semvec/pypi-packages huggingface dataset:
conll-iterator 0.0.1
A simple iterator that reads conll files.
License: MIT
Requires Python: >=3.7

CONTENTS OF THIS FILE
---------------------

*   Introduction
*   Setup
*   Getting started
*   Examples

INTRODUCTION
------------

A simple iterator that reads conll and conllu files (https://universaldependencies.org/format.html) without keeping them in memory. It can iterate over words, sentences, or documents.

SETUP
-----
```
pip install git+https://github.com/nicolaCirillo/conll_iterator.git
```

GETTING STARTED
---------------

```
from conll_iterator import ConllIterator
from tqdm import tqdm


sentences = ConllIterator('sample/sample_corpus.conllu', 
                         fields=['form', 'upos'], 
                         mode='sentences', 
                         join_char='/')

for s in tqdm(sentences):
    # do something
```

EXAMPLES
--------------

### Training word2vec

```
from gensim.models import Word2Vec
from tqdm import tqdm
from conll_iterator import ConllIterator

sentences = ConllIterator('sample/sample_corpus.conllu', fields=['lemma', 'upos'], mode='sentences', join_char='/')
w2v_parameters = {'vector_size': 25, 'window': 5, 'min_count': 1, 'sg': 1, 'epochs': 15}
model = Word2Vec(tqdm(sentences), workers=5, **w2v_parameters)
model.save('sample_w2v')
word_vectors = model.wv
similar = list(zip(*word_vectors.most_similar('Pecorino/PROPN')[:10]))[0]
print("Most similar words to Pecorino/PROPN:")
print(similar)
```

### Keyword extraction via tf-idf

```
from itertools import chain
from collections import Counter

docs = ConllIterator('sample/sample_corpus.conllu', fields=['lemma', 'upos'], lower=['lemma'], mode='documents')
doc_tf = list()
df = Counter()
allowed_pos = ['NOUN', 'PROPN','VERB', 'ADJ']
for d in docs:
    tokens = list(chain(*d))
    tokens = [t[0] for t in tokens if t[1] in allowed_pos]
    tf = Counter(tokens)
    df.update(set(tokens))
    doc_tf.append(tf)

doc_keywords = list()
for d in doc_tf:
    doc_tfidf = [(w, d[w]/df[w]) for w in d]
    doc_tfidf = sorted(doc_tfidf, key=lambda x:x[1], reverse=True)
    doc_keywords.append(list(zip(*doc_tfidf[:10]))[0])

for i, k in enumerate(doc_keywords[:20]):
    print('keywords of doc {}:'.format(i+1), '; '.join(k))
```
balderhub-snmpagent 0.0.1b0
balderhub-snmpagent: balderhub package for testing snmp agents
License: MIT
Requires Python: >=3.9
Keywords: test,systemtest,reusable,scenario,junit,balder

# BalderHub Package `balderhub-snmpagent`

This is a simple BalderHub package, that allows to test snmp agents.

Balder is a python test system that allows you to reuse a once written testcode for different but similar platforms/devices/applications. Check it out here.

## Installation

You can install the latest release with pip:

```
python -m pip install balderhub-snmpagent
```

# License

This balderhub package is free and Open-Source

Copyright (c) 2022 Max Stahlschmidt and others

Distributed under the terms of the MIT license
glaciersat 0.0.1a0
glaciersat - monitoring glaciers from space
License: MIT
Requires Python: >=3.6
Keywords: geosciences,glaciers,remote sensing,gis


**Glaciersat is a modular, open-source package for glacier remote sensing**

Glaciersat provides tools to monitor important glacier variables like snow 
distribution on the glaciers, broadband albedo and melt area from different 
platform and sensors. It put a special focus on quantifying uncertainties in 
both input and derived variables.
Links
-----
- HTML documentation: https://glaciersat.readthedocs.org
- Source code: http://github.com/jlandmann/glaciersat