spaCy errors encountered while trying out transformer-srl library on Win 10, Python 3.8
pragyakatyayan opened this issue · comments
I successfully installed transformers-srl library using my Jupyter-notebook terminal. However, I encountered the following error while trying to run the following code. I am using a Windows 10 system with Python- 3.8.
code:
from transformer_srl import dataset_readers, models, predictors
predictor = predictors.SrlTransformersPredictor.from_path("D:/srl_bert_base_conll2012.tar.gz", "transformer_srl")
predictor.predict(sentence="Did Uriah honestly think he could beat the game in under three hours?")
error with spacy version- 2.2.4
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-27de09498ecb> in <module>
1 from transformer_srl import dataset_readers, models, predictors
2
----> 3 predictor = predictors.SrlTransformersPredictor.from_path("D:/srl_bert_base_conll2012.tar.gz", "transformer_srl")
4 predictor.predict(sentence="Did Uriah honestly think he could beat the game in under three hours?")
~\anaconda3\lib\site-packages\transformer_srl\predictors.py in from_path(cls, archive_path, predictor_name, cuda_device, dataset_reader_to_load, frozen, import_plugins, language, restrict_frames, restrict_roles)
153 if import_plugins:
154 plugins.import_plugins()
--> 155 return SrlTransformersPredictor.from_archive(
156 load_archive(archive_path, cuda_device=cuda_device),
157 predictor_name,
~\anaconda3\lib\site-packages\transformer_srl\predictors.py in from_archive(cls, archive, predictor_name, dataset_reader_to_load, frozen, language, restrict_frames, restrict_roles)
197 model.eval()
198
--> 199 return predictor_class(model, dataset_reader, language)
~\anaconda3\lib\site-packages\transformer_srl\predictors.py in __init__(self, model, dataset_reader, language)
20 self, model: Model, dataset_reader: DatasetReader, language: str = "en_core_web_sm",
21 ) -> None:
---> 22 super().__init__(model, dataset_reader, language)
23
24 @staticmethod
~\anaconda3\lib\site-packages\allennlp_models\structured_prediction\predictors\srl.py in __init__(self, model, dataset_reader, language)
22 ) -> None:
23 super().__init__(model, dataset_reader)
---> 24 self._tokenizer = SpacyTokenizer(language=language, pos_tags=True)
25
26 def predict(self, sentence: str) -> JsonDict:
~\anaconda3\lib\site-packages\allennlp\data\tokenizers\spacy_tokenizer.py in __init__(self, language, pos_tags, parse, ner, keep_spacy_tokens, split_on_spaces, start_tokens, end_tokens)
61 end_tokens: Optional[List[str]] = None,
62 ) -> None:
---> 63 self.spacy = get_spacy_model(language, pos_tags, parse, ner)
64 if split_on_spaces:
65 self.spacy.tokenizer = _WhitespaceSpacyTokenizer(self.spacy.vocab)
~\anaconda3\lib\site-packages\allennlp\common\util.py in get_spacy_model(spacy_model_name, pos_tags, parse, ner)
273 disable.append("ner")
274 try:
--> 275 spacy_model = spacy.load(spacy_model_name, disable=disable)
276 except OSError:
277 logger.warning(
~\AppData\Roaming\Python\Python38\site-packages\spacy\__init__.py in load(name, **overrides)
28 if depr_path not in (True, False, None):
29 deprecation_warning(Warnings.W001.format(path=depr_path))
---> 30 return util.load_model(name, **overrides)
31
32
~\AppData\Roaming\Python\Python38\site-packages\spacy\util.py in load_model(name, **overrides)
162 return load_model_from_link(name, **overrides)
163 if is_package(name): # installed as package
--> 164 return load_model_from_package(name, **overrides)
165 if Path(name).exists(): # path to model data directory
166 return load_model_from_path(Path(name), **overrides)
~\AppData\Roaming\Python\Python38\site-packages\spacy\util.py in load_model_from_package(name, **overrides)
183 """Load a model from an installed package."""
184 cls = importlib.import_module(name)
--> 185 return cls.load(**overrides)
186
187
~\anaconda3\lib\site-packages\en_core_web_sm\__init__.py in load(**overrides)
8
9 def load(**overrides):
---> 10 return load_model_from_init_py(__file__, **overrides)
~\AppData\Roaming\Python\Python38\site-packages\spacy\util.py in load_model_from_init_py(init_file, **overrides)
226 if not model_path.exists():
227 raise IOError(Errors.E052.format(path=path2str(data_path)))
--> 228 return load_model_from_path(data_path, meta, **overrides)
229
230
~\AppData\Roaming\Python\Python38\site-packages\spacy\util.py in load_model_from_path(model_path, meta, **overrides)
207 config = meta.get("pipeline_args", {}).get(name, {})
208 factory = factories.get(name, name)
--> 209 component = nlp.create_pipe(factory, config=config)
210 nlp.add_pipe(component, name=name)
211 return nlp.from_disk(model_path, exclude=disable)
~\AppData\Roaming\Python\Python38\site-packages\spacy\language.py in create_pipe(self, name, config)
298 raise KeyError(Errors.E108.format(name=name))
299 else:
--> 300 raise KeyError(Errors.E002.format(name=name))
301 factory = self.factories[name]
302 return factory(self, **config)
KeyError: "[E002] Can't find factory for 'tok2vec'. This usually happens when spaCy calls `nlp.create_pipe` with a component name that's not built in - for example, when constructing the pipeline from a model's meta.json. If you're using a custom component, you can write to `Language.factories['tok2vec']` or remove it from the model meta and add it via `nlp.add_pipe` instead."
I tried to upgrade to spacy-3.1.1, but got the following error:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-1-27de09498ecb> in <module>
----> 1 from transformer_srl import dataset_readers, models, predictors
2
3 predictor = predictors.SrlTransformersPredictor.from_path("D:/srl_bert_base_conll2012.tar.gz", "transformer_srl")
4 predictor.predict(sentence="Did Uriah honestly think he could beat the game in under three hours?")
~\anaconda3\lib\site-packages\transformer_srl\dataset_readers.py in <module>
12 from allennlp.data.tokenizers import Token
13 from allennlp_models.common.ontonotes import Ontonotes, OntonotesSentence
---> 14 from allennlp_models.structured_prediction import SrlReader
15 from conllu import parse_incr
16 from nltk import Tree
~\anaconda3\lib\site-packages\allennlp_models\structured_prediction\__init__.py in <module>
1 # flake8: noqa: F403
----> 2 from allennlp_models.structured_prediction.predictors import *
3 from allennlp_models.structured_prediction.dataset_readers import *
4 from allennlp_models.structured_prediction.metrics import *
5 from allennlp_models.structured_prediction.models import *
~\anaconda3\lib\site-packages\allennlp_models\structured_prediction\predictors\__init__.py in <module>
2 BiaffineDependencyParserPredictor,
3 )
----> 4 from allennlp_models.structured_prediction.predictors.constituency_parser import (
5 ConstituencyParserPredictor,
6 )
~\anaconda3\lib\site-packages\allennlp_models\structured_prediction\predictors\constituency_parser.py in <module>
3 from overrides import overrides
4 from nltk import Tree
----> 5 from spacy.lang.en.tag_map import TAG_MAP
6
7 from allennlp.common.util import JsonDict, sanitize
ModuleNotFoundError: No module named 'spacy.lang.en.tag_map'
I am not sure, how to resolve the issue. Kindly help.
Any help is deeply appreciated. Thanks in advance.
Hi, requirements says you need spaCy 2.3.x. Can you try to update it from 2.2.4 to 2.3? Let me know if this solves your issue.
Thanks for replying. I downgraded spaCy to version 2.3.0, but I am still getting keyerror-
KeyError: "[E002] Can't find factory for 'tok2vec'. This usually happens when spaCy calls `nlp.create_pipe` with a component name that's not built in - for example, when constructing the pipeline from a model's meta.json. If you're using a custom component, you can write to `Language.factories['tok2vec']` or remove it from the model meta and add it via `nlp.add_pipe` instead."
UPDATE:
However, I downloaded the language model again for 2.3.0 using python -m spacy download en_core_web_sm and it resolved the error. I got the following output:
{'verbs': [{'verb': 'Did',
'description': '[do.01: Did] Uriah honestly think he could beat the game in under three hours ?',
'tags': ['B-V',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O'],
'frame': 'do.01',
'frame_score': 0.9999996423721313,
'lemma': 'do'},
{'verb': 'think',
'description': 'Did [ARG0: Uriah] [ARGM-ADV: honestly] [think.01: think] [ARG1: he could beat the game in under three hours] ?',
'tags': ['O',
'B-ARG0',
'B-ARGM-ADV',
'B-V',
'B-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'I-ARG1',
'O'],
'frame': 'think.01',
'frame_score': 1.0,
'lemma': 'think'},
{'verb': 'could',
'description': 'Did Uriah honestly think he [go.04: could] beat the game in under three hours ?',
'tags': ['O',
'O',
'O',
'O',
'O',
'B-V',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O'],
'frame': 'go.04',
'frame_score': 0.10186543315649033,
'lemma': 'could'},
{'verb': 'beat',
'description': 'Did Uriah honestly think [ARG0: he] [ARGM-MOD: could] [beat.03: beat] [ARG1: the game] [ARGM-TMP: in under three hours] ?',
'tags': ['O',
'O',
'O',
'O',
'B-ARG0',
'B-ARGM-MOD',
'B-V',
'B-ARG1',
'I-ARG1',
'B-ARGM-TMP',
'I-ARGM-TMP',
'I-ARGM-TMP',
'I-ARGM-TMP',
'O'],
'frame': 'beat.03',
'frame_score': 0.9999936819076538,
'lemma': 'beat'}],
'words': ['Did',
'Uriah',
'honestly',
'think',
'he',
'could',
'beat',
'the',
'game',
'in',
'under',
'three',
'hours',
'?']}
Glad it worked :)