diff --git a/data/attr/raw_data/process.py b/data/attr/raw_data/process.py
new file mode 100644
index 0000000..f853bd5
--- /dev/null
+++ b/data/attr/raw_data/process.py
@@ -0,0 +1,238 @@
+import os
+import warnings
+import json
+import random
+
+
+def get_entities(seq, suffix=False):
+    """Gets entities from sequence.
+
+    Args:
+        seq (list): sequence of labels.
+
+    Returns:
+        list: list of (chunk_type, chunk_start, chunk_end).
+
+    Example:
+        >>> from seqeval.metrics.sequence_labeling import get_entities
+        >>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
+        >>> get_entities(seq)
+        [('PER', 0, 1), ('LOC', 3, 3)]
+    """
+
+    def _validate_chunk(chunk, suffix):
+        if chunk in ['O', 'B', 'I', 'E', 'S']:
+            return
+
+        if suffix:
+            if not chunk.endswith(('-B', '-I', '-E', '-S')):
+                warnings.warn('{} seems not to be NE tag.'.format(chunk))
+
+        else:
+            if not chunk.startswith(('B-', 'I-', 'E-', 'S-')):
+                warnings.warn('{} seems not to be NE tag.'.format(chunk))
+
+    # for nested list
+    if any(isinstance(s, list) for s in seq):
+        seq = [item for sublist in seq for item in sublist + ['O']]
+
+    prev_tag = 'O'
+    prev_type = ''
+    begin_offset = 0
+    chunks = []
+    for i, chunk in enumerate(seq + ['O']):
+        _validate_chunk(chunk, suffix)
+
+        if suffix:
+            tag = chunk[-1]
+            type_ = chunk[:-1].rsplit('-', maxsplit=1)[0] or '_'
+        else:
+            tag = chunk[0]
+            type_ = chunk[1:].split('-', maxsplit=1)[-1] or '_'
+
+        if end_of_chunk(prev_tag, tag, prev_type, type_):
+            chunks.append((prev_type, begin_offset, i - 1))
+        if start_of_chunk(prev_tag, tag, prev_type, type_):
+            begin_offset = i
+        prev_tag = tag
+        prev_type = type_
+
+    return chunks
+
+
+def end_of_chunk(prev_tag, tag, prev_type, type_):
+    """Checks if a chunk ended between the previous and current word.
+
+    Args:
+        prev_tag: previous chunk tag.
+        tag: current chunk tag.
+        prev_type: previous type.
+        type_: current type.
+
+    Returns:
+        chunk_end: boolean.
+    """
+    chunk_end = False
+
+    if prev_tag == 'E':
+        chunk_end = True
+    if prev_tag == 'S':
+        chunk_end = True
+
+    if prev_tag == 'B' and tag == 'B':
+        chunk_end = True
+    if prev_tag == 'B' and tag == 'S':
+        chunk_end = True
+    if prev_tag == 'B' and tag == 'O':
+        chunk_end = True
+    if prev_tag == 'I' and tag == 'B':
+        chunk_end = True
+    if prev_tag == 'I' and tag == 'S':
+        chunk_end = True
+    if prev_tag == 'I' and tag == 'O':
+        chunk_end = True
+
+    if prev_tag != 'O' and prev_tag != '.' and prev_type != type_:
+        chunk_end = True
+
+    return chunk_end
+
+
+def start_of_chunk(prev_tag, tag, prev_type, type_):
+    """Checks if a chunk started between the previous and current word.
+
+    Args:
+        prev_tag: previous chunk tag.
+        tag: current chunk tag.
+        prev_type: previous type.
+        type_: current type.
+
+    Returns:
+        chunk_start: boolean.
+    """
+    chunk_start = False
+
+    if tag == 'B':
+        chunk_start = True
+    if tag == 'S':
+        chunk_start = True
+
+    if prev_tag == 'E' and tag == 'E':
+        chunk_start = True
+    if prev_tag == 'E' and tag == 'I':
+        chunk_start = True
+    if prev_tag == 'S' and tag == 'E':
+        chunk_start = True
+    if prev_tag == 'S' and tag == 'I':
+        chunk_start = True
+    if prev_tag == 'O' and tag == 'E':
+        chunk_start = True
+    if prev_tag == 'O' and tag == 'I':
+        chunk_start = True
+
+    if tag != 'O' and tag != '.' and prev_type != type_:
+        chunk_start = True
+
+    return chunk_start
+
+
+def preprocess(input_path, save_path, mode, split=None, ratio=None):
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    result = []
+    tmp = {}
+    tmp['id'] = 0
+    tmp['text'] = ''
+    tmp['labels'] = []
+    # =======先找出句子和句子中的所有实体和类型=======
+    with open(input_path, 'r', encoding='utf-8') as fp:
+        lines = fp.readlines()
+        texts = []
+        words = []
+        entities = []
+        char_label_tmp = []
+        for line in lines:
+            line = line.strip().split(" ")
+            if len(line) == 2:
+                word = line[0]
+                label = line[1]
+                words.append(word)
+                char_label_tmp.append(label)
+            else:
+                texts.append("".join(words))
+                entities.append(get_entities(char_label_tmp))
+                words = []
+                char_label_tmp = []
+
+    # ==========================================
+    # =======找出句子中实体的位置=======
+    # entities里面每一个元素：[实体类别, 实体起始位置, 实体结束位置]
+    i = 0
+    labels = set()
+    for text, entity in zip(texts, entities):
+        if entity:
+            tmp['id'] = i
+            tmp['text'] = text
+            for j, ent in enumerate(entity):
+                labels.add(ent[0])
+                tmp['labels'].append(["T{}".format(str(j)), ent[0], ent[1], ent[2] + 1,
+                                      text[int(ent[1]):int(ent[2] + 1)]])
+        else:
+            tmp['id'] = i
+            tmp['text'] = text
+            tmp['labels'] = []
+        result.append(tmp)
+        # print(i, text, entity, tmp)
+        tmp = {}
+        tmp['id'] = 0
+        tmp['text'] = ''
+        tmp['labels'] = []
+        i += 1
+
+    if mode == "train":
+        label_path = os.path.join(save_path, "labels.json")
+        with open(label_path, 'w', encoding='utf-8') as fp:
+            fp.write(json.dumps(list(labels), ensure_ascii=False))
+
+
+    if split:
+        train_data_path = os.path.join(save_path, mode + ".json")
+        dev_data_path = os.path.join(save_path, "dev" + ".json")
+        random.shuffle(result)
+        train_result = result[:int(len(result) * (1 - ratio))]
+        dev_result = result[int(len(result) * (1 - ratio)):]
+        with open(train_data_path, 'w', encoding='utf-8') as fp:
+            fp.write(json.dumps(train_result, ensure_ascii=False))
+        with open(dev_data_path, 'w', encoding='utf-8') as fp:
+            fp.write(json.dumps(dev_result, ensure_ascii=False))
+    else:
+        data_path = os.path.join(save_path, mode + ".json")
+        with open(data_path, 'w', encoding='utf-8') as fp:
+            fp.write(json.dumps(result, ensure_ascii=False))
+
+
+path = '../mid_data/'
+preprocess("train.txt", path, "train", split=True, ratio=0.2)
+# preprocess("train.txt", path, "train", split=None, ratio=None)
+# preprocess("dev.txt", path, "dev", split=None, ratio=None)
+
+labels_path = os.path.join(path, 'labels.json')
+with open(labels_path, 'r') as fp:
+    labels = json.load(fp)
+
+tmp_labels = []
+tmp_labels.append('O')
+for label in labels:
+    tmp_labels.append('B-' + label)
+    tmp_labels.append('I-' + label)
+    tmp_labels.append('E-' + label)
+    tmp_labels.append('S-' + label)
+
+label2id = {}
+for k, v in enumerate(tmp_labels):
+    label2id[v] = k
+
+if not os.path.exists(path):
+    os.makedirs(path)
+with open(os.path.join(path, "nor_ent2id.json"), 'w') as fp:
+    fp.write(json.dumps(label2id, ensure_ascii=False))