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utils.py
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utils.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import json
import random
from tqdm import tqdm
import numpy as np
import paddle
MODEL_MAP = {
"uie-base": {
"encoding_model": "ernie-3.0-base-zh",
"resource_file_urls": {
"model_state.pdparams":
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/model_state.pdparams",
"model_config.json":
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/model_config.json"
}
},
"uie-tiny": {
"encoding_model": "ernie-3.0-medium-zh",
"resource_file_urls": {
"model_state.pdparams":
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_tiny/model_state.pdparams",
"model_config.json":
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_tiny/model_config.json"
}
},
}
def set_seed(seed):
paddle.seed(seed)
random.seed(seed)
np.random.seed(seed)
def convert_example(example, tokenizer, max_seq_len):
"""
example: {
title
prompt
content
result_list
}
"""
encoded_inputs = tokenizer(
text=[example["prompt"]],
text_pair=[example["content"]],
stride=len(example["prompt"]),
truncation=True,
max_seq_len=max_seq_len,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_position_ids=True,
return_dict=False)
encoded_inputs = encoded_inputs[0]
offset_mapping = [list(x) for x in encoded_inputs["offset_mapping"]]
bias = 0
for index in range(len(offset_mapping)):
if index == 0:
continue
mapping = offset_mapping[index]
if mapping[0] == 0 and mapping[1] == 0 and bias == 0:
bias = index
if mapping[0] == 0 and mapping[1] == 0:
continue
offset_mapping[index][0] += bias
offset_mapping[index][1] += bias
start_ids = [0 for x in range(max_seq_len)]
end_ids = [0 for x in range(max_seq_len)]
for item in example["result_list"]:
start = map_offset(item["start"] + bias, offset_mapping)
end = map_offset(item["end"] - 1 + bias, offset_mapping)
start_ids[start] = 1.0
end_ids[end] = 1.0
tokenized_output = [
encoded_inputs["input_ids"], encoded_inputs["token_type_ids"],
encoded_inputs["position_ids"], encoded_inputs["attention_mask"],
start_ids, end_ids
]
tokenized_output = [np.array(x, dtype="int64") for x in tokenized_output]
return tuple(tokenized_output)
def map_offset(ori_offset, offset_mapping):
"""
map ori offset to token offset
"""
for index, span in enumerate(offset_mapping):
if span[0] <= ori_offset < span[1]:
return index
return -1
def reader(data_path, max_seq_len=512):
"""
read json
"""
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
json_line = json.loads(line)
content = json_line['content']
prompt = json_line['prompt']
# Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
# It include three summary tokens.
if max_seq_len <= len(prompt) + 3:
raise ValueError(
"The value of max_seq_len is too small, please set a larger value"
)
max_content_len = max_seq_len - len(prompt) - 3
if len(content) <= max_content_len:
yield json_line
else:
result_list = json_line['result_list']
json_lines = []
accumulate = 0
while True:
cur_result_list = []
for result in result_list:
if result['start'] + 1 <= max_content_len < result[
'end']:
max_content_len = result['start']
break
cur_content = content[:max_content_len]
res_content = content[max_content_len:]
while True:
if len(result_list) == 0:
break
elif result_list[0]['end'] <= max_content_len:
if result_list[0]['end'] > 0:
cur_result = result_list.pop(0)
cur_result_list.append(cur_result)
else:
cur_result_list = [
result for result in result_list
]
break
else:
break
json_line = {
'content': cur_content,
'result_list': cur_result_list,
'prompt': prompt
}
json_lines.append(json_line)
for result in result_list:
if result['end'] <= 0:
break
result['start'] -= max_content_len
result['end'] -= max_content_len
accumulate += max_content_len
max_content_len = max_seq_len - len(prompt) - 3
if len(res_content) == 0:
break
elif len(res_content) < max_content_len:
json_line = {
'content': res_content,
'result_list': result_list,
'prompt': prompt
}
json_lines.append(json_line)
break
else:
content = res_content
for json_line in json_lines:
yield json_line
def add_negative_example(examples, texts, prompts, label_set, negative_ratio):
with tqdm(total=len(prompts)) as pbar:
for i, prompt in enumerate(prompts):
negtive_sample = []
redundants_list = list(set(label_set) ^ set(prompt))
redundants_list.sort()
if len(examples[i]) == 0:
continue
else:
actual_ratio = math.ceil(
len(redundants_list) / len(examples[i]))
if actual_ratio <= negative_ratio:
idxs = [k for k in range(len(redundants_list))]
else:
idxs = random.sample(
range(0, len(redundants_list)),
negative_ratio * len(examples[i]))
for idx in idxs:
negtive_result = {
"content": texts[i],
"result_list": [],
"prompt": redundants_list[idx]
}
negtive_sample.append(negtive_result)
examples[i].extend(negtive_sample)
pbar.update(1)
return examples
def construct_relation_prompt_set(entity_name_set, predicate_set):
relation_prompt_set = set()
for entity_name in entity_name_set:
for predicate in predicate_set:
# The relation prompt is constructed as follows:
# subject + "η" + predicate
relation_prompt = entity_name + "η" + predicate
relation_prompt_set.add(relation_prompt)
return sorted(list(relation_prompt_set))
def convert_cls_examples(raw_examples, prompt_prefix, options):
examples = []
print(f"Converting doccano data...")
with tqdm(total=len(raw_examples)) as pbar:
for line in raw_examples:
items = json.loads(line)
text, labels = items["data"], items["label"]
random.shuffle(options)
prompt = ""
sep = ","
for option in options:
prompt += option
prompt += sep
prompt = prompt_prefix + "[" + prompt.rstrip(sep) + "]"
result_list = []
example = {
"content": text,
"result_list": result_list,
"prompt": prompt
}
for label in labels:
start = prompt.rfind(label[0]) - len(prompt) - 1
end = start + len(label)
result = {"text": label, "start": start, "end": end}
example["result_list"].append(result)
examples.append(example)
return examples
def convert_ext_examples(raw_examples, negative_ratio):
texts = []
entity_examples = []
relation_examples = []
entity_prompts = []
relation_prompts = []
entity_label_set = []
entity_name_set = []
predicate_set = []
print(f"Converting doccano data...")
with tqdm(total=len(raw_examples)) as pbar:
for line in raw_examples:
items = json.loads(line)
entity_id = 0
if "data" in items.keys():
text = items["data"]
entities = []
for item in items["label"]:
entity = {
"id": entity_id,
"start_offset": item[0],
"end_offset": item[1],
"label": item[2]
}
entities.append(entity)
entity_id += 1
relations = []
else:
text, relations, entities = items["text"], items[
"relations"], items["entities"]
texts.append(text)
entity_example = []
entity_prompt = []
entity_example_map = {}
entity_map = {} # id to entity name
for entity in entities:
entity_name = text[entity["start_offset"]:entity["end_offset"]]
entity_map[entity["id"]] = {
"name": entity_name,
"start": entity["start_offset"],
"end": entity["end_offset"]
}
entity_label = entity["label"]
result = {
"text": entity_name,
"start": entity["start_offset"],
"end": entity["end_offset"]
}
if entity_label not in entity_example_map.keys():
entity_example_map[entity_label] = {
"content": text,
"result_list": [result],
"prompt": entity_label
}
else:
entity_example_map[entity_label]["result_list"].append(
result)
if entity_label not in entity_label_set:
entity_label_set.append(entity_label)
if entity_name not in entity_name_set:
entity_name_set.append(entity_name)
entity_prompt.append(entity_label)
for v in entity_example_map.values():
entity_example.append(v)
entity_examples.append(entity_example)
entity_prompts.append(entity_prompt)
relation_example = []
relation_prompt = []
relation_example_map = {}
for relation in relations:
predicate = relation["type"]
subject_id = relation["from_id"]
object_id = relation["to_id"]
# The relation prompt is constructed as follows:
# subject + "η" + predicate
prompt = entity_map[subject_id]["name"] + "η" + predicate
result = {
"text": entity_map[object_id]["name"],
"start": entity_map[object_id]["start"],
"end": entity_map[object_id]["end"]
}
if prompt not in relation_example_map.keys():
relation_example_map[prompt] = {
"content": text,
"result_list": [result],
"prompt": prompt
}
else:
relation_example_map[prompt]["result_list"].append(result)
if predicate not in predicate_set:
predicate_set.append(predicate)
relation_prompt.append(prompt)
for v in relation_example_map.values():
relation_example.append(v)
relation_examples.append(relation_example)
relation_prompts.append(relation_prompt)
pbar.update(1)
print(f"Adding negative samples for first stage prompt...")
entity_examples = add_negative_example(entity_examples, texts,
entity_prompts, entity_label_set,
negative_ratio)
if len(predicate_set) != 0:
print(f"Constructing relation prompts...")
relation_prompt_set = construct_relation_prompt_set(entity_name_set,
predicate_set)
print(f"Adding negative samples for second stage prompt...")
relation_examples = add_negative_example(
relation_examples, texts, relation_prompts, relation_prompt_set,
negative_ratio)
return entity_examples, relation_examples