tokenizers | 💥 Fast State-of-the-Art Tokenizers | Natural Language Processing library

I was able to mock class attributes and instance methods when all I wanted to was to mock an instance attribute. I also read many tutorials, which did not help me fully understand what I was doing either.

Eventually, after a desperate google search with a piece of my own code that should not yield any important results (i.e.: mocker.patch.object(Article, summary="abc", create=True)), I came across the best tutorial I found all around the web over the last week, which finally helped me connect the docs.

The final solution for own my question is (docstring includes the tutorial that helped me):

In the new config, you want to use the source option to load the components from the existing pipeline. You would modify the [component] blocks to contain only the source setting and no other settings:

And according to sentence-transformers code: Link

the flax_model.msgpack , rust_model.ot, tf_model.h5 are getting ignored when the it is trying to download.

and these are the files that it downloads :

However, in the same code line, I was getting an error with sentencepiece.

Wrapping str() around both parameters yielded the same Traceback.

The library previously named LPOT has been renamed to Intel Neural Compressor (INC), which resulted in a change in the name of our subpackage from lpot to neural_compressor. The correct way to import would now be from optimum.intel.neural_compressor.quantization import IncQuantizerForSequenceClassification Concerning the graphcore subpackage, you need to install it first with pip install optimum[graphcore] Furthermore you'll need to have access to an IPU in order to use it.

Solution