KeyNeg-RS

pip install keyneg-enterprise-rs \
    --index-url https://pypi.grandnasser.com/simple/ \
    --trusted-host pypi.grandnasser.com

pip install keyneg-enterprise-rs[model-export]

# Using the CLI tool
keyneg-export-model --output-dir ./models

from keyneg import KeyNeg, version

# Check version
print(f"KeyNeg-RS version: {version()}")

# Initialize with model paths
kn = KeyNeg("models/model.onnx", "models/tokenizer.json")

# Analyze text
text = "The work environment is toxic and management is incompetent."

# Extract sentiments
sentiments = kn.extract_sentiments(text, top_n=5)
for label, score in sentiments:
    print(f"{label}: {score:.3f}")

# Output:
# hostile work environment: 0.603
# toxic culture: 0.453
# incompetent management: 0.533

sentiments = kn.extract_sentiments(
    doc="The toxic culture and micromanagement is unbearable",
    top_n=5,
    threshold=0.3,
    diversity=0.0
)

for label, score in sentiments:
    print(f"{label}: {score:.3f}")

keywords = kn.extract_keywords(
    doc="Poor communication and lack of transparency from leadership",
    top_n=10,
    threshold=0.25,
    keyphrase_ngram_range=(1, 2),
    use_taxonomy=True,
    diversity=0.0
)

for keyword, score in keywords:
    print(f"{keyword}: {score:.3f}")

result = kn.analyze(
    doc="The management style is terrible and there's no work-life balance.",
    top_n_keywords=10,
    top_n_sentiments=5
)

print(f"Text length: {result['text_length']}")
print(f"Word count: {result['word_count']}")
print(f"Keywords: {result['keywords']}")
print(f"Sentiments: {result['sentiments']}")

{
    'keywords': [('terrible', 0.71), ('management style', 0.65), ...],
    'sentiments': [('poor work-life balance', 0.62), ...],
    'text_length': 72,
    'word_count': 12
}

documents = [
    "I hate my job",
    "The management is terrible",
    "No work-life balance at all",
    "Toxic environment with constant pressure",
]

results = kn.analyze_batch(documents, top_n_keywords=5, top_n_sentiments=3)

for i, result in enumerate(results):
    print(f"Doc {i+1}: {result['sentiments'][0][0]} ({result['sentiments'][0][1]:.2f})")

# Good: Process all at once
results = kn.analyze_batch(all_documents)

# Bad: Process one by one
results = [kn.analyze(doc) for doc in all_documents]

# Good: Create once, use many times
kn = KeyNeg("models/model.onnx", "models/tokenizer.json")
for doc in documents:
    result = kn.analyze(doc)

# Bad: Create new instance for each document
for doc in documents:
    kn = KeyNeg("models/model.onnx", "models/tokenizer.json")
    result = kn.analyze(doc)

# Windows PowerShell
$env:ORT_DYLIB_PATH = "path\to\onnxruntime.dll"

# Or in Python
import os
os.environ["ORT_DYLIB_PATH"] = "path/to/onnxruntime.dll"

from keyneg import check_trial_status, activate_license_key

# Check trial status
status = check_trial_status()
print(f"License: {status['license_type']}")
print(f"Days remaining: {status['days_remaining']}")

# Activate full license
activate_license_key("KN-ENT-xxxxxxxxxxxx-xxxxxxxx", domain="yourcompany.com")