DeepKE-cnSchema使用遇到问题

[512fengbujue]

Describe the question

A clear and concise description of what the question is.
RuntimeError: Error(s) in loading state_dict for BertNer:
size mismatch for classifier.weight: copying a param with shape torch.Size([60, 768]) from checkpoint, the shape in current model is torch.Size([2, 768]).
size mismatch for classifier.bias: copying a param with shape torch.Size([60]) from checkpoint, the shape in current model is torch.Size([2]).

Environment (please complete the following information):

• OS: [e.g. mac / window]
• Python Version [e.g. 3.6]
  window 3.8.19

Screenshots

If applicable, add screenshots to help explain your problem.

Additional context

Add any other context about the problem here.
text: '此外网易云平台还上架了一系列歌曲，其中包括田馥甄的《小幸运》等'
nerfp: 'D:\项目\地面推理机\DeepKE\DeepKE-main\example\triple\cnschema\ner_pretrain_model\checkpoints_pretrain_bert'
refp: 'D:\项目\地面推理机\DeepKE\DeepKE-main\example\triple\cnschema\re_pretrain_model\re_bert.pth'

cwd: ???

defaults:

• hydra/output: custom
• preprocess
• train
• embedding
• predict
• model: lm #

vocab_size: ???
word_dim: 60
pos_size: ??? # 2 * pos_limit + 2
pos_dim: 10 # 当为 sum 时，此值无效，和 word_dim 强行相同

dim_strategy: sum # [cat, sum]

关系种类

num_relations: 51

[BeasterYong]

您好，请检查一下ner模型版本是否正确，可以在链接中重新下载后重试

[F2023888]

您好，我遇到了另外一个问题。

Logits Label: [58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
Logits Confidence: [0.507036566734314, 0.5059388875961304, 0.5061192512512207, 0.5054084062576294, 0.5053091049194336, 0.5055623650550842, 0.5059460997581482, 0.5060293674468994, 0.5062680840492249, 0.5059909224510193, 0.5056659579277039, 0.5060045719146729, 0.5064235329627991, 0.505791962146759, 0.5063173770904541, 0.5058268904685974, 0.5060592293739319, 0.5063878893852234, 0.5065939426422119, 0.5059806108474731, 0.505949079990387, 0.5061217546463013, 0.5054893493652344, 0.5059810280799866, 0.5060664415359497, 0.506255030632019, 0.5060448050498962, 0.5057430863380432, 0.5060313940048218, 0.5054957866668701, 0.5062739253044128, 0.5064918398857117, 0.5069468021392822, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447]
Labels: [('[CLS]', 0.5059388875961304), ('[CLS]', 0.5061192512512207), ('[CLS]', 0.5054084062576294), ('[CLS]', 0.5053091049194336), ('[CLS]', 0.5055623650550842), ('[CLS]', 0.5059460997581482), ('[CLS]', 0.5060293674468994), ('[CLS]', 0.5062680840492249), ('[CLS]', 0.5059909224510193), ('[CLS]', 0.5056659579277039), ('[CLS]', 0.5060045719146729), ('[CLS]', 0.5064235329627991), ('[CLS]', 0.505791962146759), ('[CLS]', 0.5063173770904541), ('[CLS]', 0.5058268904685974), ('[CLS]', 0.5060592293739319), ('[CLS]', 0.5063878893852234), ('[CLS]', 0.5065939426422119), ('[CLS]', 0.5059806108474731), ('[CLS]', 0.505949079990387), ('[CLS]', 0.5061217546463013), ('[CLS]', 0.5054893493652344), ('[CLS]', 0.5059810280799866), ('[CLS]', 0.5060664415359497), ('[CLS]', 0.506255030632019), ('[CLS]', 0.5060448050498962), ('[CLS]', 0.5057430863380432), ('[CLS]', 0.5060313940048218), ('[CLS]', 0.5054957866668701), ('[CLS]', 0.5062739253044128), ('[CLS]', 0.5064918398857117)]
Result: [('此', '[CLS]'), ('外', '[CLS]'), ('网', '[CLS]'), ('易', '[CLS]'), ('云', '[CLS]'), ('平', '[CLS]'), ('台', '[CLS]'), ('还', '[CLS]'), ('上', '[CLS]'), ('架', '[CLS]'), ('了', '[CLS]'), ('一', '[CLS]'), ('系', '[CLS]'), ('列', '[CLS]'), ('歌', '[CLS]'), ('曲', '[CLS]'), ('，', '[CLS]'), ('其', '[CLS]'), ('中', '[CLS]'), ('包', '[CLS]'), ('括', '[CLS]'), ('田', '[CLS]'), ('馥', '[CLS]'), ('甄', '[CLS]'), ('的', '[CLS]'), ('《', '[CLS]'), ('小', '[CLS]'), ('幸', '[CLS]'), ('运', '[CLS]'), ('》', '[CLS]'), ('等', '[CLS]')]
它好像没有成功运行NER任务，我的NER和RE都可以单独正确执行，这是因为什么呢

[BeasterYong]

您好，我遇到了另外一个问题。 Logits Label: [58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Logits Confidence: [0.507036566734314, 0.5059388875961304, 0.5061192512512207, 0.5054084062576294, 0.5053091049194336, 0.5055623650550842, 0.5059460997581482, 0.5060293674468994, 0.5062680840492249, 0.5059909224510193, 0.5056659579277039, 0.5060045719146729, 0.5064235329627991, 0.505791962146759, 0.5063173770904541, 0.5058268904685974, 0.5060592293739319, 0.5063878893852234, 0.5065939426422119, 0.5059806108474731, 0.505949079990387, 0.5061217546463013, 0.5054893493652344, 0.5059810280799866, 0.5060664415359497, 0.506255030632019, 0.5060448050498962, 0.5057430863380432, 0.5060313940048218, 0.5054957866668701, 0.5062739253044128, 0.5064918398857117, 0.5069468021392822, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447, 0.017459817230701447] Labels: [('[CLS]', 0.5059388875961304), ('[CLS]', 0.5061192512512207), ('[CLS]', 0.5054084062576294), ('[CLS]', 0.5053091049194336), ('[CLS]', 0.5055623650550842), ('[CLS]', 0.5059460997581482), ('[CLS]', 0.5060293674468994), ('[CLS]', 0.5062680840492249), ('[CLS]', 0.5059909224510193), ('[CLS]', 0.5056659579277039), ('[CLS]', 0.5060045719146729), ('[CLS]', 0.5064235329627991), ('[CLS]', 0.505791962146759), ('[CLS]', 0.5063173770904541), ('[CLS]', 0.5058268904685974), ('[CLS]', 0.5060592293739319), ('[CLS]', 0.5063878893852234), ('[CLS]', 0.5065939426422119), ('[CLS]', 0.5059806108474731), ('[CLS]', 0.505949079990387), ('[CLS]', 0.5061217546463013), ('[CLS]', 0.5054893493652344), ('[CLS]', 0.5059810280799866), ('[CLS]', 0.5060664415359497), ('[CLS]', 0.506255030632019), ('[CLS]', 0.5060448050498962), ('[CLS]', 0.5057430863380432), ('[CLS]', 0.5060313940048218), ('[CLS]', 0.5054957866668701), ('[CLS]', 0.5062739253044128), ('[CLS]', 0.5064918398857117)] Result: [('此', '[CLS]'), ('外', '[CLS]'), ('网', '[CLS]'), ('易', '[CLS]'), ('云', '[CLS]'), ('平', '[CLS]'), ('台', '[CLS]'), ('还', '[CLS]'), ('上', '[CLS]'), ('架', '[CLS]'), ('了', '[CLS]'), ('一', '[CLS]'), ('系', '[CLS]'), ('列', '[CLS]'), ('歌', '[CLS]'), ('曲', '[CLS]'), ('，', '[CLS]'), ('其', '[CLS]'), ('中', '[CLS]'), ('包', '[CLS]'), ('括', '[CLS]'), ('田', '[CLS]'), ('馥', '[CLS]'), ('甄', '[CLS]'), ('的', '[CLS]'), ('《', '[CLS]'), ('小', '[CLS]'), ('幸', '[CLS]'), ('运', '[CLS]'), ('》', '[CLS]'), ('等', '[CLS]')] 它好像没有成功运行NER任务，我的NER和RE都可以单独正确执行，这是因为什么呢

您好，经过我们验证没有遇到您的问题，可以麻烦发一下详细的参数配置和运行场景吗？

[F2023888]

参数就是修改了这个config.yaml和lm.yaml（从hugging——face下载到本地的bert_base_chinese的路径）

[F2023888]

packages in environment at D:\Anaconda_envs\envs\rz:

Name Version Build Channel

absl-py 2.1.0 pypi_0 pypi
aiohttp 3.9.5 pypi_0 pypi
aiosignal 1.3.1 pypi_0 pypi
antlr4-python3-runtime 4.8 pypi_0 pypi
asttokens 2.4.1 pypi_0 pypi
async-timeout 4.0.3 pypi_0 pypi
attrs 23.2.0 pypi_0 pypi
backcall 0.2.0 pypi_0 pypi
blinker 1.8.2 pypi_0 pypi
ca-certificates 2024.3.11 haa95532_0 defaults
cachetools 4.2.4 pypi_0 pypi
certifi 2024.2.2 pypi_0 pypi
charset-normalizer 3.3.2 pypi_0 pypi
click 8.1.7 pypi_0 pypi
colorama 0.4.6 pypi_0 pypi
configparser 7.0.0 pypi_0 pypi
cycler 0.12.1 pypi_0 pypi
datasets 2.13.2 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
dill 0.3.6 pypi_0 pypi
docker-pycreds 0.4.0 pypi_0 pypi
environs 11.0.0 pypi_0 pypi
executing 2.0.1 pypi_0 pypi
filelock 3.14.0 pypi_0 pypi
flask 3.0.3 pypi_0 pypi
flask-cors 4.0.1 pypi_0 pypi
frozendict 2.4.4 pypi_0 pypi
frozenlist 1.4.1 pypi_0 pypi
fsspec 2024.6.0 pypi_0 pypi
gitdb 4.0.11 pypi_0 pypi
gitpython 3.1.43 pypi_0 pypi
google-auth 1.35.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
grpcio 1.64.1 pypi_0 pypi
huggingface-hub 0.11.0 pypi_0 pypi
hydra-core 1.0.6 pypi_0 pypi
idna 3.7 pypi_0 pypi
importlib-metadata 7.1.0 pypi_0 pypi
importlib-resources 6.4.0 pypi_0 pypi
interchange 2021.0.4 pypi_0 pypi
ipdb 0.13.11 pypi_0 pypi
ipython 8.12.3 pypi_0 pypi
itsdangerous 2.2.0 pypi_0 pypi
jedi 0.19.1 pypi_0 pypi
jieba 0.42.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
joblib 1.4.2 pypi_0 pypi
jsonlines 4.0.0 pypi_0 pypi
kiwisolver 1.4.5 pypi_0 pypi
libffi 3.4.4 hd77b12b_1 defaults
lxml 5.2.2 pypi_0 pypi
markdown 3.6 pypi_0 pypi
markupsafe 2.1.5 pypi_0 pypi
marshmallow 3.21.2 pypi_0 pypi
matplotlib 3.4.1 pypi_0 pypi
matplotlib-inline 0.1.7 pypi_0 pypi
monotonic 1.6 pypi_0 pypi
multidict 6.0.5 pypi_0 pypi
multiprocess 0.70.14 pypi_0 pypi
mysql-connector 2.2.9 pypi_0 pypi
neo4j 5.20.0 pypi_0 pypi
nltk 3.8 pypi_0 pypi
numpy 1.21.0 pypi_0 pypi
oauthlib 3.2.2 pypi_0 pypi
omegaconf 2.0.6 pypi_0 pypi
openai 0.28.0 pypi_0 pypi
openssl 3.0.13 h2bbff1b_2 defaults
opt-einsum 3.3.0 pypi_0 pypi
packaging 24.0 pypi_0 pypi
pandas 2.0.3 pypi_0 pypi
pansi 2020.7.3 pypi_0 pypi
parso 0.8.4 pypi_0 pypi
pathtools 0.1.2 pypi_0 pypi
pickleshare 0.7.5 pypi_0 pypi
pillow 10.3.0 pypi_0 pypi
pip 24.0 py38haa95532_0 defaults
promise 2.3 pypi_0 pypi
prompt-toolkit 3.0.47 pypi_0 pypi
protobuf 3.20.1 pypi_0 pypi
psutil 5.9.8 pypi_0 pypi
psycopg2 2.9.9 pypi_0 pypi
pure-eval 0.2.2 pypi_0 pypi
py2neo 2021.2.4 pypi_0 pypi
pyarrow 16.1.0 pypi_0 pypi
pyasn1 0.6.0 pypi_0 pypi
pyasn1-modules 0.4.0 pypi_0 pypi
pygments 2.18.0 pypi_0 pypi
pyhocon 0.3.60 pypi_0 pypi
pyld 2.0.4 pypi_0 pypi
pymysql 1.1.1 pypi_0 pypi
pyparsing 3.1.2 pypi_0 pypi
python 3.8.19 h1aa4202_0 defaults
python-dateutil 2.9.0.post0 pypi_0 pypi
python-dotenv 1.0.1 pypi_0 pypi
pytorch-crf 0.7.2 pypi_0 pypi
pytz 2024.1 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
regex 2024.5.15 pypi_0 pypi
requests 2.32.3 pypi_0 pypi
requests-oauthlib 2.0.0 pypi_0 pypi
rsa 4.9 pypi_0 pypi
safetensors 0.4.3 pypi_0 pypi
scikit-learn 0.24.1 pypi_0 pypi
scipy 1.10.1 pypi_0 pypi
sentry-sdk 2.5.1 pypi_0 pypi
seqeval 1.2.2 pypi_0 pypi
setuptools 69.5.1 py38haa95532_0 defaults
shortuuid 1.0.13 pypi_0 pypi
six 1.16.0 pypi_0 pypi
smmap 5.0.1 pypi_0 pypi
sqlite 3.45.3 h2bbff1b_0 defaults
stack-data 0.6.3 pypi_0 pypi
subprocess32 3.5.4 pypi_0 pypi
tensorboard 2.4.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorboardx 2.5.1 pypi_0 pypi
termcolor 2.4.0 pypi_0 pypi
threadpoolctl 3.5.0 pypi_0 pypi
tokenizers 0.13.3 pypi_0 pypi
tomli 2.0.1 pypi_0 pypi
torch 1.11.0+cu113 pypi_0 pypi
tqdm 4.66.1 pypi_0 pypi
traitlets 5.14.3 pypi_0 pypi
transformers 4.26.0 pypi_0 pypi
typing-extensions 4.12.1 pypi_0 pypi
tzdata 2024.1 pypi_0 pypi
ujson 5.6.0 pypi_0 pypi
urllib3 2.2.1 pypi_0 pypi
vc 14.2 h2eaa2aa_1 defaults
vs2015_runtime 14.29.30133 h43f2093_3 defaults
wandb 0.12.7 pypi_0 pypi
wcwidth 0.2.13 pypi_0 pypi
werkzeug 3.0.3 pypi_0 pypi
wheel 0.43.0 py38haa95532_0 defaults
xxhash 3.4.1 pypi_0 pypi
yarl 1.9.4 pypi_0 pypi
yaspin 2.5.0 pypi_0 pypi
zipp 3.19.0 pypi_0 pypi

这个是我的conda list

[F2023888]

这个是predict.py
`logger = logging.getLogger(name)

def _preprocess_data(data, cfg):

relation_data = load_csv(os.path.join(cfg.cwd, cfg.data_path, 'relation.csv'), verbose=False)
rels = _handle_relation_data(relation_data)
_lm_serialize(data,cfg)
return data, rels

def get_jsonld(head,rel,tail,url):

doc = {
"@id": head,
url: {"@id":tail},
}
context = {
    rel:url
}
compacted = jsonld.compact(doc, context)
logger.info(json.dumps(compacted, indent=2,ensure_ascii=False))

@hydra.main(config_path="conf", config_name='config')
def main(cfg):
cwd = utils.get_original_cwd()
cfg.cwd = cwd

label2word = {}
with open(os.path.join(cfg.cwd, cfg.data_path, 'type.txt'), 'r', encoding='utf-8') as f:
    data = f.readlines()
    for d in data:
        label2word[d.split(' ')[1].strip('\n')] = d.split(' ')[0]
logger.info(label2word)
rel2url = {}
with open(os.path.join(cfg.cwd, cfg.data_path, 'url.txt'), 'r', encoding='utf-8') as f:
    data = f.readlines()
    for d in data:
        rel2url[d.split(' ')[0]] = d.split(' ')[1].strip('\n')
logger.info(rel2url)
model = InferNer(cfg.nerfp)
text = cfg.text

print("text")
logger.info(text)

result = model.predict(text)
print("result")
logger.info(result)
temp = ''
last_type = result[0][1][2:]
res = {}
word_len = len(result)

for i in range(word_len):
    k = result[i][0]
    v = result[i][1]
    
    if v[0] == 'B':
        if temp != '':
            res[temp] =  label2word[result[i - 1][1][2:]]
        temp = k
        last_type = result[i][1][2:]
    elif v[0] == 'I':
        if last_type == result[i][1][2:]:
            temp += k        
    

if temp != '':
    res[temp] = label2word[result[len(result) - 1][1][2:]]
logger.info(res)
entity = []
entity_label = []
for k,v in res.items():
  entity.append(k)
  entity_label.append(v)

entity_len = len(entity_label)

for i in range(entity_len):
  for j in range(i + 1,entity_len):
    instance = dict()
    instance['sentence'] = text.strip()
    instance['head'] = entity[i].strip()
    instance['tail'] = entity[j].strip()
    instance['head_type'] = entity_label[i].strip()
    instance['tail_type'] = entity_label[j].strip()

    data = [instance]
    data, rels = _preprocess_data(data, cfg)

    device = torch.device('cpu')

    model = LM(cfg)
    model.load(cfg.refp, device=device)
    model.to(device)
    model.eval()

    x = dict()
    x['word'], x['lens'] = torch.tensor([data[0]['token2idx']]), torch.tensor([data[0]['seq_len']])
    for key in x.keys():
        x[key] = x[key].to(device)

    with torch.no_grad():
        y_pred = model(x)
        y_pred = torch.softmax(y_pred, dim=-1)[0]
        prob = y_pred.max().item()
        prob_rel = list(rels.keys())[y_pred.argmax().item()]
        logger.info(f"\"{data[0]['head']}\" 和 \"{data[0]['tail']}\" 在句中关系为：\"{prob_rel}\"，置信度为{prob:.2f}。")
        get_jsonld(data[0]['head'],prob_rel,data[0]['tail'],rel2url[prob_rel])

if name == 'main':
main()`

[F2023888]

抱歉，我的pytorch_transformers没有下载，现在已经解决了