some problems

[milky-FatJay]

Traceback (most recent call last):
File "/home/yanghaofeng/anaconda3/envs/dygiepp/bin/allennlp", line 8, in
sys.exit(run())
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/main.py", line 34, in run
main(prog="allennlp")
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/commands/init.py", line 92, in main
args.func(args)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/commands/predict.py", line 211, in _predict
predictor = _get_predictor(args)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/commands/predict.py", line 110, in _get_predictor
overrides=args.overrides,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/models/archival.py", line 191, in load_archive
cuda_device=cuda_device,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/models/model.py", line 367, in load
return model_class._load(config, serialization_dir, weights_file, cuda_device)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/models/model.py", line 295, in _load
model = Model.from_params(vocab=vocab, params=model_params)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 595, in from_params
**extras,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 622, in from_params
kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 193, in create_kwargs
cls.name, param_name, annotation, param.default, params, **extras
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 302, in pop_and_construct_arg
return construct_arg(class_name, name, popped_params, annotation, default, **extras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 336, in construct_arg
return annotation.from_params(params=popped_params, **subextras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 595, in from_params
**extras,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 622, in from_params
kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 193, in create_kwargs
cls.name, param_name, annotation, param.default, params, **extras
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 302, in pop_and_construct_arg
return construct_arg(class_name, name, popped_params, annotation, default, **extras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 383, in construct_arg
**extras,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 336, in construct_arg
return annotation.from_params(params=popped_params, **subextras)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 595, in from_params
**extras,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/from_params.py", line 624, in from_params
return constructor_to_call(**kwargs) # type: ignore
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/modules/token_embedders/pretrained_transformer_mismatched_embedder.py", line 53, in init
gradient_checkpointing=gradient_checkpointing,
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/modules/token_embedders/pretrained_transformer_embedder.py", line 63, in init
model_name, True, override_weights_file, override_weights_strip_prefix
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/allennlp/common/cached_transformers.py", line 79, in get
transformer = AutoModel.from_pretrained(model_name)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/transformers/modeling_auto.py", line 502, in from_pretrained
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
File "/home/yanghaofeng/anaconda3/envs/dygiepp/lib/python3.7/site-packages/transformers/modeling_utils.py", line 662, in from_pretrained
raise EnvironmentError(msg)
OSError: Can't load weights for 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'. Make sure that:

• 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' is a correct model identifier listed on 'https://huggingface.co/models'

• or 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.
  在执行factual_serialization.py出现了如上错误，但是文件中根本没有microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext，请问你有遇到这种问题吗

[mk-runner]

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext is a pre-trained model used in RadGraph, which can be downloaded in huggingface.

The experimental setup includes two virtual environments: one for extracting factual serialization from radiology reports using the structural entities approach (see knowledge_encoder/radgraph_requirements.txt for details), and the other for running the overall FSE framework excluding the structural entities approach (see requirements.txt for more information).

[milky-FatJay]

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext is a pre-trained model used in RadGraph, which can be downloaded in huggingface.

The experimental setup includes two virtual environments: one for extracting factual serialization from radiology reports using the structural entities approach (see knowledge_encoder/radgraph_requirements.txt for details), and the other for running the overall FSE framework excluding the structural entities approach (see requirements.txt for more information).

感谢作者的提醒，我把radgraph里面的model.tar.gz解压了之后发现了这个配置，现在问题已经解决了

[milky-FatJay]

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext is a pre-trained model used in RadGraph, which can be downloaded in huggingface.

The experimental setup includes two virtual environments: one for extracting factual serialization from radiology reports using the structural entities approach (see knowledge_encoder/radgraph_requirements.txt for details), and the other for running the overall FSE framework excluding the structural entities approach (see requirements.txt for more information).

请问mimic_cxr_annotation_sen_best_reports_keywords_20.json是哪里来的，我经过运行knowledge_encoder/factual_serialization.py只生成了mimic_cxr_annotation_sen.json

[mk-runner]