semvec/pypi-packages huggingface dataset:
OneOnOne 0.7116
A package for pre-trained image classification and context-decider for question-answering chatbots.
Keywords: python,image-classification,active-learning-sampling,question-answering,pre-trained models,tiny-image-net,speech-recognition,cifar10

Your one-stop destination to utilize image-classification models with just one line of code. A library meant to simplify your life by providing you with pre-trained models like ResNet50, EfficientNetVB6, VGG19, etc. You can simply opt for training your own models from scratch by just tweaking a few values. If you want to try popular active-learning sampling methods on image classification, no need to worry! This library has got you covered. Along with that for simple-bridging and basic into NLP, we have context-deciders, HTML parsers and simple chatbot object classes, to create an interface similar to Google Lens. You input an image or item that you are curious about and you can ask one-on-one questions from the chatbot. This is made possible by using the tiny imagenet dataset. This library is being actively updated and new features are being added frequently. New datasets and pre-trained models will be updated soon. Feel free to share your feedback! I would really appreciate it!
iM-Seeker 1.0.14
iM-Seeker is commandline software designed to predict DNA i-Motif folding status and folding strength.

# iM-Seeker

iM-Seeker is commandline software designed to predict DNA i-Motif folding status and folding strength.
Details can be found at https://github.com/YANGB1/iM-Seeker

# Installation and Usage
The dependency packages can be installed by:
``` 
pip3 install -r requirements.txt
``` 
iM-Seeker can be installed by:
``` 
pip3 install iM-Seeker
```
Alternatively, the python script 'iM-Seeker.py' can be downloaded directly from Github. The stored directory can be added to the ‘PATH’ environmental variable or the scripts with full path can be run alternatively using command like: 
``` 
python3 iM-Seeker.py -h
``` 
**Please pay attention !!!!!! The program needs two models 'pickle_model_classification.pkl' and 'pickle_model_regression.pkl' which are required as the input files of the software. Please find all the files at https://figshare.com/s/e4e72e2e8ceaa0a4fbd6, where all these files can be downloaded directly.**
  
After intalled the package with 'pip',the help page can be checked by following command:
``` 
iM-Seeker.py -h
``` 
Parameters can be configured according to the user's own needs. Here is an example:
``` 
iM-Seeker.py --sequence input.fa --classification_model pickle_model_classification.pkl --regression_model pickle_model_regression.pkl --overlapped 2 --greedy 2 --stem_short 3 --stem_long 5 --loop1_short 1 --loop1_long 12 --loop2_short 1 --loop2_long 12 --loop3_short 1 --loop3_long 12 --representative_conformation 2 --output_folder output_path
``` 

# Input and output
The input sequences should be in fasta formation, for instance:

\>test1

CCCTCCCCCTCCCCTCCCTCCCCCCCCTCCCCTCCCTCCCTCCCCCCCCTCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCCCCCCCCCTCCTCCCCTCCCCCTCCCCTCCCTCCCTCC

\>test2

CCCCCTCCCCCTCCCCCTCCCCCTCCCCC

\>test3

CCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC

\>test4

CCCCGACCCCAACCCCTCCCCCAACCCCTCCCC

The output files are stored in the pre-set output folder.

If --representative_conformation is set as 1, 'iM-seeker_result_average_conformation.txt' includes conformation A of pre-set iM structures. 

If --representative_conformation is set as 2, 'iM-seeker_result_side_shorter_conformation.txt' includes conformation B of pre-set iM structures. 

The prediction result is kept in 'iM-seeker_final_prediction.txt'.

"0" of folding status means unfolded while "1" means folded. Folding strength is a continuous number.