Added ability to pass additional parameters to simpletransformer ner in RestorePuncts class.

[nbertagnolli]

Thanks for the great library! When running this without a GPU I had problems. I think there is a simple fix. The simple transformer NER model defaults to enabling cuda. This PR allows the user to pass a dictionary of arguments specifically for the simpletransformers NER model. So you can now run the code on a CPU by initializing rpunct like so

rpunct = RestorePuncts(ner_args={"use_cuda": False})

Before this change, when running rpunct examples on the CPU the following error occurs:

from rpunct import RestorePuncts
# The default language is 'english'
rpunct = RestorePuncts()
rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were
a few atoms thick anything thicker would cause the electrons to scatter in ways that could not be disentangled now a team again led by david muller the samuel b eckert
professor of engineering has bested its own record by a factor of two with an electron microscope pixel array detector empad that incorporates even more sophisticated
3d reconstruction algorithms the resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves""")

---

ValueError Traceback (most recent call last)
/var/folders/hx/dhzhl_x51118fm5cd13vzh2h0000gn/T/ipykernel_10548/194907560.py in
1 from rpunct import RestorePuncts
2 # The default language is 'english'
----> 3 rpunct = RestorePuncts()
4 rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
5 by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were

~/repos/rpunct/rpunct/punctuate.py in init(self, wrds_per_pred, ner_args)
19 if ner_args is None:
20 ner_args = {}
---> 21 self.model = NERModel("bert", "felflare/bert-restore-punctuation", labels=self.valid_labels,
22 args={"silent": True, "max_seq_length": 512}, **ner_args)
23

~/repos/transformers/transformer-env/lib/python3.8/site-packages/simpletransformers/ner/ner_model.py in init(self, model_type, model_name, labels, args, use_cuda, cuda_device, onnx_execution_provider, **kwargs)
209 self.device = torch.device(f"cuda:{cuda_device}")
210 else:
--> 211 raise ValueError(
212 "'use_cuda' set to True when cuda is unavailable."
213 "Make sure CUDA is available or set use_cuda=False."

ValueError: 'use_cuda' set to True when cuda is unavailable.Make sure CUDA is available or set use_cuda=False.

[MisterCapi]

@Felflare any chance you could merge it soon? I've encountered the same problem on non-cuda device