ERROR: Could not find a version that satisfies the requirement torch==1.8.1 (from rpunct) (from versions: 1.11.0, 1.12.0, 1.12.1, 1.13.0) ERROR: No matching distribution found for torch==1.8.1

I forked this repository (link here) to fix the outdated dependencies and incompatibility with non-CUDA machines. If anyone needs these fixes, feel free to install from the fork:

pip install git+https://github.com/samwaterbury/rpunct.git

Hopefully this repository is updated or another maintainer is assigned. And thanks to the creator @Felflare, this is a useful tool!

First, thanks a lot for providing this package :)

Currently, the requirements.txt, and thus the dependencies in the setup.py are for very specific versions of Pytorch etc. This shouldn't be the case if you want this package to be used as a general library (think of a second package that would do the same but ask for an incompatible version of PyTorch and would prevent any possible installation of the two together). The end user might also be needing a more recent version of PyTorch. Given that PyTorch is almost always backward compatible, and quite stable, I think the requirements for it could be changed from ==1.8.1 to >=1.8.1. I believe the same would be true for the other packages.

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.

using the package in an environment without cuda support causes it to fail. Adding the parameter to shut it off if necessary allows it to function normall.