Fix `model_name` check for only tokenizer libraries which need `model_name`
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# What does this PR do ?

For the function `get_nmt_tokenizer`, the user may use libraries which do not require `model_name` (e.g. `sentencepiece`, which is the default library), thus would not pass in `model_name` to the function. 

The current implementation throws a `ValueError` for such cases.

This PR fixes the `model_name` check for only libraries which need them.


# Usage
From step 4, option 2 in this nvidia [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html)

```
python <NeMo_ROOT_FOLDER>/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \
--input=train_data.jsonl \
--json-keys=text \
--tokenizer-library=sentencepiece \
--tokenizer-model=spm_32k_wiki.model \
--output-prefix=gpt_training_data \
--append-eod \
--workers=32
```

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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

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## Who can review?
@MaximumEntropy, @ericharper 
Anyone in the community is free to review the PR once the checks have passed. 
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# Additional Information
* Related to # (issue)
