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[Question] running self trained NER model causes errors#46
Description
What is your question?
error message:
Traceback (most recent call last):
File "/root/raid/electrolyte_papers_extraction/NER/ner.py", line 31, in <module>
ner = NER('ckpt/ner/240731013859.938009/output_best', device = 'gpu')
File "/root/raid/electrolyte_papers_extraction/NER/ner.py", line 12, in __init__
self.pipeline = pipeline(Tasks.named_entity_recognition, abspath(ckpt), device = device)
File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/builder.py", line 169, in pipeline
return build_pipeline(cfg, task_name=task)
File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/builder.py", line 65, in build_pipeline
return build_from_cfg(
File "/usr/local/lib/python3.10/dist-packages/modelscope/utils/registry.py", line 215, in build_from_cfg
raise type(e)(f'{obj_cls.__name__}: {e}')
RuntimeError: SequenceLabelingPipeline: SequenceLabelingModel: TransformerEmbedder: Try loading from huggingface and modelscope failed
huggingface:
The request model: google-bert/bert-base-cased does not exist!
modelscope:
The request model: google-bert/bert-base-cased does not exist!
File "/root/raid/electrolyte_papers_extraction/NER/ner.py", line 31, in <module>
ner = NER('ckpt/ner/240731013859.938009/output_best', device = 'gpu')
File "/root/raid/electrolyte_papers_extraction/NER/ner.py", line 12, in __init__
self.pipeline = pipeline(Tasks.named_entity_recognition, abspath(ckpt), device = device)
File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/builder.py", line 169, in pipeline
return build_pipeline(cfg, task_name=task)
File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/builder.py", line 65, in build_pipeline
return build_from_cfg(
File "/usr/local/lib/python3.10/dist-packages/modelscope/utils/registry.py", line 215, in build_from_cfg
raise type(e)(f'{obj_cls.__name__}: {e}')
RuntimeError: SequenceLabelingPipeline: SequenceLabelingModel: TransformerEmbedder: Try loading from huggingface and modelscope failed
huggingface:
The request model: google-bert/bert-base-cased does not exist!
modelscope:
The request model: google-bert/bert-base-cased does not exist!
self trained NER checkpoint:
What have you tried?
under ckpt/ner/. edit
from
"plugins": [
"adaseq"
]
"adaseq"
]
to
"plugins": [
"https://files.pythonhosted.org/packages/49/47/ddf684253dbb4c3e0716fcda67094aa3c407237d5eb8930ede0a91b9feb8/adaseq-0.6.6-py3-none-any.whl"
]
"https://files.pythonhosted.org/packages/49/47/ddf684253dbb4c3e0716fcda67094aa3c407237d5eb8930ede0a91b9feb8/adaseq-0.6.6-py3-none-any.whl"
]
Code (if necessary)
source code:
from os.path import abspath
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
pipeline_ = pipeline(Tasks.named_entity_recognition, abspath('ckpt/ner/240731013859.938009/output_best'), device = 'gpu')
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
pipeline_ = pipeline(Tasks.named_entity_recognition, abspath('ckpt/ner/240731013859.938009/output_best'), device = 'gpu')
What's your environment?
- AdaSeq Version (e.g., 1.0 or master): 0.6.6
- ModelScope Version (e.g., 1.0 or master): 1.16.1
- PyTorch Version (e.g., 1.12.1): 2.2.0a0+81ea7a4
- OS (e.g., Ubuntu 20.04): Ubuntu 22.04.3 LTS
- Python version: 3.10.12
- CUDA/cuDNN version: 8.9.7.29-1+cuda12.2
- GPU models and configuration: A100
- Any other relevant information:
Code of Conduct
- I agree to follow this project's Code of Conduct