Traceback (most recent call last):


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/click/core.py", line 782, in main

rv = self.invoke(ctx)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/click/core.py", line 1259, in invoke

return _process_result(sub_ctx.command.invoke(sub_ctx))


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/click/core.py", line 1066, in invoke

return ctx.invoke(self.callback, **ctx.params)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/click/core.py", line 610, in invoke

return callback(*args, **kwargs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/typer/main.py", line 497, in wrapper

return callback(**use_params)
# type: ignore


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/cli/train.py", line 59, in train_cli

train(nlp, output_path, use_gpu=use_gpu, stdout=sys.stdout, stderr=sys.stderr)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/training/loop.py", line 114, in train

raise e


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/training/loop.py", line 98, in train

for batch, info, is_best_checkpoint in training_step_iterator:


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/training/loop.py", line 212, in train_while_improving

score, other_scores = evaluate()


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/training/loop.py", line 264, in evaluate

scores = nlp.evaluate(dev_corpus(nlp))


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/language.py", line 1354, in evaluate

examples,


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy/util.py", line 1478, in _pipe

yield from proc.pipe(docs, **kwargs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy_transformers/pipeline_component.py", line 202, in pipe

self.set_annotations(subbatch, self.predict(subbatch))


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy_transformers/pipeline_component.py", line 218, in predict

activations = self.model.predict(docs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/thinc/model.py", line 312, in predict

return self._func(self, X, is_train=False)[0]


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/spacy_transformers/layers/transformer_model.py", line 142, in forward

tensors, bp_tensors = transformer(wordpieces, is_train)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/thinc/model.py", line 288, in __call__

return self._func(self, X, is_train=is_train)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/thinc/layers/pytorchwrapper.py", line 80, in forward

Ytorch, torch_backprop = model.shims[0](Xtorch, is_train)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/thinc/shims/pytorch.py", line 29, in __call__

return self.predict(inputs), lambda a: ...


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/thinc/shims/pytorch.py", line 38, in predict

outputs = self._model(*inputs.args, **inputs.kwargs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl

result = self.forward(*input, **kwargs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/transformers/modeling_bert.py", line 838, in forward

input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl

result = self.forward(*input, **kwargs)


 "/home/nenashevas/.local/share/virtualenvs/bert-clauses-classifier-t7CyBQ2n/lib/python3.7/site-packages/transformers/modeling_bert.py", line 201, in forward

embeddings = inputs_embeds + position_embeddings + token_type_embeddings
RuntimeError: The size of tensor a (588) must match the size of tensor b (512) at non-singleton dimension 1


Traceback (most recent call last):


 "<string>", line 1, in <module>


 "/tmp/pip-build-EawBUu/hexbytes/setup.py", line 67, in <module>

'Programming Language :: Python :: Implementation :: PyPy',


 "/home/rsp/catalyst-venv/local/lib/python2.7/site-packages/setuptools/__init__.py", line 129, in setup

return distutils.core.setup(**attrs)


 "/usr/lib/python2.7/distutils/core.py", line 111, in setup

_setup_distribution = dist = klass(attrs)


 "/home/rsp/catalyst-venv/local/lib/python2.7/site-packages/setuptools/dist.py", line 333, in __init__

_Distribution.__init__(self, attrs)


 "/usr/lib/python2.7/distutils/dist.py", line 287, in __init__

self.finalize_options()


 "/home/rsp/catalyst-venv/local/lib/python2.7/site-packages/setuptools/dist.py", line 476, in finalize_options

ep.load()(self, ep.name, value)


 "/tmp/pip-build-EawBUu/hexbytes/.eggs/setuptools_markdown-0.2-py2.7.egg/setuptools_markdown.py", line 22, in long_description_markdown_filename

output = pypandoc.convert(markdown_filename, 'rst')


 "/tmp/pip-build-EawBUu/hexbytes/.eggs/pypandoc-1.4-py2.7.egg/pypandoc/__init__.py", line 66, in convert

raise RuntimeError("Format missing, but need one (identified source as text as no "

RuntimeError: Format missing, but need one (identified source as text as no file with that name was found).
