semvec/pypi-packages huggingface dataset:
SCBert 0.2
A small package to do Sentence Clustering with BERT (SCBert)
Keywords: sentence clustering,bert,keyword extraction,sentence embedding,neural networks,flaubert,camembert

# Sentence Clustering with BERT (SCB)

Sentence Clustering with BERT project which aim to use state-of-the-art BERT models to compute vectors for sentences. A few tools are also implemented to explore those vectors and how sentences are related to each others in the latent space. 

### Demonstration 

- **Create vectors from raw data :**

```
#How to transform raw french texts into vectors using BERT model. 
from SCBert.SCBert import Vectorizer

vectorizer = Vectorizer("flaubert")
text_vectors = vectorizer.vectorize(data)
```

- **Explore the embedded space :**
```
#How to explore the relation in your data. 
from SCBert.SCBert import EmbeddingExplorer

ee = EmbeddingExplorer(data,text_vectors)
labels = ee.cluster(k=3)                     #Cluster with k-means 
ee.extract_keywords()                        #Extract keywords using Rake algorithm, then accessible with ee.keywords
ee.explore(color = labels)                   #Generate a plot with PCA of the embedded vectors with colors corresponding to the labels 
```

### Built-in example

There is a built-in example that you can find in the example folder. It comes with it's own data which is the CLS-fr composed of Amazon reviews from different sources (DVD, CD, Livres)

### Installation 

You can either download the zip file or use the Pypi package that you can install with the following command : 

```
> pip install SCBert
```


If you encounter problems during the installation it may be because of the multi-rake dependy with cld2-cffi. I will try to address this later on. To bypass, just follow the instructions : 

```
> export CFLAGS="-Wno-narrowing"
> pip install cld2-cffi
> pip install multi-rake
```
mp3treesim 1.0.6
Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels
Requires Python: >=3.6


# MP3-treesim

Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels.

```
usage: mp3treesim [-h] [-i | -u | -g] [-c CORES] [--labeled-only]
                   [--exclude [EXCLUDE [EXCLUDE ...]]]
                   TREE TREE

MP3 tree similarity measure

positional arguments:
  TREE                  Paths to the trees

optional arguments:
  -h, --help            show this help message and exit
  -i                    Run MP3-treesim in Intersection mode.
  -u                    Run MP3-treesim in Union mode.
  -g                    Run MP3-treesim in Geometric mode.
  -c CORES, --cores CORES
                        Number of cores to be used in computation.
  --labeled-only        Ingore nodes without "label" attribute. The trees will
                        be interpred as partially-label trees.
  --exclude [EXCLUDE [EXCLUDE ...]]
                        String(s) of comma separated labels to exclude from
                        computation. If only one string is provided the labels
                        will be excluded from both trees. If two strings are
                        provided they will be excluded from the respective
                        tree. E.g.: --exclude "A,D,E" will exclude labels from
                        both trees; --exclude "A,B" "C,F" will exclude A,B
                        from Tree 1 and C,F from Tree 2; --exclude "" "C" will
                        exclude and C from Tree 2 and nothing from Tree 1

```

A detailed description of the module is available on our [github repo](https://github.com/AlgoLab/mp3treesim).
booster-pytorch 0.0.2
A lightweight library to ease the training and the debugging of deep neural networks with PyTorch. Data structures and paradigms.

# Booster

A lightweight library to ease the training and the debugging of deep neural networks with PyTorch. Data structures and paradigms.

## Data Structures

### Diagnostic

A two level dictionary structure to store the model diagnostics. Compatible with Tensorboard datastructure.

Example:

```python
from booster import Diagnostic

data = {
'loss' : {'nll' : [45., 58.], 'kl': [22., 18.]},
'info' : {'batch_size' : 16, 'runtime' : 0.01}
}

diagnostic = Diagnostic(data)
```

### Aggregator

A module to compute the running average of the diagnostics.

```python
from booster import Aggregator, Diagnostic

aggregator = Aggregator()
...
aggregator.initialize()
for x in data_loader:
  data = optimization_step(model, data)
  aggregator.update(data)

summmary = aggregator.data # summary is an instance of Diagnostic
summmary = summary.to('cpu')
```

The output is a Diagnostic object and can easily be logged to Tensorboard.

```python
# log to tensorboard
writer = SummaryWriter(log_dir="...")
summary.log(writer, global_step)

```

## Evaluator

The Evaluator computes a forward pass through the model, the loss and additional Diagnostics.

```python
from booster.evaluation import Classification
model = Classifier()
evaluator = Classification(categories=10)

# evaluate model
data = next(iter(loader))
loss, diagnostics, output = evaluator(model, data)

```

## Pipeline: model + evaluator
 
The pipeline fuses the model forward pass with the evaluator and can be wrapped into a custom Dataparallel class that handles the diagnostics.

```python
from booster import Pipeline, DataParallelPipeline

# fuse model + evaluator
pipeline = Pipeline(model, evaluator)

# wrap as DataParallel
parallel_pipeline = DataParallelPipeline(pipeline, device_ids=device_ids)

# evaluate model on multiple devices and gather loss and diagnostics
data = next(iter(loader))
loss, diagnostics, output = parallel_pipeline(data) 
```
gitpy 0.0.5
A Python wrapper for git
Requires Python: >=3.6

# Gitpy
 A Python wrapper for git

This tool can be used for:
* Creating automatic commits in Python
* Speeding up your workflow
* Keeping everything up to date on all devices with the repo

## Setup
`pip install gitpy`

## Sample usage
```python
from gitpy import commitAdd, push

commitAdd("File updates")
push()
```
snakelib 0.0.2
Development tools
Requires Python: >=3.4

[![Build Status](https://travis-ci.org/snake-soft/snakelib.svg?branch=master)](https://travis-ci.org/snake-soft/snakelib)
[![Coverage Status](https://coveralls.io/repos/github/snake-soft/snakelib/badge.svg?branch=master)](https://coveralls.io/github/snake-soft/snakelib?branch=master)

# snake-lib
Library for our projects.