https://github.com/hectorpulido/peque-nlu
Peque-NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts intends, features and information.
https://github.com/hectorpulido/peque-nlu
ai data-science intent intent-classification machine-learning nlp nlu pequesoft text-classification
Last synced: 6 days ago
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Peque-NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts intends, features and information.
- Host: GitHub
- URL: https://github.com/hectorpulido/peque-nlu
- Owner: HectorPulido
- License: mit
- Created: 2023-05-07T03:40:02.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-26T22:12:12.000Z (about 1 year ago)
- Last Synced: 2024-01-26T23:26:12.896Z (about 1 year ago)
- Topics: ai, data-science, intent, intent-classification, machine-learning, nlp, nlu, pequesoft, text-classification
- Language: Python
- Homepage:
- Size: 36.1 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Peque NLU - Natural Language Understanding with Machine Learning
Peque-NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extract intents, features and information.
For example: `quiero conocer el ultimo blogpost de unity`
Result: Timing -> latest, Technology -> unity, Intention -> search
## Table of Contents
- [Features](#features)
- [Use Cases](#use-cases)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Features
- Feature extraction from text
Agnostic algorithm: you can use SGD, MLNN, LLMs, Word2Vec, etc.
- 100% Free and Open source
## Use cases
- Chatbots, to get intention and extract features
- Search engines, get keywords and intention from a semantic info
- Data mining, classifying text and unstructured data without boilerplate
## Getting Started
### Prerequisites
- Python 3.6+
### Installation
>[!WARNING]
> pip installation coming soon
1. Clone this repo
```
git clone [email protected]:HectorPulido/peque-nlu.git
```
2. Install the requirements
```
pip install -r requirements.txt
```
3. Use the library
```py
from peque_nlu.intent_engines import SGDIntentEngine
from peque_nlu.intent_classifiers import ModelIntentClassifier
intent_engine = SGDIntentEngine("spanish")
model = ModelIntentClassifier("spanish", intent_engine)
model.fit(DATASET_PATH)
prediction = model.multiple_predict(
[
"Hola como te encuentras?",
"Quiero aprender sobre lo último de python",
"describeme usando un meme",
]
)
assert len(prediction) == 3
first_prediction = prediction[0]
assert "intent" in first_prediction
assert "probability" in first_prediction
assert "text" in first_prediction
assert "features" not in first_prediction
assert first_prediction["intent"] == "small_talk"
```
## Usage
You need to provide to the algorithm before start, you [can check this](https://github.com/HectorPulido/peque-nlu/blob/main/intents_example.json) as base
```json
{
"intents": {
"small_talk": [
"hola",
...
],
"fun_phrases": [
"eres gracioso",
...
],
"meme": [
"¿conoces algun buen meme?",
...
],
"thanks": [
"gracias",
...
]
},
"entities": {
"technology": [
"python",
...
],
"timing": [
"recient",
...
]
}
}
```
When you have your format ready, you can load and fit your dataset.
```py
intent_engine = SGDIntentEngine("spanish")
model = ModelIntentClassifier("spanish", intent_engine)
model.fit(DATASET_PATH)
```
You can also save and load your models to reduce time and resources.
```py
# Save
saver = PickleSaver()
saver.save(intent_engine, PICKLE_PATH)
# Load
intent_engine_loaded = SGDIntentEngine("spanish")
intent_engine_loaded = saver.load(PICKLE_PATH)
```
Then you can start to predict or extract features from a text
```py
prediction = model.predict("quiero conocer el ultimo blogpost de unity")
```
Response:
```
{
"intent": "search",
"features": [
{
"word": "ultimo",
"entity": "timing",
"similarities": 1
},
{
"word": "otro_ejemplo",
"entity": "otra_entidad",
"similarities": 0.9
}
]
}
```
## Contributing
Your contributions are greatly appreciated! Please follow these steps:
1. Fork the project
2. Create your feature branch `git checkout -b feature/MyFeature`
3. Commit your changes `git commit -m "my cool feature"`
4. Push to the branch `git push origin feature/MyFeature`
5. Open a Pull Request
## License
Every base code made by me is under the MIT license
## Contact
Let's connect 😋
