Loading problem for the model fr_dep_news_trf

[ludovicbockensia]

I got this error in loading the model fr_dep_news_trf with :

nlp = spacy.load("fr_dep_news_trf")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
C:\LOCAL\TEMP/ipykernel_20836/768117964.py in <module>
----> 1 nlp = spacy.load("fr_dep_news_trf")

~\Anaconda3\lib\site-packages\spacy\__init__.py in load(name, vocab, disable, exclude, config)
     49     RETURNS (Language): The loaded nlp object.
     50     """
---> 51     return util.load_model(
     52         name, vocab=vocab, disable=disable, exclude=exclude, config=config
     53     )

~\Anaconda3\lib\site-packages\spacy\util.py in load_model(name, vocab, disable, exclude, config)
    418             return get_lang_class(name.replace("blank:", ""))()
    419         if is_package(name):  # installed as package
--> 420             return load_model_from_package(name, **kwargs)  # type: ignore[arg-type]
    421         if Path(name).exists():  # path to model data directory
    422             return load_model_from_path(Path(name), **kwargs)  # type: ignore[arg-type]

~\Anaconda3\lib\site-packages\spacy\util.py in load_model_from_package(name, vocab, disable, exclude, config)
    451     """
    452     cls = importlib.import_module(name)
--> 453     return cls.load(vocab=vocab, disable=disable, exclude=exclude, config=config)  # type: ignore[attr-defined]
    454 
    455 

~\Anaconda3\lib\site-packages\fr_dep_news_trf\__init__.py in load(**overrides)
      8 
      9 def load(**overrides):
---> 10     return load_model_from_init_py(__file__, **overrides)

~\Anaconda3\lib\site-packages\spacy\util.py in load_model_from_init_py(init_file, vocab, disable, exclude, config)
    613     if not model_path.exists():
    614         raise IOError(Errors.E052.format(path=data_path))
--> 615     return load_model_from_path(
    616         data_path,
    617         vocab=vocab,

~\Anaconda3\lib\site-packages\spacy\util.py in load_model_from_path(model_path, meta, vocab, disable, exclude, config)
    486     overrides = dict_to_dot(config)
    487     config = load_config(config_path, overrides=overrides)
--> 488     nlp = load_model_from_config(config, vocab=vocab, disable=disable, exclude=exclude)
    489     return nlp.from_disk(model_path, exclude=exclude, overrides=overrides)
    490 

~\Anaconda3\lib\site-packages\spacy\util.py in load_model_from_config(config, vocab, disable, exclude, auto_fill, validate)
    523     # registry, including custom subclasses provided via entry points
    524     lang_cls = get_lang_class(nlp_config["lang"])
--> 525     nlp = lang_cls.from_config(
    526         config,
    527         vocab=vocab,

~\Anaconda3\lib\site-packages\spacy\language.py in from_config(cls, config, vocab, disable, exclude, meta, auto_fill, validate)
   1780                     # The pipe name (key in the config) here is the unique name
   1781                     # of the component, not necessarily the factory
-> 1782                     nlp.add_pipe(
   1783                         factory,
   1784                         name=pipe_name,

~\Anaconda3\lib\site-packages\spacy\language.py in add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
    790                     lang_code=self.lang,
    791                 )
--> 792             pipe_component = self.create_pipe(
    793                 factory_name,
    794                 name=name,

~\Anaconda3\lib\site-packages\spacy\language.py in create_pipe(self, factory_name, name, config, raw_config, validate)
    672         # We're calling the internal _fill here to avoid constructing the
    673         # registered functions twice
--> 674         resolved = registry.resolve(cfg, validate=validate)
    675         filled = registry.fill({"cfg": cfg[factory_name]}, validate=validate)["cfg"]
    676         filled = Config(filled)

~\Anaconda3\lib\site-packages\thinc\config.py in resolve(cls, config, schema, overrides, validate)
    727         validate: bool = True,
    728     ) -> Dict[str, Any]:
--> 729         resolved, _ = cls._make(
    730             config, schema=schema, overrides=overrides, validate=validate, resolve=True
    731         )

~\Anaconda3\lib\site-packages\thinc\config.py in _make(cls, config, schema, overrides, resolve, validate)
    776         if not is_interpolated:
    777             config = Config(orig_config).interpolate()
--> 778         filled, _, resolved = cls._fill(
    779             config, schema, validate=validate, overrides=overrides, resolve=resolve
    780         )

~\Anaconda3\lib\site-packages\thinc\config.py in _fill(cls, config, schema, validate, resolve, parent, overrides)
    831                     schema.__fields__[key] = copy_model_field(field, Any)
    832                 promise_schema = cls.make_promise_schema(value, resolve=resolve)
--> 833                 filled[key], validation[v_key], final[key] = cls._fill(
    834                     value,
    835                     promise_schema,

~\Anaconda3\lib\site-packages\thinc\config.py in _fill(cls, config, schema, validate, resolve, parent, overrides)
    848                     # We don't want to try/except this and raise our own error
    849                     # here, because we want the traceback if the function fails.
--> 850                     getter_result = getter(*args, **kwargs)
    851                 else:
    852                     # We're not resolving and calling the function, so replace

~\Anaconda3\lib\site-packages\spacy_transformers\architectures.py in create_TransformerModel_v3(name, get_spans, tokenizer_config, transformer_config, mixed_precision, grad_scaler_config)
    232         `growth_interval` steps.
    233     """
--> 234     model = TransformerModel(
    235         name,
    236         get_spans,

~\Anaconda3\lib\site-packages\spacy_transformers\layers\transformer_model.py in __init__(self, name, get_spans, tokenizer_config, transformer_config, mixed_precision, grad_scaler_config)
     43         """
     44         hf_model = HFObjects(None, None, None, tokenizer_config, transformer_config)
---> 45         wrapper = HFWrapper(
     46             hf_model,
     47             convert_inputs=_convert_transformer_inputs,

~\Anaconda3\lib\site-packages\spacy_transformers\layers\hf_wrapper.py in HFWrapper(hf_model, convert_inputs, convert_outputs, mixed_precision, grad_scaler_config)
     47         attrs={"convert_inputs": convert_inputs, "convert_outputs": convert_outputs},
     48         shims=[
---> 49             HFShim(
     50                 hf_model,
     51                 mixed_precision=mixed_precision,

~\Anaconda3\lib\site-packages\spacy_transformers\layers\hf_shim.py in __init__(self, model, config, optimizer, mixed_precision, grad_scaler_config)
     38             optimizer,
     39             mixed_precision,
---> 40             grad_scaler=PyTorchGradScaler(**grad_scaler_config),
     41         )
     42 

~\Anaconda3\lib\site-packages\thinc\shims\pytorch_grad_scaler.py in __init__(self, enabled, init_scale, backoff_factor, growth_factor, growth_interval)
     52         self._growth_interval = growth_interval
     53 
---> 54         self._found_inf = torch.full((1,), 0.0)
     55         self._growth_tracker = torch.full((1,), 0, dtype=torch.int)
     56         self._scale = torch.full((1,), init_scale)

NameError: name 'torch' is not defined

[polm]

It seems you do not have Torch installed. How did you install the trf pipeline? I understand that sometimes it helps to install Torch separately first.