def _recursive_rm(path: Path):
+    if path.is_file() or path.is_symlink():
+        if path.exists():
+            try:
+                path.unlink()
+            except Exception:
+                pass
+        return
+    for sub_path in list(path.iterdir()):
+        _recursive_rm(sub_path)
+    path.rmdir()
\ No newline at end of file
diff --git a/fastNLP/envs/env.py b/fastNLP/envs/env.py
index 74d833e0..9cc05a02 100644
--- a/fastNLP/envs/env.py
+++ b/fastNLP/envs/env.py
@@ -22,7 +22,7 @@ FASTNLP_GLOBAL_RANK = "FASTNLP_GLOBAL_RANK"
 FASTNLP_LOG_LEVEL = "FASTNLP_LOG_LEVEL"
 
 
-# todo 每一个分布式的 driver 都应当正确地设立该值；具体可见 ddp；
+# 每一个分布式的 driver 都应当正确地设立该值；具体可见 ddp；
 # FASTNLP_LAUNCH_TIME 记录了当前 fastNLP 脚本启动的时间。
 FASTNLP_LAUNCH_TIME = "FASTNLP_LAUNCH_TIME"
 
@@ -42,7 +42,7 @@ USER_CUDA_VISIBLE_DEVICES = 'USER_CUDA_VISIBLE_DEVICES'
 # 用于在 torch.distributed.launch 时移除传入的 rank ，在 pytorch 中有使用。值的可选为 [0, 1]
 FASTNLP_REMOVE_LOCAL_RANK = 'FASTNLP_REMOVE_LOCAL_RANK'
 
-# todo 注释
+# 检测到当前脚本是通过类似 python -m torch.launch 启动的话设置这个变量为1
 FASTNLP_BACKEND_LAUNCH = "FASTNLP_BACKEND_LAUNCH"
 
 # fastNLP中用于关闭 fastNLP 1.barrier 与 2.gather/broadcast 。默认为 '0' 表示不关闭；为 '1' 表示 fastNLP 的 barrier 不执行；
diff --git a/tests/core/callbacks/test_checkpoint_callback_torch.py b/tests/core/callbacks/test_checkpoint_callback_torch.py
index 2de21825..60dcc862 100644
--- a/tests/core/callbacks/test_checkpoint_callback_torch.py
+++ b/tests/core/callbacks/test_checkpoint_callback_torch.py
@@ -11,7 +11,7 @@ from fastNLP.core.controllers.trainer import Trainer
 from fastNLP.envs import FASTNLP_LAUNCH_TIME, FASTNLP_DISTRIBUTED_CHECK
 
 from tests.helpers.utils import magic_argv_env_context
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from tests.helpers.datasets.torch_data import TorchArgMaxDataset
 from torchmetrics import Accuracy
diff --git a/tests/core/callbacks/test_more_evaluate_callback.py b/tests/core/callbacks/test_more_evaluate_callback.py
index 08c6f8e2..9c32c20b 100644
--- a/tests/core/callbacks/test_more_evaluate_callback.py
+++ b/tests/core/callbacks/test_more_evaluate_callback.py
@@ -20,7 +20,7 @@ from fastNLP.core.controllers.trainer import Trainer
 from fastNLP.envs import FASTNLP_LAUNCH_TIME, FASTNLP_DISTRIBUTED_CHECK
 
 from tests.helpers.utils import magic_argv_env_context
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from tests.helpers.datasets.torch_data import TorchArgMaxDataset
 from torchmetrics import Accuracy
diff --git a/tests/core/collators/test_collator.py b/tests/core/collators/test_collator.py
index ba1e7e08..65101321 100644
--- a/tests/core/collators/test_collator.py
+++ b/tests/core/collators/test_collator.py
@@ -83,7 +83,7 @@ class TestCollator:
         assert raw_pad_batch == collator(dict_batch)
         collator = Collator(backend='raw')
         raw_pad_lst = [['1', '2'], [['1'], ['2', '2']], [1, 2], [[1, 0], [2, 2]], [[[1, 0], [0, 0]], [[1, 0], [1, 2]]],
-                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [np.ones(1), np.ones(2)], [{'1': '1'}, {'2': '2'}],
+                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [[1, 0], [1, 1]], [{'1': '1'}, {'2': '2'}],
                        [{'1'}, {'2'}]]
         findListDiff(raw_pad_lst, collator(list_batch))
 
@@ -194,7 +194,7 @@ class TestCollator:
         collator.set_ignore('_0', '_3', '_1')
         collator.set_pad('_4', pad_val=None)
         raw_pad_lst = [[1, 2], [[[1]], [[1], [1, 2]]],
-                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [np.ones(1), np.ones(2)], [{'1': '1'}, {'2': '2'}],
+                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [[1, 0], [1, 1]], [{'1': '1'}, {'2': '2'}],
                        [{'1'}, {'2'}]]
         findListDiff(raw_pad_lst, collator(list_batch))
 
@@ -210,7 +210,7 @@ class TestCollator:
         collator.set_pad('_2', backend='numpy')
         collator.set_pad('_4', backend='numpy', pad_val=100)
         raw_pad_lst = [np.array([1, 2]), np.array([[[1, 100], [100, 100]], [[1, 100], [1, 2]]]),
-                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [np.ones(1), np.ones(2)], [{'1': '1'}, {'2': '2'}],
+                       [1.1, 2.1], [[1.1], [2.1]], [True, False], [[1, 0], [1, 1]], [{'1': '1'}, {'2': '2'}],
                        [{'1'}, {'2'}]]
         findListDiff(raw_pad_lst, collator(list_batch))
 
diff --git a/tests/core/controllers/test_trainer_wo_evaluator_torch.py b/tests/core/controllers/test_trainer_wo_evaluator_torch.py
index 102ab310..e3d90e9b 100644
--- a/tests/core/controllers/test_trainer_wo_evaluator_torch.py
+++ b/tests/core/controllers/test_trainer_wo_evaluator_torch.py
@@ -13,7 +13,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
 from tests.helpers.callbacks.helper_callbacks import RecordLossCallback
 from tests.helpers.callbacks.helper_callbacks_torch import RecordAccumulationStepsCallback_Torch
 from tests.helpers.utils import magic_argv_env_context, Capturing
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 if _NEED_IMPORT_TORCH:
     import torch.distributed as dist
diff --git a/tests/core/drivers/paddle_driver/test_fleet.py b/tests/core/drivers/paddle_driver/test_fleet.py
index a184bb11..3b3f15ec 100644
--- a/tests/core/drivers/paddle_driver/test_fleet.py
+++ b/tests/core/drivers/paddle_driver/test_fleet.py
@@ -12,7 +12,7 @@ from fastNLP.core.samplers import (
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset
 from tests.helpers.utils import magic_argv_env_context
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 if _NEED_IMPORT_PADDLE:
     import paddle
diff --git a/tests/core/drivers/paddle_driver/test_single_device.py b/tests/core/drivers/paddle_driver/test_single_device.py
index b8ccd802..ba243106 100644
--- a/tests/core/drivers/paddle_driver/test_single_device.py
+++ b/tests/core/drivers/paddle_driver/test_single_device.py
@@ -7,7 +7,7 @@ from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
 if _NEED_IMPORT_PADDLE:
     import paddle
diff --git a/tests/core/drivers/torch_driver/test_ddp.py b/tests/core/drivers/torch_driver/test_ddp.py
index d6f0ee77..0e3f99ad 100644
--- a/tests/core/drivers/torch_driver/test_ddp.py
+++ b/tests/core/drivers/torch_driver/test_ddp.py
@@ -12,7 +12,7 @@ from fastNLP.core.samplers import (
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDataset
 from tests.helpers.utils import magic_argv_env_context
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 if _NEED_IMPORT_TORCH:
     import torch
diff --git a/tests/core/drivers/torch_driver/test_single_device.py b/tests/core/drivers/torch_driver/test_single_device.py
index ef60e2b6..9115ed19 100644
--- a/tests/core/drivers/torch_driver/test_single_device.py
+++ b/tests/core/drivers/torch_driver/test_single_device.py
@@ -7,7 +7,7 @@ from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDataset
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
 if _NEED_IMPORT_TORCH:
     import torch
diff --git a/tests/core/log/test_logger_torch.py b/tests/core/log/test_logger_torch.py
index 13a758e9..7d45782c 100644
--- a/tests/core/log/test_logger_torch.py
+++ b/tests/core/log/test_logger_torch.py
@@ -7,7 +7,7 @@ import re
 import pytest
 
 from fastNLP.envs.env import FASTNLP_LAUNCH_TIME
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.core.log.logger import logger
 
 from tests.helpers.utils import magic_argv_env_context, recover_logger
diff --git a/tests/core/utils/test_cache_results.py b/tests/core/utils/test_cache_results.py
index 77c618bb..efef9f10 100644
--- a/tests/core/utils/test_cache_results.py
+++ b/tests/core/utils/test_cache_results.py
@@ -6,7 +6,7 @@ import sys
 sys.path.append(os.path.join(os.path.dirname(__file__), '../../..'))
 
 from fastNLP.core.utils.cache_results import cache_results
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 
 
 def get_subprocess_results(cmd):
diff --git a/tests/envs/test_set_backend.py b/tests/envs/test_set_backend.py
index 170110ce..c45acd7b 100644
--- a/tests/envs/test_set_backend.py
+++ b/tests/envs/test_set_backend.py
@@ -3,7 +3,7 @@ import pytest
 
 from fastNLP.envs.set_backend import dump_fastnlp_backend
 from tests.helpers.utils import Capturing
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 
 
 def test_dump_fastnlp_envs():
diff --git a/tests/modules/mix_modules/_test_mix_module.py b/tests/modules/mix_modules/_test_mix_module.py
index 700e0cfe..87206fd6 100644
--- a/tests/modules/mix_modules/_test_mix_module.py
+++ b/tests/modules/mix_modules/_test_mix_module.py
@@ -9,7 +9,7 @@ import numpy as np
 
 from fastNLP.modules.mix_modules.mix_module import MixModule
 from fastNLP.modules.mix_modules.utils import paddle2torch, torch2paddle
-from fastNLP.core import rank_zero_rm
+from fastNLP.envs.distributed import rank_zero_rm
 
 
 ############################################################################
diff --git a/tutorials/fastnlp_tutorial_0.ipynb b/tutorials/fastnlp_tutorial_0.ipynb
index 28fcfddf..26675ecf 100644
--- a/tutorials/fastnlp_tutorial_0.ipynb
+++ b/tutorials/fastnlp_tutorial_0.ipynb
@@ -136,7 +136,7 @@
     "在`fastNLP 0.8`中，使用`pytorch.nn.Module`搭建需要训练的模型，在搭建模型过程中，除了\n",
     "\n",
     "  添加`pytorch`要求的`forward`方法外，还需要添加 **`train_step`** 和 **`evaluate_step`** 这两个方法\n",
-    "***\n",
+    "\n",
     "```python\n",
     "class Model(torch.nn.Module):\n",
     "    def __init__(self):\n",
@@ -177,9 +177,7 @@
     "\n",
     "  从模块角度，该字典的键值和`metric`中的`update`函数的签名一致，这样的机制在传参时被称为“**参数匹配**”\n",
     "\n",
-    "***\n",
-    "\n",
-    "![fastNLP 0.8 中，Trainer 和 Evaluator 的关系图](./figures/T0-fig-trainer-and-evaluator.png)"
+    "<img src=\"./figures/T0-fig-trainer-and-evaluator.png\" width=\"80%\" height=\"80%\" align=\"center\"></img>"
    ]
   },
   {
@@ -206,7 +204,7 @@
     "&emsp; 而在`Trainer`和`Evaluator`中的参数`model_wo_auto_param_call`被设置为`True`时\n",
     "\n",
     "&emsp; &emsp; `fastNLP 0.8`会将`batch`直接传给模型的`train_step`、`evaluate_step`或`forward`函数\n",
-    "***\n",
+    "\n",
     "```python\n",
     "class Dataset(torch.utils.data.Dataset):\n",
     "    def __init__(self, x, y):\n",
@@ -253,7 +251,7 @@
    "id": "5314482b",
    "metadata": {
     "pycharm": {
-     "is_executing": false
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -641,11 +639,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.43</span><span style=\"font-weight: bold\">}</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.29</span><span style=\"font-weight: bold\">}</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.43\u001b[0m\u001b[1m}\u001b[0m\n"
+       "\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.29\u001b[0m\u001b[1m}\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -654,7 +652,7 @@
     {
      "data": {
       "text/plain": [
-       "{'acc#acc': 0.43}"
+       "{'acc#acc': 0.29}"
       ]
      },
      "execution_count": 9,
diff --git a/tutorials/fastnlp_tutorial_1.ipynb b/tutorials/fastnlp_tutorial_1.ipynb
new file mode 100644
index 00000000..11bd2219
--- /dev/null
+++ b/tutorials/fastnlp_tutorial_1.ipynb
@@ -0,0 +1,1156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cdc25fcd",
+   "metadata": {},
+   "source": [
+    "# T1. dataset 和 vocabulary 的基本使用\n",
+    "\n",
+    "&emsp; 1 &ensp; dataset 的使用与结构\n",
+    " \n",
+    "&emsp; &emsp; 1.1 &ensp; dataset 的结构与创建\n",
+    "\n",
+    "&emsp; &emsp; 1.2 &ensp; dataset 的数据预处理\n",
+    "\n",
+    "&emsp; &emsp; 1.3 &ensp; 延伸：instance 和 field\n",
+    "\n",
+    "&emsp; 2 &ensp; vocabulary 的结构与使用\n",
+    "\n",
+    "&emsp; &emsp; 2.1 &ensp; vocabulary 的创建与修改\n",
+    "\n",
+    "&emsp; &emsp; 2.2 &ensp; vocabulary 与 OOV 问题\n",
+    "\n",
+    "&emsp; 3 &ensp; dataset 和 vocabulary 的组合使用\n",
+    " \n",
+    "&emsp; &emsp; 3.1 &ensp; 从 dataframe 中加载 dataset\n",
+    "\n",
+    "&emsp; &emsp; 3.2 &ensp; 从 dataset 中获取 vocabulary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb18a22",
+   "metadata": {},
+   "source": [
+    "## 1. dataset 的基本使用\n",
+    "\n",
+    "### 1.1  dataset 的结构与创建\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`DataSet`模块表示数据集，**`dataset`类似于关系型数据库中的数据表**（下文统一为小写`dataset`）\n",
+    "\n",
+    "&emsp; **主要包含`field`字段和`instance`实例两个元素**，对应`table`中的`field`字段和`record`记录\n",
+    "\n",
+    "在`fastNLP 0.8`中，`DataSet`模块被定义在`fastNLP.core.dataset`路径下，导入该模块后，最简单的\n",
+    "\n",
+    "&emsp; 初始化方法，即将字典形式的表格 **`{'field1': column1, 'field2': column2, ...}`** 传入构造函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a1d69ad2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP.core.dataset import DataSet\n",
+    "\n",
+    "data = {'idx': [0, 1, 2],  \n",
+    "        'sentence':[\"This is an apple .\", \"I like apples .\", \"Apples are good for our health .\"],\n",
+    "        'words': [['This', 'is', 'an', 'apple', '.'], \n",
+    "                  ['I', 'like', 'apples', '.'], \n",
+    "                  ['Apples', 'are', 'good', 'for', 'our', 'health', '.']],\n",
+    "        'num': [5, 4, 7]}\n",
+    "\n",
+    "dataset = DataSet(data)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9260fdc6",
+   "metadata": {},
+   "source": [
+    "&emsp; 在`dataset`的实例中，字段`field`的名称和实例`instance`中的字符串也可以中文"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d72ef00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------+--------------------+------------------------+------+\n",
+      "| 序号 | 句子               | 字符                   | 长度 |\n",
+      "+------+--------------------+------------------------+------+\n",
+      "| 0    | 生活就像海洋，     | ['生', '活', '就', ... | 7    |\n",
+      "| 1    | 只有意志坚强的人， | ['只', '有', '意', ... | 9    |\n",
+      "| 2    | 才能到达彼岸。     | ['才', '能', '到', ... | 7    |\n",
+      "+------+--------------------+------------------------+------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "temp = {'序号': [0, 1, 2],  \n",
+    "        '句子':[\"生活就像海洋，\", \"只有意志坚强的人，\", \"才能到达彼岸。\"],\n",
+    "        '字符': [['生', '活', '就', '像', '海', '洋', '，'], \n",
+    "                  ['只', '有', '意', '志', '坚', '强', '的', '人', '，'], \n",
+    "                  ['才', '能', '到', '达', '彼', '岸', '。']],\n",
+    "        '长度': [7, 9, 7]}\n",
+    "\n",
+    "chinese = DataSet(temp)\n",
+    "print(chinese)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "202e5490",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`drop`方法可以删除满足条件的实例，这里使用了python中的`lambda`表达式\n",
+    "\n",
+    "&emsp; 注一：在`drop`方法中，通过设置`inplace`参数将删除对应实例后的`dataset`作为一个新的实例生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09b478f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1969418794120 1971237588872\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropped = dataset\n",
+    "dropped = dropped.drop(lambda ins:ins['num'] < 5, inplace=False)\n",
+    "print(id(dropped), id(dataset))\n",
+    "print(dropped)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa277674",
+   "metadata": {},
+   "source": [
+    "&emsp; 注二：在`fastNLP 0.8`中，**对`dataset`使用等号**，**其效果是传引用**，**而不是赋值**（？？？）\n",
+    "\n",
+    "&emsp; &emsp; 如下所示，**`dropped`和`dataset`具有相同`id`**，**对`dropped`执行删除操作`dataset`同时会被修改**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "77c8583a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1971237588872 1971237588872\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropped = dataset\n",
+    "dropped.drop(lambda ins:ins['num'] < 5)\n",
+    "print(id(dropped), id(dataset))\n",
+    "print(dropped)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a76199dc",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`delet_instance`方法可以删除对应序号的`instance`实例，序号从0开始"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8824b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+--------------------+------------------------+-----+\n",
+      "| idx | sentence           | words                  | num |\n",
+      "+-----+--------------------+------------------------+-----+\n",
+      "| 0   | This is an apple . | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .    | ['I', 'like', 'appl... | 4   |\n",
+      "+-----+--------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.delete_instance(2)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4fa9f33",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`delet_field`方法可以删除对应名称的`field`字段"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f68ddb40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+--------------------+------------------------------+\n",
+      "| idx | sentence           | words                        |\n",
+      "+-----+--------------------+------------------------------+\n",
+      "| 0   | This is an apple . | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .    | ['I', 'like', 'apples', '... |\n",
+      "+-----+--------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.delete_field('num')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1e9d42c",
+   "metadata": {},
+   "source": [
+    "### 1.2  dataset 的数据预处理\n",
+    "\n",
+    "在`dataset`模块中，`apply`、`apply_field`、`apply_more`和`apply_field_more`函数可以进行简单的数据预处理\n",
+    "\n",
+    "&emsp; **`apply`和`apply_more`针对整条实例**，**`apply_field`和`apply_field_more`仅针对实例的部分字段**\n",
+    "\n",
+    "&emsp; **`apply`和`apply_field`仅针对单个字段**，**`apply_more`和`apply_field_more`则可以针对多个字段**\n",
+    "\n",
+    "&emsp; **`apply`和`apply_field`返回的是个列表**，**`apply_more`和`apply_field_more`返回的是个字典**\n",
+    "\n",
+    "***\n",
+    "\n",
+    "`apply`的参数包括一个函数`func`和一个新字段名`new_field_name`，函数`func`的处理对象是`dataset`模块中\n",
+    "\n",
+    "&emsp; 的每个`instance`实例，函数`func`的处理结果存放在`new_field_name`对应的新建字段内"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "72a0b5f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'idx': [0, 1, 2], \n",
+    "        'sentence':[\"This is an apple .\", \"I like apples .\", \"Apples are good for our health .\"], }\n",
+    "dataset = DataSet(data)\n",
+    "dataset.apply(lambda ins: ins['sentence'].split(), new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c10275ee",
+   "metadata": {},
+   "source": [
+    "&emsp; **`apply`使用的函数可以是一个基于`lambda`表达式的匿名函数**，**也可以是一个自定义的函数**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1a8631f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "\n",
+    "def get_words(instance):\n",
+    "    sentence = instance['sentence']\n",
+    "    words = sentence.split()\n",
+    "    return words\n",
+    "\n",
+    "dataset.apply(get_words, new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64abf745",
+   "metadata": {},
+   "source": [
+    "`apply_field`的参数，除了函数`func`外还有`field_name`和`new_field_name`，该函数`func`的处理对象仅\n",
+    "\n",
+    "&emsp; 是`dataset`模块中的每个`field_name`对应的字段内容，处理结果存放在`new_field_name`对应的新建字段内"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "057c1d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_field(lambda sent:sent.split(), field_name='sentence', new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a9cc8b2",
+   "metadata": {},
+   "source": [
+    "`apply_more`的参数只有函数`func`，函数`func`的处理对象是`dataset`模块中的每个`instance`实例\n",
+    "\n",
+    "&emsp; 要求函数`func`返回一个字典，根据字典的`key-value`确定存储在`dataset`中的字段名称与内容"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "51e2f02c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_more(lambda ins:{'words': ins['sentence'].split(), 'num': len(ins['sentence'].split())})\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02d2b7ef",
+   "metadata": {},
+   "source": [
+    "`apply_more`的参数只有函数`func`，函数`func`的处理对象是`dataset`模块中的每个`instance`实例\n",
+    "\n",
+    "&emsp; 要求函数`func`返回一个字典，根据字典的`key-value`确定存储在`dataset`中的字段名称与内容"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "db4295d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_field_more(lambda sent:{'words': sent.split(), 'num': len(sent.split())}, \n",
+    "                         field_name='sentence')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c09e592",
+   "metadata": {},
+   "source": [
+    "### 1.3 延伸：instance 和 field\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`Instance`模块表示数据集`dataset`中的每条数据，被称为实例\n",
+    "\n",
+    "&emsp; 构造方式类似于构造一个字典，通过键值相同的`Instance`列表，也可以初始化一个`dataset`，代码如下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "012f537c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP.core.dataset import DataSet\n",
+    "from fastNLP.core.dataset import Instance\n",
+    "\n",
+    "dataset = DataSet([\n",
+    "    Instance(sentence=\"This is an apple .\",\n",
+    "        words=['This', 'is', 'an', 'apple', '.'],\n",
+    "        num=5),\n",
+    "    Instance(sentence=\"I like apples .\",\n",
+    "        words=['I', 'like', 'apples', '.'],\n",
+    "        num=4),\n",
+    "    Instance(sentence=\"Apples are good for our health .\",\n",
+    "        words=['Apples', 'are', 'good', 'for', 'our', 'health', '.'],\n",
+    "        num=7),\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fafb1ef",
+   "metadata": {},
+   "source": [
+    "&emsp; 通过`items`、`keys`和`values`方法，可以分别获得`dataset`的`item`列表、`key`列表、`value`列表"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a4c1c10d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_items([('sentence', 'This is an apple .'), ('words', ['This', 'is', 'an', 'apple', '.']), ('num', 5)])\n",
+      "dict_keys(['sentence', 'words', 'num'])\n",
+      "dict_values(['This is an apple .', ['This', 'is', 'an', 'apple', '.'], 5])\n"
+     ]
+    }
+   ],
+   "source": [
+    "ins = Instance(sentence=\"This is an apple .\", words=['This', 'is', 'an', 'apple', '.'], num=5)\n",
+    "\n",
+    "print(ins.items())\n",
+    "print(ins.keys())\n",
+    "print(ins.values())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5459a2d",
+   "metadata": {},
+   "source": [
+    "&emsp; 通过`add_field`方法，可以在`Instance`实例中，通过参数`field_name`添加字段，通过参数`field`赋值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55376402",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------------------+-----+-----+\n",
+      "| sentence           | words                  | num | idx |\n",
+      "+--------------------+------------------------+-----+-----+\n",
+      "| This is an apple . | ['This', 'is', 'an'... | 5   | 0   |\n",
+      "+--------------------+------------------------+-----+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "ins.add_field(field_name='idx', field=0)\n",
+    "print(ins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49caaa9c",
+   "metadata": {},
+   "source": [
+    "在`fastNLP 0.8`中，使用`FieldArray`模块表示数据集`dataset`中的每条字段名（注：没有`field`类）\n",
+    "\n",
+    "&emsp; 通过`get_all_fields`方法可以获取`dataset`的字段列表\n",
+    "\n",
+    "&emsp; 通过`get_field_names`方法可以获取`dataset`的字段名称列表，代码如下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fe15f4c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentence': <fastNLP.core.dataset.field.FieldArray at 0x1ca8a879d08>,\n",
+       " 'words': <fastNLP.core.dataset.field.FieldArray at 0x1ca8a879d88>,\n",
+       " 'num': <fastNLP.core.dataset.field.FieldArray at 0x1ca8a879e08>}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.get_all_fields()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5433815c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['num', 'sentence', 'words']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.get_field_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4964eeed",
+   "metadata": {},
+   "source": [
+    "其他`dataset`的基本使用：通过`in`或者`has_field`方法可以判断`dataset`的是否包含某种字段\n",
+    "\n",
+    "&emsp; 通过`rename_field`方法可以更改`dataset`中的字段名称；通过`concat`方法可以实现两个`dataset`中的拼接\n",
+    "\n",
+    "&emsp; 通过`len`可以统计`dataset`中的实例数目；`dataset`的全部变量与函数可以通过`dir(dataset)`查询"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "25ce5488",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 False\n",
+      "6 True\n",
+      "+------------------------------+------------------------------+--------+\n",
+      "| sentence                     | words                        | length |\n",
+      "+------------------------------+------------------------------+--------+\n",
+      "| This is an apple .           | ['This', 'is', 'an', 'app... | 5      |\n",
+      "| I like apples .              | ['I', 'like', 'apples', '... | 4      |\n",
+      "| Apples are good for our h... | ['Apples', 'are', 'good',... | 7      |\n",
+      "| This is an apple .           | ['This', 'is', 'an', 'app... | 5      |\n",
+      "| I like apples .              | ['I', 'like', 'apples', '... | 4      |\n",
+      "| Apples are good for our h... | ['Apples', 'are', 'good',... | 7      |\n",
+      "+------------------------------+------------------------------+--------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(dataset), dataset.has_field('length')) \n",
+    "if 'num' in dataset:\n",
+    "    dataset.rename_field('num', 'length')\n",
+    "elif 'length' in dataset:\n",
+    "    dataset.rename_field('length', 'num')\n",
+    "dataset.concat(dataset)\n",
+    "print(len(dataset), dataset.has_field('length'))  \n",
+    "print(dataset)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e30a6cd7",
+   "metadata": {},
+   "source": [
+    "## 2. vocabulary 的结构与使用\n",
+    "\n",
+    "### 2.1 vocabulary 的创建与修改\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`Vocabulary`模块表示词汇表，**`vocabulary`的核心是从单词到序号的映射**\n",
+    "\n",
+    "&emsp; 可以直接通过构造函数实例化，通过查找`word2idx`属性，可以找到`vocabulary`映射对应的字典实现\n",
+    "\n",
+    "&emsp; **默认补零`padding`用`<pad>`表示**，**对应序号为0**；**未知单词`unknown`用`<unk>`表示**，**对应序号1**\n",
+    "\n",
+    "&emsp; 通过打印`vocabulary`可以看到词汇表中的单词列表，其中，`padding`和`unknown`不会显示"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3515e096",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary([]...)\n",
+      "{'<pad>': 0, '<unk>': 1}\n",
+      "<pad> 0\n",
+      "<unk> 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP.core.vocabulary import Vocabulary\n",
+    "\n",
+    "vocab = Vocabulary()\n",
+    "print(vocab)\n",
+    "print(vocab.word2idx)\n",
+    "print(vocab.padding, vocab.padding_idx)\n",
+    "print(vocab.unknown, vocab.unknown_idx)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "640be126",
+   "metadata": {},
+   "source": [
+    "在`vocabulary`中，通过`add_word`方法或`add_word_lst`方法，可以单独或批量添加单词\n",
+    "\n",
+    "&emsp; 通过`len`或`word_count`属性，可以显示`vocabulary`的单词量和每个单词添加的次数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "88c7472a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 Counter({'生活': 1, '就像': 1, '海洋': 1})\n",
+      "6 Counter({'生活': 1, '就像': 1, '海洋': 1, '只有': 1})\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab.add_word_lst(['生活', '就像', '海洋'])\n",
+    "print(len(vocab), vocab.word_count)\n",
+    "vocab.add_word('只有')\n",
+    "print(len(vocab), vocab.word_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ec8b28",
+   "metadata": {},
+   "source": [
+    "&emsp; **通过`to_word`方法可以找到单词对应的序号**，**通过`to_index`方法可以找到序号对应的单词**\n",
+    "\n",
+    "&emsp; &emsp; 由于序号0和序号1已经被占用，所以**新加入的词的序号从2开始计数**，如`'生活'`对应2\n",
+    "\n",
+    "&emsp; &emsp; 通过`has_word`方法可以判断单词是否在词汇表中，没有的单词被判做`<unk>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3447acde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<pad> 0\n",
+      "<unk> 1\n",
+      "生活 2\n",
+      "只有 5\n",
+      "彼岸 1 False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vocab.to_word(0), vocab.to_index('<pad>'))\n",
+    "print(vocab.to_word(1), vocab.to_index('<unk>'))\n",
+    "print(vocab.to_word(2), vocab.to_index('生活'))\n",
+    "print(vocab.to_word(5), vocab.to_index('只有'))\n",
+    "print('彼岸', vocab.to_index('彼岸'), vocab.has_word('彼岸'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4e36850",
+   "metadata": {},
+   "source": [
+    "**`vocabulary`允许反复添加相同单词**，**可以通过`word_count`方法看到相应单词被添加的次数**\n",
+    "\n",
+    "&emsp; 但其中没有`<unk>`和`<pad>`，`vocabulary`的全部变量与函数可以通过`dir(vocabulary)`查询"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "490b101c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13 Counter({'生活': 2, '就像': 2, '海洋': 2, '只有': 2, '意志': 1, '坚强的': 1, '人': 1, '才': 1, '能': 1, '到达': 1, '彼岸': 1})\n",
+      "彼岸 12 True\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab.add_word_lst(['生活', '就像', '海洋', '只有', '意志', '坚强的', '人', '才', '能', '到达', '彼岸'])\n",
+    "print(len(vocab), vocab.word_count)\n",
+    "print('彼岸', vocab.to_index('彼岸'), vocab.has_word('彼岸'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e32a63",
+   "metadata": {},
+   "source": [
+    "### 2.2 vocabulary 与 OOV 问题\n",
+    "\n",
+    "在`vocabulary`模块初始化的时候，可以通过指定`unknown`和`padding`为`None`，限制其存在\n",
+    "\n",
+    "&emsp; 此时添加单词直接从0开始标号，如果遇到未知单词会直接报错，即 out of vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a99ff909",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'positive': 0, 'negative': 1}\n",
+      "ValueError: word `neutral` not in vocabulary\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = Vocabulary(unknown=None, padding=None)\n",
+    "\n",
+    "vocab.add_word_lst(['positive', 'negative'])\n",
+    "print(vocab.word2idx)\n",
+    "\n",
+    "try:\n",
+    "    print(vocab.to_index('neutral'))\n",
+    "except ValueError:\n",
+    "    print(\"ValueError: word `neutral` not in vocabulary\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "618da6bd",
+   "metadata": {},
+   "source": [
+    "&emsp; 相应的，如果只指定其中的`unknown`，则编号会后移一个，同时遇到未知单词全部当做`<unk>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "432f74c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'<unk>': 0, 'positive': 1, 'negative': 2}\n",
+      "0 <unk>\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = Vocabulary(unknown='<unk>', padding=None)\n",
+    "\n",
+    "vocab.add_word_lst(['positive', 'negative'])\n",
+    "print(vocab.word2idx)\n",
+    "\n",
+    "print(vocab.to_index('neutral'), vocab.to_word(vocab.to_index('neutral')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6263f73",
+   "metadata": {},
+   "source": [
+    "## 3 dataset 和 vocabulary 的组合使用\n",
+    " \n",
+    "### 3.1 从 dataframe 中加载 dataset\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89059713",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3dbd985d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f634586",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ba13989",
+   "metadata": {},
+   "source": [
+    "### 3.2 从 dataset 中获取 vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2de615b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f5eed18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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