NLP实践——中文指代消解方案
- 1. 参考项目
- 2. 数据
- 2.1 生成conll格式
- 2.2 生成jsonline格式
- 3. 训练
- 3.1 实例化模型
- 3.2 读取数据
- 3.3 评估方法
- 3.4 训练方法
- 4. 推理
- 5. 总结
1. 参考项目
关于指代消解任务，有很多开源的项目和工具可以借鉴，比如spacy的基础模型，就包含了指代消解的功能，一般来讲，这些模型多是在Ontonotes 5.0的数据集上进行训练的。然而，尽管Ontonotes 5.0数据中也提供了中文数据，但相比英文指代消解模型，中文的可以直接使用的指代消解模型却不那么容易找到。
在这篇参考文档（https://chinesenlp.xyz/#/zh/docs/co-reference_resolution）中，介绍了现有的中文指代消解相关论文，下图是其中一些参考工作的得分指标。
关于指代消解任务的评测指标以及python版本的实现方法，在之前的博客 共指消解评测方法详解与python实现 中也有过介绍，感兴趣的同学可以阅读。
但是在上文提到的参考文献中，也没有找到很方便可以直接下载使用的模型，所以选择了利用之前的一个方案，在中文数据上重新训练一下。选择的方案是2021年的一篇论文《Coreference Resolution without Span Representations》，其项目又名s2e-coref，是指代消歧任务的一个经典工作。
- 论文地址：https://www.semanticscholar.org/reader/3029263ca51e6c2907f9f99277083cf6afb1adb7
- 项目地址：https://github.com/yuvalkirstain/s2e-coref
论文中没有给出模型结构的图，为了方便理解，这里我简单画一下：
此图只作为示意帮助理解，具体结构还要结合代码一起看。总的来说，就是在seq维度上，做了交互特征，然后选取topk，利用topk的索引去gather另一个linear的结果，最后拼接成一个final_logits，在解码阶段使用final_logits中，以此取max，回到topk_start_ids和topk_end_ids进行解码。
项目中给出了训练好的英文模型的直接下载地址，而对于中文模型，则需要自己去训练一下。接下来就详细介绍训练和推理的方法。
2. 数据
2.1 生成conll格式
数据采用的是Ontonotes，此数据虽然不需要付费，但是需要在LDC上申请，操作并不复杂。
参考知乎文章：https://zhuanlan.zhihu.com/p/121786025
按照文中的步骤操作即可。分别下载conll数据和ontonotes数据，然后进入conll-2012/v3/script，执行skeleton2conll.sh -D [path/to/conll-2012-train-v0/data/files/data] [path/to/conll-2012]，注意这里的两个路径，分别是下载的ontonotes 5.0，解压之后的data目录，以及conll数据解压之后的v4/data目录。
执行完之后，会在各个小文件夹之下，如v4/data/train/data/chinese/annotations/bc/cctv/00，生成*_conll文件。需要注意的是脚本是python2写的，所以要把所有的print都注释掉，否则语法报错。
然后再在/conll-2012/v4/data/train [development| test]中分别创建merge.py，放入参考的知乎链接中的代码，将所有conll文件合并，最终生成了train [development| test].chinese.v4_gold_conll这3个文件，也就是用于s2e_coref项目的输入数据。
2.2 生成jsonline格式
在生成conll格式的数据之后，还需要根据s2e_coref项目的要求，进行预处理格式转换：
python minimize.py $DATA_DIR
其中minimize.py是s2e_coref项目中提供的脚本，DATA_DIR是转换好的conll格式的数据，经过这个转换之后，就把数据转换成了jsonline格式。（在这一步转换时我遇到了test数据的转换错误，由于不影响训练评估，所以就没有再花时间去解决报错的问题）
在minimize.py中，需要把english修改为chinese：
if __name__ == "__main__":
data_dir = sys.argv[1]
labels = collections.defaultdict(set)
stats = collections.defaultdict(int)
# minimize_language(data_dir, "english", labels, stats)
minimize_language(data_dir, "chinese", labels, stats)
# minimize_language("arabic", labels, stats)
for k, v in labels.items():
print("{} = [{}]".format(k, ", ".join("\"{}\"".format(label) for label in v)))
for k, v in stats.items():
print("{} = {}".format(k, v))
然后会在DATA_DIR中生成train.chinese.jsonlines和dev.chinese.jsonlines。
3. 训练
3.1 实例化模型
训练部分没有直接使用项目中给出的python run_coref.py，而是放在jupyter中方便调试。
首先，实例化模型。原项目中采用的longformer，所以这里就直接去HF上找一个中文版本的longformer：
https://huggingface.co/ValkyriaLenneth/longformer_zh
但是其中的Tokenizer没有做很详细的说明，经过了一番尝试之后，发现直接使用BertTokenizer实例化即可（后续会遇到一点问题但是可以解决）：
from transformers import BertTokenizer, AutoConfig, LongformerConfig
from modeling import S2E # 从项目中的py引用
# 先实例化一个config
config = AutoConfig.from_pretrained('YOUR_PATH_TO/longformer_zh') # 下载的longformer模型的地址
S2E.config_class = LongformerConfig
S2E.base_model_prefix = 'longformer'
# 然后由于是jupyter执行，写一个辅助的参数类：
class Args:
def __init__(self,
model_name_or_path: str,
model_type: str = 'longformer',
tokenizer_name: str = 'allenai/longformer-large-4096',
max_seq_length: int = 4096,
dropout_prob: float = 0.3,
top_lambda: float = 0.4,
max_span_length: int = 30,
max_total_seq_len: int = 5000,
ffnn_size: int = 3072,
normalise_loss: bool = True
):
self.model_type = model_type
self.model_name_or_path = model_name_or_path
self.tokenizer_name = tokenizer_name
self.max_seq_length = max_seq_length
self.dropout_prob = dropout_prob
self.top_lambda = top_lambda
self.max_span_length = max_span_length
self.ffnn_size = ffnn_size
self.normalise_loss = normalise_loss
self.max_total_seq_len = max_total_seq_len
# 然后实例化参数类
args = Args('YOUR_PATH_TO/longformer_zh')
# 实例化模型和tokenizer，会报一些warning，不用管它
tokenizer = BertTokenizer.from_pretrained('YOUR_PATH_TO/longformer_zh')
model = S2E.from_pretrained('YOUR_PATH_TO/longformer_zh',
config=config,
args=args)
model.to('cuda:0')
3.2 读取数据
实例化模型之后，读取数据集：
from data import CorefDataset # 从项目的data.py引用数据类
train_file = 'train.chinese.jsonlines' # 之前转换的jsonline数据
dev_file = 'dev.chinese.jsonlines'
train_dataset = CorefDataset(train_file, tokenizer, 4096)
dev_dataset = CorefDataset(dev_file, tokenizer, 4096)
3.3 评估方法
模型的评估方法直接从项目源码部分截取，然后删除不必要的部分：
import json
import os
import logging
import random
from collections import OrderedDict, defaultdict
import numpy as np
import torch
from coref_bucket_batch_sampler import BucketBatchSampler
from data import get_dataset
from metrics import CorefEvaluator, MentionEvaluator
from utils import extract_clusters, extract_mentions_to_predicted_clusters_from_clusters, extract_clusters_for_decode
from conll import evaluate_conll
# logger = logging.getLogger(__name__)
class Evaluator:
def __init__(self, args, tokenizer):
self.args = args
# self.eval_output_dir = args.output_dir
self.tokenizer = tokenizer
def evaluate(self, model, eval_dataset, prefix="", tb_writer=None, global_step=None, official=False):
# eval_dataset = get_dataset(self.args, tokenizer=self.tokenizer, evaluate=True)
# if self.eval_output_dir and not os.path.exists(self.eval_output_dir) and self.args.local_rank in [-1, 0]:
# os.makedirs(self.eval_output_dir)
# Note that DistributedSampler samples randomly
# eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = BucketBatchSampler(eval_dataset, max_total_seq_len=self.args.max_total_seq_len, batch_size_1=True)
# Eval!
print("***** Running evaluation {} *****".format(prefix))
print(" Examples number: %d", len(eval_dataset))
model.eval()
post_pruning_mention_evaluator = MentionEvaluator()
mention_evaluator = MentionEvaluator()
coref_evaluator = CorefEvaluator()
losses = defaultdict(list)
doc_to_prediction = {}
doc_to_subtoken_map = {}
for (doc_key, subtoken_maps), batch in eval_dataloader:
batch = tuple(tensor.to(self.args.device) for tensor in batch)
input_ids, attention_mask, gold_clusters = batch
input_ids = torch.where(input_ids == 22560, 100, input_ids)
input_ids = torch.where(input_ids == 49518, 100, input_ids)
with torch.no_grad():
outputs = model(input_ids=input_ids,
attention_mask=attention_mask,
gold_clusters=gold_clusters,
return_all_outputs=True)
loss_dict = outputs[-1]
if self.args.n_gpu > 1:
loss_dict = {key: val.mean() for key, val in loss_dict.items()}
for key, val in loss_dict.items():
losses[key].append(val.item())
outputs = outputs[1:-1]
batch_np = tuple(tensor.cpu().numpy() for tensor in batch)
outputs_np = tuple(tensor.cpu().numpy() for tensor in outputs)
for output in zip(*(batch_np + outputs_np)):
gold_clusters = output[2]
gold_clusters = extract_clusters(gold_clusters)
mention_to_gold_clusters = extract_mentions_to_predicted_clusters_from_clusters(gold_clusters)
gold_mentions = list(mention_to_gold_clusters.keys())
starts, end_offsets, coref_logits, mention_logits = output[-4:]
max_antecedents = np.argmax(coref_logits, axis=1).tolist()
mention_to_antecedent = {((int(start), int(end)),
(int(starts[max_antecedent]), int(end_offsets[max_antecedent])))
for start, end, max_antecedent in
zip(starts, end_offsets, max_antecedents) if max_antecedent < len(starts)}
predicted_clusters, _ = extract_clusters_for_decode(mention_to_antecedent)
candidate_mentions = list(zip(starts, end_offsets))
mention_to_predicted_clusters = extract_mentions_to_predicted_clusters_from_clusters(predicted_clusters)
predicted_mentions = list(mention_to_predicted_clusters.keys())
post_pruning_mention_evaluator.update(candidate_mentions, gold_mentions)
mention_evaluator.update(predicted_mentions, gold_mentions)
coref_evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted_clusters,
mention_to_gold_clusters)
doc_to_prediction[doc_key] = predicted_clusters
doc_to_subtoken_map[doc_key] = subtoken_maps
post_pruning_mention_precision, post_pruning_mentions_recall, post_pruning_mention_f1 = post_pruning_mention_evaluator.get_prf()
mention_precision, mentions_recall, mention_f1 = mention_evaluator.get_prf()
prec, rec, f1 = coref_evaluator.get_prf()
results = [(key, sum(val) / len(val)) for key, val in losses.items()]
results += [
("post pruning mention precision", post_pruning_mention_precision),
("post pruning mention recall", post_pruning_mentions_recall),
("post pruning mention f1", post_pruning_mention_f1),
("mention precision", mention_precision),
("mention recall", mentions_recall),
("mention f1", mention_f1),
("precision", prec),
("recall", rec),
("f1", f1)
]
print("***** Eval results {} *****".format(prefix))
for key, values in results:
if isinstance(values, float):
print(f" {key} = {values:.3f}")
else:
print(f" {key} = {values}")
if tb_writer is not None and global_step is not None:
tb_writer.add_scalar(key, values, global_step)
# if self.eval_output_dir:
# output_eval_file = os.path.join(self.eval_output_dir, "eval_results.txt")
# with open(output_eval_file, "a") as writer:
# if prefix:
# writer.write(f'\n{prefix}:\n')
# for key, values in results:
# if isinstance(values, float):
# writer.write(f"{key} = {values:.3f}\n")
# else:
# writer.write(f"{key} = {values}\n")
results = OrderedDict(results)
# results["experiment_name"] = self.args.experiment_name
results["data"] = prefix
print(results)
# with open(os.path.join(self.args.output_dir, "results.jsonl"), "a+") as f:
# f.write(json.dumps(results) + '\n')
# if official:
# with open(os.path.join(self.args.output_dir, "preds.jsonl"), "w") as f:
# f.write(json.dumps(doc_to_prediction) + '\n')
# f.write(json.dumps(doc_to_subtoken_map) + '\n')
# if self.args.conll_path_for_eval is not None:
# conll_results = evaluate_conll(self.args.conll_path_for_eval, doc_to_prediction, doc_to_subtoken_map)
# official_f1 = sum(results["f"] for results in conll_results.values()) / len(conll_results)
# logger.info('Official avg F1: %.4f' % official_f1)
return results
需要注意的是，由于tokenizer创建的有问题，会造成embedding的时候OOV，具体表现为，报”list out of range“的错误（如果是在cuda上，则报cuda的错误），所以需要做一个简单的替换，也就是以下的两行：
input_ids = torch.where(input_ids == 22560, 100, input_ids)
input_ids = torch.where(input_ids == 49518, 100, input_ids)
3.4 训练方法
训练也是直接从项目源码中截取。
首先需要配置一下训练参数：
args.batch_size_1 = False
args.gradient_accumulation_steps = 1
args.num_train_epochs = 100
args.head_learning_rate = 3e-4
args.learning_rate = 1e-5
args.weight_decay = 0.01
args.adam_beta1 = 0.9
args.adam_beta2 = 0.98
args.adam_epsilon = 1e-6
args.warmup_steps = 5600
args.dropout_prob = 0.3
args.top_lambda = 0.4
args.amp = False
args.n_gpu = 1
args.local_rank = -1
args.seed = 42
args.device = 'cuda:0'
args.do_eval = True
args.eval_steps = 1000
args.save_steps = 3000
args.save_if_best = True
训练过程中需要用到evaluator，实例化一个：
evaluator = Evaluator(args, tokenizer)
然后是训练方法：
def train(args, train_dataset, model, tokenizer, evaluator, dev_dataset):
""" Train the model """
# tb_path = os.path.join(args.tensorboard_dir, os.path.basename(args.output_dir))
# tb_writer = SummaryWriter(tb_path, flush_secs=30)
# logger.info('Tensorboard summary path: %s' % tb_path)
train_dataloader = BucketBatchSampler(train_dataset, max_total_seq_len=args.max_total_seq_len, batch_size_1=args.batch_size_1)
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
head_params = ['coref', 'mention', 'antecedent']
model_decay = [p for n, p in model.named_parameters() if
not any(hp in n for hp in head_params) and not any(nd in n for nd in no_decay)]
model_no_decay = [p for n, p in model.named_parameters() if
not any(hp in n for hp in head_params) and any(nd in n for nd in no_decay)]
head_decay = [p for n, p in model.named_parameters() if
any(hp in n for hp in head_params) and not any(nd in n for nd in no_decay)]
head_no_decay = [p for n, p in model.named_parameters() if
any(hp in n for hp in head_params) and any(nd in n for nd in no_decay)]
head_learning_rate = args.head_learning_rate if args.head_learning_rate else args.learning_rate
optimizer_grouped_parameters = [
{'params': model_decay, 'lr': args.learning_rate, 'weight_decay': args.weight_decay},
{'params': model_no_decay, 'lr': args.learning_rate, 'weight_decay': 0.0},
{'params': head_decay, 'lr': head_learning_rate, 'weight_decay': args.weight_decay},
{'params': head_no_decay, 'lr': head_learning_rate, 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
loaded_saved_optimizer = False
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")
):
# Load in optimizer and scheduler states
optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
loaded_saved_optimizer = True
if args.amp:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
# logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_dataset))
# logger.info(" Num Epochs = %d", args.num_train_epochs)
# logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
# logger.info(" Total optimization steps = %d", t_total)
global_step = 0
if os.path.exists(args.model_name_or_path) and 'checkpoint' in args.model_name_or_path:
try:
# set global_step to gobal_step of last saved checkpoint from model path
checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
global_step = int(checkpoint_suffix)
# logger.info(" Continuing training from checkpoint, will skip to saved global_step")
# logger.info(" Continuing training from global step %d", global_step)
# if not loaded_saved_optimizer:
# logger.warning("Training is continued from checkpoint, but didn't load optimizer and scheduler")
except ValueError:
print('Starting fine-tuning.')
# logger.info(" Starting fine-tuning.")
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
set_seed(args) # Added here for reproducibility (even between python 2 and 3)
# If nonfreeze_params is not empty, keep all params that are
# not in nonfreeze_params fixed.
# if args.nonfreeze_params:
# names = []
# for name, param in model.named_parameters():
# freeze = True
# for nonfreeze_p in args.nonfreeze_params.split(','):
# if nonfreeze_p in name:
# freeze = False
# if freeze:
# param.requires_grad = False
# else:
# names.append(name)
# print('nonfreezing layers: {}'.format(names))
train_iterator = trange(
0, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
)
# Added here for reproducibility
set_seed(args)
best_f1 = -1
best_global_step = -1
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
batch = tuple(tensor.to(args.device) for tensor in batch)
input_ids, attention_mask, gold_clusters = batch
input_ids = torch.where(input_ids == 22560, 100, input_ids)
input_ids = torch.where(input_ids == 49518, 100, input_ids)
model.train()
outputs = model(input_ids=input_ids,
attention_mask=attention_mask,
gold_clusters=gold_clusters,
return_all_outputs=False)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
losses = outputs[-1]
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
losses = {key: val.mean() for key, val in losses.items()}
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# Log metrics
# if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# logger.info(f"\nloss step {global_step}: {(tr_loss - logging_loss) / args.logging_steps}")
# tb_writer.add_scalar('Training_Loss', (tr_loss - logging_loss) / args.logging_steps, global_step)
# for key, value in losses.items():
# logger.info(f"\n{key}: {value}")
# logging_loss = tr_loss
if args.local_rank in [-1, 0] and args.do_eval and args.eval_steps > 0 and global_step % args.eval_steps == 0:
results = evaluator.evaluate(model, dev_dataset, prefix=f'step_{global_step}', tb_writer=None, global_step=global_step)
f1 = results["f1"]
if f1 > best_f1:
best_f1 = f1
best_global_step = global_step
torch.save(model.state_dict(), 'best_model_zh.pt')
# Save model checkpoint
# output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
# model_to_save.save_pretrained(output_dir)
# tokenizer.save_pretrained(output_dir)
# torch.save(args, os.path.join(output_dir, 'training_args.bin'))
# print("Saving model checkpoint to %s", output_dir)
# torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
# torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
# print("Saving optimizer and scheduler states to %s", output_dir)
print(f"best f1 is {best_f1} on global step {best_global_step}")
# if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0 and \
# (not args.save_if_best or (best_global_step == global_step)):
# # Save model checkpoint
# output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# model_to_save = model.module if hasattr(model,
# 'module') else model # Take care of distributed/parallel training
# model_to_save.save_pretrained(output_dir)
# tokenizer.save_pretrained(output_dir)
# torch.save(args, os.path.join(output_dir, 'training_args.bin'))
# print("Saving model checkpoint to %s", output_dir)
# torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
# torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
# print("Saving optimizer and scheduler states to %s", output_dir)
if 0 < t_total < global_step:
train_iterator.close()
break
# with open(os.path.join(args.output_dir, f"best_f1.json"), "w") as f:
# json.dump({"best_f1": best_f1, "best_global_step": best_global_step}, f)
# tb_writer.close()
return global_step, tr_loss / global_step
与评估类似地，训练方法中，也需要对OOV的情况进行相应的替换，替换内容已经体现在上述代码中。
最后训练即可：
global_step, tr_loss = train(args, train_dataset, model, tokenizer, evaluator, dev_dataset)
我没有进行特别仔细地调整超参数，直接跑了200轮，最终f1的最好结果是67.6。
4. 推理
最后写一下推理部分，一般来说，开源项目都不提供推理的代码，这部分需要自己实现。
def predict(model, tokenizer, text: str, device: str = 'cpu'):
"""
预测
:param model: s2e模型
:param tokenizer: 分词器
:param text: 原文
:param device: 运行的设备
:return:
---------------
ver: 2022-09-05
by: changhongyu
---------------
修改为适用于中文
ver: 2023-09-19
"""
model.eval()
example = process_input(text, tokenizer)
example = tuple(tensor.to(device) for tensor in example if tensor is not None)
input_ids, attention_mask = example
input_ids = torch.where(input_ids == 22560, 100, input_ids)
input_ids = torch.where(input_ids == 49518, 100, input_ids)
with torch.no_grad():
outputs = model(input_ids=input_ids,
attention_mask=attention_mask,
gold_clusters=None,
return_all_outputs=True)
# outputs: (mention_start_ids, mention_end_ids, final_logits, mention_logits)
batch_np = tuple(tensor.cpu().numpy() for tensor in example if tensor is not None)
outputs_np = tuple(tensor.cpu().numpy() for tensor in outputs)
predicted_clusters = None
for output in zip(*(batch_np + outputs_np)):
# gold_clusters = output[2]
# gold_clusters = extract_clusters(gold_clusters)
# mention_to_gold_clusters = extract_mentions_to_predicted_clusters_from_clusters(gold_clusters)
# gold_mentions = list(mention_to_gold_clusters.keys())
starts, end_offsets, coref_logits, mention_logits = output[-4:]
max_antecedents = np.argmax(coref_logits, axis=1).tolist()
mention_to_antecedent = {
((int(start), int(end)), (int(starts[max_antecedent]), int(end_offsets[max_antecedent]))) for
start, end, max_antecedent in zip(starts, end_offsets, max_antecedents)
if max_antecedent < len(starts)}
predicted_clusters, _ = extract_clusters_for_decode(mention_to_antecedent)
if not predicted_clusters:
return
# 格式转换
formatted_clusters = []
# token idx转token list
tokens = convert_token_idx_to_tokens(input_ids[0].cpu().numpy().tolist(), tokenizer)
tokens = [tok if tok not in ["''", "``"] else '"' for tok in tokens]
for cluster_idx, cluster in enumerate(predicted_clusters):
formatted_cluster = []
for ent_idx, (token_start, token_end) in enumerate(cluster):
# 对簇里的每一个实体
# 转为char_span
char_span = convert_token_span_to_char_span(text=text,
token_span=[token_start - 4, token_end - 4],
tokens=tokens[4: -1])
formatted_info = {
"cluster_id": str(cluster_idx),
"id": f"{cluster_idx}-{ent_idx}",
"text": text[char_span[0]: char_span[1]+1],
"start_pos": char_span[0],
"end_pos": char_span[1],
}
formatted_cluster.append(formatted_info)
formatted_clusters.append(formatted_cluster)
return formatted_clusters
其中convert_token_span_to_char_span和convert_token_idx_to_tokens的作用，分别是将实体表述的token span转换为字符span，以及将token_id的列表转换为token的列表，这里不展示这两个方法。如果读者写不出来的话，可以找chatGPT帮忙，或者私信我。
最后，测试一下效果：
# test case 1
text = '据美联社报道，上周利比亚的洪灾已经造成当地超过4000人死亡，10000人失踪'
predict(model, tokenizer, text, 'cpu')
'''
[[{'cluster_id': '0',
'id': '0-0',
'text': '利比亚',
'start_pos': 9,
'end_pos': 11},
{'cluster_id': '0',
'id': '0-1',
'text': '当地',
'start_pos': 19,
'end_pos': 20}]]
'''
# test case 2
text = '拜登在2020年的大选中击败了特朗普，成功当选美国总统。他说，特朗普不会让美国再次伟大。'
predict(model, tokenizer, text, 'cpu')
'''
[[{'cluster_id': '0', 'id': '0-0', 'text': '拜登', 'start_pos': 0, 'end_pos': 1},
{'cluster_id': '0',
'id': '0-1',
'text': '他',
'start_pos': 28,
'end_pos': 28}],
[{'cluster_id': '1',
'id': '1-0',
'text': '特朗普',
'start_pos': 15,
'end_pos': 17},
{'cluster_id': '1',
'id': '1-1',
'text': '特朗普',
'start_pos': 31,
'end_pos': 33}],
[{'cluster_id': '2',
'id': '2-0',
'text': '美国',
'start_pos': 23,
'end_pos': 24},
{'cluster_id': '2',
'id': '2-1',
'text': '美国',
'start_pos': 37,
'end_pos': 38}]]
'''
5. 总结
本文介绍如何使用Ontonotes 5.0数据集训练一个中文实体共指模型，主要介绍训练和推理方法，模型原理和细节没有进行详细的说明，如果感兴趣的话，建议看一下原项目的源码，不要直接读论文，代码写的比较清晰，论文反而没有那么好理解。
如果本文对你有所帮助，记得点一个免费的赞，我们下期再见。
