semvec/pypi-packages huggingface dataset:
freenlpc 0.2.3
A wrapper for nlpcloud free-tier services with no requests per minute limits.
Keywords: python,natural languge processing,nlp,deep learning,AI,GPT,LLMS,nlpcloud

# freenlpc
a wrapper for nlpcloud free-tier.

# FEATURES
- wrappes all the important nlpcloud free-tier models in one object.
- no rate limit per minute error, it will just keep on trying until it gets the response.
- you can initialize it with more than one API token, if one reached the rate limit it will automatically switch to the other API token.

# AVAILABLE TASKS
- classification
- dialog summary
- headline generation
- entities extraction
- question answering
- semantic similarity
- sentiment/emotions analysis
- summarization
- embeddings
- translation
- language detection
- tokenization and lemmatization
- sentence dependencies

# INSTALLATION
```
pip install freenlpc
```
# USAGE
```
from freenlpc import FreeNlpc

tokens = ["token1", "token2"] # your nlpcloud api token/s
nlpc = FreeNlpc(tokens)

# then use whatever task you want
result = nlpc.sentiment_emotions("i am feelin happy")
```
TGPred 0.1.0
TGPred: Efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.
Requires Python: >=3.6

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# TGPred v.0.1.0 (Python Version)

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Python version of TGPred contains six efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization:

- **HuberNet**: Huber loss function along with Network-based penalty function;
- **HuberLasso**: Huber loss function along with Lasso penalty function;
- **HuberENET**: Huber loss function along with Elastic Net penalty function;
- **MSENet**: Mean square error loss function along with Network-based penalty function;
- **MSELasso**: Mean square error loss function along with Lasso penalty function;
- **MSEENET**: Mean square error loss function along with Elastic Net penalty function;
- **APGD**: The Accelerated Proximal Gradient Descent (APGD) algorithm to solve the above six penalized regression models.

## Functions

Please refer from [Github site](https://github.com/tobefuture/TGPred).