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2023-06-08 14:43:53,296 - INFO - modelscope - Checkpoints will be saved to NER/koubei/230608144347.471030 2023-06-08 14:43:53,296 - INFO - adaseq.training.hooks.text_logger_hook - Text logs will be saved to: NER/koubei/230608144347.471030/metrics.json 2023-06-08 14:43:53,369 - INFO - modelscope - tensorboard files will be saved to NER/koubei/230608144347.471030/tensorboard_output /data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/modules/decoders/crf.py:293: UserWarning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (Triggered internally at ../aten/src/ATen/native/TensorCompare.cpp:402.) score = torch.where(mask[i].unsqueeze(1), next_score, score) 2023-06-08 14:44:00,504 - INFO - modelscope - epoch [1][50/478] lr: 5.000e-05, eta: 0:05:33, iter_time: 0.143, data_load_time: 0.013, memory: 2636, loss: 24.8958 2023-06-08 14:44:05,806 - INFO - modelscope - epoch [1][100/478] lr: 5.000e-05, eta: 0:04:44, iter_time: 0.106, data_load_time: 0.011, memory: 2689, loss: 6.3790 2023-06-08 14:44:11,301 - INFO - modelscope - epoch [1][150/478] lr: 5.000e-05, eta: 0:04:27, iter_time: 0.110, data_load_time: 0.012, memory: 2689, loss: 4.3439 2023-06-08 14:44:16,351 - INFO - modelscope - epoch [1][200/478] lr: 5.000e-05, eta: 0:04:11, iter_time: 0.101, data_load_time: 0.012, memory: 2689, loss: 3.1956 2023-06-08 14:44:22,130 - INFO - modelscope - epoch [1][250/478] lr: 5.000e-05, eta: 0:04:06, iter_time: 0.116, data_load_time: 0.014, memory: 2800, loss: 3.9149 2023-06-08 14:44:28,207 - INFO - modelscope - epoch [1][300/478] lr: 5.000e-05, eta: 0:04:02, iter_time: 0.122, data_load_time: 0.015, memory: 2800, loss: 3.5845 2023-06-08 14:44:33,858 - INFO - modelscope - epoch [1][350/478] lr: 5.000e-05, eta: 0:03:55, iter_time: 0.113, data_load_time: 0.014, memory: 2800, loss: 2.7284 2023-06-08 14:44:38,887 - INFO - modelscope - epoch [1][400/478] lr: 5.000e-05, eta: 0:03:46, iter_time: 0.101, data_load_time: 0.012, memory: 2800, loss: 2.6073 2023-06-08 14:44:44,202 - INFO - modelscope - epoch [1][450/478] lr: 5.000e-05, eta: 0:03:39, iter_time: 0.106, data_load_time: 0.012, memory: 2800, loss: 2.7159 Total test samples: 0%|▏ | 1/463 [00:00<00:46, 9.89it/sTotal test samples: 4%|███▏ | 17/463 [00:00<00:14, 30.77iTotal test samples: 11%|█████████▎ | 49/463 [00:00<00:04, Total test samples: 17%|███████████████▏ | 81/463 [00:00<00Total test samples: 24%|████████████████████▉ | 113/463 [00:Total test samples: 31%|██████████████████████████▉ | 145/46Total test samples: 38%|████████████████████████████████▉ | Total test samples: 45%|██████████████████████████████████████▊ Total test samples: 52%|████████████████████████████████████████████▊ Total test samples: 62%|█████████████████████████████████████████████████████▋ Total test samples: 69%|███████████████████████████████████████████████████████████▌ Total test samples: 76%|████████████████████████████████████████████████████████████Total test samples: 87%|████████████████████████████████████████████████████████████Total test samples: 97%|████████████████████████████████████████████████████████████Total test samples: 100%|██████████████████████████████████████████████████████████████████████████████████████| 463/463 [00:02<00:00, 227.71it/s] Traceback (most recent call last): File "/data/ningfeim/.myconda/envs/modelscope/bin/adaseq", line 8, in sys.exit(run()) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/main.py", line 13, in run main(prog='adaseq') File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/commands/init.py", line 29, in main args.func(args) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/commands/train.py", line 85, in train_model_from_args checkpoint_path=args.checkpoint_path, File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/commands/train.py", line 144, in train_model trainer.train(checkpoint_path) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/training/default_trainer.py", line 146, in train return super().train(checkpoint_path=checkpoint_path, *args, **kwargs) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/trainer.py", line 676, in train self.train_loop(self.train_dataloader) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/trainer.py", line 1181, in train_loop self.invoke_hook(TrainerStages.after_train_epoch) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/trainer.py", line 1328, in invoke_hook getattr(hook, fn_name)(self) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/hooks/evaluation_hook.py", line 35, in after_train_epoch self.do_evaluate(trainer) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/hooks/evaluation_hook.py", line 47, in do_evaluate eval_res = trainer.evaluate() File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/trainer.py", line 764, in evaluate metric_classes) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/trainer.py", line 1239, in evaluation_loop data_loader_iters=self._eval_iters_per_epoch) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/utils/inference.py", line 77, in single_gpu_test return get_metric_values(metric_classes) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/modelscope/trainers/utils/inference.py", line 194, in get_metric_values metric_values.update(metric_cls.evaluate()) File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/data/dataset_dumpers/base.py", line 26, in evaluate self.dump() File "/data/ningfeim/.myconda/envs/modelscope/lib/python3.7/site-packages/adaseq/data/dataset_dumpers/named_entity_recognition_dataset_dumper.py", line 40, in dump raise NotImplementedError NotImplementedError

环境： python3.7 ubuntu 22.04 cuda11.7

Name Version Build Channel

_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
absl-py 1.4.0 pypi_0 pypi adaseq 0.6.2 pypi_0 pypi addict 2.4.0 pypi_0 pypi aiohttp 3.8.4 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi albumentations 1.3.0 pypi_0 pypi aliyun-python-sdk-core 2.13.36 pypi_0 pypi aliyun-python-sdk-kms 2.16.0 pypi_0 pypi apex 0.1 pypi_0 pypi async-timeout 4.0.2 pypi_0 pypi asynctest 0.13.0 pypi_0 pypi attrs 22.2.0 pypi_0 pypi beautifulsoup4 4.11.2 pypi_0 pypi blis 0.7.9 pypi_0 pypi boto3 1.26.89 pypi_0 pypi botocore 1.29.89 pypi_0 pypi bs4 0.0.1 pypi_0 pypi ca-certificates 2023.01.10 h06a4308_0
cachetools 5.3.0 pypi_0 pypi catalogue 2.0.8 pypi_0 pypi certifi 2022.12.7 py37h06a4308_0
cffi 1.15.1 pypi_0 pypi charset-normalizer 3.1.0 pypi_0 pypi click 8.1.3 pypi_0 pypi confection 0.0.4 pypi_0 pypi crcmod 1.7 pypi_0 pypi cryptography 39.0.2 pypi_0 pypi cycler 0.11.0 pypi_0 pypi cymem 2.0.7 pypi_0 pypi datasets 2.8.0 pypi_0 pypi deepspeed 0.8.2 pypi_0 pypi dill 0.3.6 pypi_0 pypi einops 0.6.0 pypi_0 pypi en-core-web-sm 3.5.0 pypi_0 pypi filelock 3.9.0 pypi_0 pypi fonttools 4.38.0 pypi_0 pypi frozenlist 1.3.3 pypi_0 pypi fsspec 2023.1.0 pypi_0 pypi ftfy 6.1.1 pypi_0 pypi gast 0.5.3 pypi_0 pypi google-auth 2.16.2 pypi_0 pypi google-auth-oauthlib 0.4.6 pypi_0 pypi grpcio 1.52.0 pypi_0 pypi hjson 3.1.0 pypi_0 pypi huggingface-hub 0.13.1 pypi_0 pypi idna 3.4 pypi_0 pypi imageio 2.26.0 pypi_0 pypi importlib-metadata 6.0.0 pypi_0 pypi jieba 0.42.1 pypi_0 pypi jinja2 3.1.2 pypi_0 pypi jmespath 0.10.0 pypi_0 pypi joblib 1.2.0 pypi_0 pypi jsonplus 0.8.0 pypi_0 pypi kiwisolver 1.4.4 pypi_0 pypi langcodes 3.3.0 pypi_0 pypi ld_impl_linux-64 2.38 h1181459_1
libffi 3.4.2 h6a678d5_6
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libstdcxx-ng 11.2.0 h1234567_1
markdown 3.4.1 pypi_0 pypi markupsafe 2.1.2 pypi_0 pypi matplotlib 3.5.3 pypi_0 pypi megatron-util 1.3.1 pypi_0 pypi mmdet 2.28.2 pypi_0 pypi mock 5.0.1 pypi_0 pypi modelscope 1.4.2 pypi_0 pypi multidict 6.0.4 pypi_0 pypi multiprocess 0.70.14 pypi_0 pypi murmurhash 1.0.9 pypi_0 pypi ncurses 6.4 h6a678d5_0
networkx 2.6.3 pypi_0 pypi ninja 1.11.1 pypi_0 pypi nltk 3.8.1 pypi_0 pypi numpy 1.21.6 pypi_0 pypi nvidia-cublas-cu11 11.10.3.66 pypi_0 pypi nvidia-cuda-nvrtc-cu11 11.7.99 pypi_0 pypi nvidia-cuda-runtime-cu11 11.7.99 pypi_0 pypi nvidia-cudnn-cu11 8.5.0.96 pypi_0 pypi oauthlib 3.2.2 pypi_0 pypi opencv-python-headless 4.7.0.72 pypi_0 pypi openssl 1.1.1t h7f8727e_0
oss2 2.17.0 pypi_0 pypi packaging 23.0 pypi_0 pypi pai-easynlp 0.0.7 pypi_0 pypi pandas 1.3.5 pypi_0 pypi pathy 0.10.1 pypi_0 pypi pillow 9.4.0 pypi_0 pypi pip 22.3.1 py37h06a4308_0
preshed 3.0.8 pypi_0 pypi protobuf 3.19.0 pypi_0 pypi psutil 5.9.4 pypi_0 pypi py-cpuinfo 9.0.0 pypi_0 pypi pyarrow 11.0.0 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pycocotools 2.0.6 pypi_0 pypi pycparser 2.21 pypi_0 pypi pycryptodome 3.17 pypi_0 pypi pydantic 1.10.6 pypi_0 pypi pymysql 1.0.2 pypi_0 pypi pyparsing 3.0.9 pypi_0 pypi pythainlp 3.1.1 pypi_0 pypi python 3.7.16 h7a1cb2a_0
python-crfsuite 0.9.9 pypi_0 pypi python-dateutil 2.8.2 pypi_0 pypi pytz 2022.7.1 pypi_0 pypi pyvi 0.1.1 pypi_0 pypi pywavelets 1.4.0 pypi_0 pypi pyyaml 6.0 pypi_0 pypi qudida 0.0.4 pypi_0 pypi readline 8.2 h5eee18b_0
regex 2022.10.31 pypi_0 pypi requests 2.28.2 pypi_0 pypi requests-oauthlib 1.3.1 pypi_0 pypi responses 0.18.0 pypi_0 pypi rouge 1.0.1 pypi_0 pypi rsa 4.9 pypi_0 pypi s3transfer 0.6.0 pypi_0 pypi sacremoses 0.0.53 pypi_0 pypi scikit-image 0.19.3 pypi_0 pypi scikit-learn 1.0.2 pypi_0 pypi scipy 1.7.3 pypi_0 pypi sentencepiece 0.1.97 pypi_0 pypi seqeval 1.2.2 pypi_0 pypi setuptools 59.8.0 pypi_0 pypi simplejson 3.18.3 pypi_0 pypi six 1.16.0 pypi_0 pypi sklearn-crfsuite 0.3.6 pypi_0 pypi smart-open 6.3.0 pypi_0 pypi sortedcontainers 2.4.0 pypi_0 pypi soupsieve 2.4 pypi_0 pypi spacy 3.5.1 pypi_0 pypi spacy-legacy 3.0.12 pypi_0 pypi spacy-loggers 1.0.4 pypi_0 pypi sqlite 3.40.1 h5082296_0
srsly 2.4.6 pypi_0 pypi subword-nmt 0.3.8 pypi_0 pypi tabulate 0.9.0 pypi_0 pypi tensorboard 2.11.2 pypi_0 pypi tensorboard-data-server 0.6.1 pypi_0 pypi tensorboard-plugin-wit 1.8.1 pypi_0 pypi termcolor 2.2.0 pypi_0 pypi terminaltables 3.1.10 pypi_0 pypi thinc 8.1.9 pypi_0 pypi threadpoolctl 3.1.0 pypi_0 pypi tifffile 2021.11.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0
tokenizers 0.13.2 pypi_0 pypi torch 1.12.1+cu113 pypi_0 pypi torchvision 0.14.1 pypi_0 pypi tornado 6.2 pypi_0 pypi tqdm 4.65.0 pypi_0 pypi transformers 4.26.1 pypi_0 pypi typer 0.7.0 pypi_0 pypi typing-extensions 4.4.0 pypi_0 pypi urllib3 1.26.15 pypi_0 pypi wasabi 1.1.1 pypi_0 pypi wcwidth 0.2.6 pypi_0 pypi werkzeug 2.2.3 pypi_0 pypi wheel 0.38.4 py37h06a4308_0
xxhash 3.2.0 pypi_0 pypi xz 5.2.10 h5eee18b_1
yapf 0.32.0 pypi_0 pypi yarl 1.8.2 pypi_0 pypi zhconv 1.4.3 pypi_0 pypi zipp 3.15.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0

Describe the solution you'd like.

最后一行依赖对应报错行：这块是在判断训练集类型conll

yaml训练内如下： experiment: exp_dir: NER/ # 所有实验的根文件夹 exp_name: koubei # 本次配置文件的实验名称 seed: 42 # 随机种子

dataset: data_file: # 数据文件 train: '/data/ningfeim/test/NER/koubei/datas/train.txt' dev: '/data/ningfeim/test/NER/koubei/datas/dev.txt' test: '/data/ningfeim/test/NER/koubei/datas/test.txt' data_type: conll # 数据格式

task: named-entity-recognition # 任务名称，用于加载内建的 DatasetBuilder（如果需要的话）

preprocessor: type: sequence-labeling-preprocessor # 预处理器名称 model_dir: /data/ningfeim/project/Bert-ner-yiwei/bert #huggingface/modelscope 模型名字或路径，用于初始化 Tokenizer，可缺省 max_length: 512 # 预训练模型支持的最大输入长度

data_collator: SequenceLabelingDataCollatorWithPadding # 用于 batch 转换的 data_collator 名称

model: type: sequence-labeling-model # 模型名称 embedder: model_name_or_path: /data/ningfeim/test/NER/koubei/nlp_raner_named-entity-recognition_chinese-base-news # 预训练模型名称或路径，可以是huggingface/modelscope的backbone模型，或者也可以加载modelscope上的任务模型 dropout: 0.1 # dropout 概率 use_crf: true # 是否使用CRF

train: hooks: - type: TensorboardHook max_epochs: 5 # 最大训练轮数 dataloader: batch_size_per_gpu: 8 # 训练batch_size optimizer: type: AdamW # pytorch 优化器名称 lr: 5.0e-5 # 全局学习率 param_groups: - regex: crf lr: 5.0e-1 lr_scheduler: type: LinearLR # transformers 或 pytorch 的 lr_scheduler 名称 start_factor: 1.0 end_factor: 0.0 total_iters: 20

evaluation: dataloader: batch_size_per_gpu: 16 # 评估batch_size metrics: - type: ner-metric # 所有已实现的metric见 adaseq/metainfo.py 的 Metrics 类。 - type: ner-dumper # 输出预测结果 model_type: sequence_labeling dump_format: column

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Jun 08 '23 07:06 masir110

请问这是什么问题呢，如有相关讨论社区麻烦也给发一下

Jun 08 '23 07:06 masir110

它的配置文件不能照抄，这里说dump_format只实现了两种格式的输出：conll和jsonline，，给的配置文件里面是column

Jan 12 '24 03:01 wanshichenguang

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NotImplementedError

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AdaSeq
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