Comments (2)
the following dependency issue was raised when trying to use more recent version of transformers
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed PyYAML-5.4.1 Questgen-1.0.0 boto3-1.14.40 botocore-1.17.63 docutils-0.15.2 flashtext-2.7 future-0.18.2 jmespath-0.10.0 joblib-0.14.1 networkx-2.4 python-dateutil-2.8.1 pytorch-lightning-0.8.1 pytz-2020.1 s3transfer-0.3.7 sacremoses-0.0.46 sense2vec-1.0.3 sentencepiece-0.1.96 strsim-0.0.3 tokenizers-0.8.1rc1 transformers-3.0.2 unidecode-1.1.1
WARNING: The following packages were previously imported in this runtime:
[dateutil,pytz]
You must restart the runtime in order to use newly installed versions.
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i have resolved this matter on my fork, however my changes also includes PEP standard adherance therfore i will not raise a PR on this matter because it has become incompatible.
My advice to you as someone with 30 years of industry experience you should take sometime to cleanup your code before making announcements, you had outdated dependencies and unused libraries like boto3 in your code. this shows that you have not taken the time to properly present your work to the python community.
Thanks again for your wonderful work,
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Related Issues (20)
- What is diversity penalty?
- Explainability / Model Interrogation?
- No matching distribution for Spacy==2.2.4 HOT 1
- Library is giving error for thinc...and eventually not getting loaded HOT 1
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- May function MCQs_available is not necessary while the get_keywords being called by predict_shortq?
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- ModuleNotFoundError: No module named 'Questgen'` HOT 7
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- Value error HOT 3
- while executing this line - !pip install --upgrade git+https://github.com/ramsrigouthamg/Questgen.ai HOT 10
- Always generating same questions HOT 1
- tokenizer with `model_max_length` HOT 2
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