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lxucs / coref-hoi

PyTorch implementation of the end-to-end coreference resolution model with different higher-order inference methods.
Apache License 2.0
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ValueError when predicting #15

Open qinliu9 opened 3 years ago

qinliu9 commented 3 years ago

All the data and models required have been downloaded into proper path.

Trying to run predict.py with command: python predict.py --config_name=train_spanbert_large_ml0_d2 --model_identifier=May08_12-38-29_58000 --gpu_id=0 and encounter ValueError:

Traceback (most recent call last): File "predict.py", line 71, in nlp.add_pipe(nlp.create_pipe('sentencizer')) File "/home/qliu/anaconda3/envs/e2e/lib/python3.6/site-packages/spacy/language.py", line 754, in add_pipe raise ValueError(err) ValueError: [E966] nlp.add_pipe now takes the string name of the registered component factory, not a callable component. Expected string, but got <spacy.pipeline.sentencizer.Sentencizer object at 0x7f7fabe3f288> (name: 'None').

  • If you created your component with nlp.create_pipe('name'): remove nlp.create_pipe and call nlp.add_pipe('name') instead.

  • If you passed in a component like TextCategorizer(): call nlp.add_pipe with the string name instead, e.g. nlp.add_pipe('textcat').

  • If you're using a custom component: Add the decorator @Language.component (for function components) or @Language.factory (for class components / factories) to your custom component and assign it a name, e.g. @Language.component('your_name'). You can then run nlp.add_pipe('your_name') to add it to the pipeline.