Metadata-Version: 2.1
Name: classy-classification
Version: 0.3.5
Summary: Have you every struggled with needing a Spacy TextCategorizer but didn't have the time to train one from scratch? Classy Classification is the way to go! For few-shot classification using sentence-transformers or spaCy models, provide a dictionary with labels and examples, or just provide a list of labels for zero shot-classification with Hugginface zero-shot classifiers. 
Home-page: https://github.com/davidberenstein1957/classy-classification
License: MIT
Keywords: spacy,rasa,few-shot classification,nlu,sentence-transformers
Author: David Berenstein
Author-email: david.berenstein@pandoraintelligence.com
Requires-Python: >=3.7,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Dist: scikit-learn (>=1.0,<2.0)
Requires-Dist: sentence-transformers (>=2.0,<3.0)
Requires-Dist: spacy[transformers] (>=3.0,<4.0)
Project-URL: Documentation, https://github.com/davidberenstein1957/classy-classification
Project-URL: Repository, https://github.com/davidberenstein1957/classy-classification
Description-Content-Type: text/markdown

# Classy Classification
Have you every struggled with needing a [Spacy TextCategorizer](https://spacy.io/api/textcategorizer) but didn't have the time to train one from scratch? Classy Classification is the way to go! For few-shot classification using [sentence-transformers](https://github.com/UKPLab/sentence-transformers) or [spaCy models](https://spacy.io/usage/models), provide a dictionary with labels and examples, or just provide a list of labels for zero shot-classification with [Hugginface zero-shot classifiers](https://huggingface.co/models?pipeline_tag=zero-shot-classification). 
# Install
``` pip install classy-classification```
# Quickstart
## SpaCy embeddings
```
import spacy
import classy_classification

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "spacy"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]
```
## Sentence-transfomer embeddings
```
import spacy
import classy_classification

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

nlp = spacy.blank("en")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]
```
## Hugginface zero-shot classifiers
```
import spacy
import classy_classification

data = ["furniture", "kitchen"]

nlp = spacy.blank("en")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "facebook/bart-large-mnli",
        "cat_type": "zero"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]
```
# Credits
## Inspiration Drawn From
[Huggingface](https://huggingface.co/) does offer some nice models for few/zero-shot classification, but these are not tailored to multi-lingual approaches. Rasa NLU has [a nice approach](https://rasa.com/blog/rasa-nlu-in-depth-part-1-intent-classification/) for this, but its too embedded in their codebase for easy usage outside of Rasa/chatbots. Additionally, it made sense to integrate [sentence-transformers](https://github.com/UKPLab/sentence-transformers) and [Hugginface zero-shot](https://huggingface.co/models?pipeline_tag=zero-shot-classification), instead of default [word embeddings](https://arxiv.org/abs/1301.3781). Finally, I decided to integrate with Spacy, since training a custom [Spacy TextCategorizer](https://spacy.io/api/textcategorizer) seems like a lot of hassle if you want something quick and dirty. 

- [Scikit-learn](https://github.com/scikit-learn/scikit-learn)
- [Rasa NLU](https://github.com/RasaHQ/rasa) 
- [Sentence Transformers](https://github.com/UKPLab/sentence-transformers)
- [Spacy](https://github.com/explosion/spaCy)

## Or buy me a coffee
[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/98kf2552674)


# Standalone usage without spaCy

```
from classy_classification import classyClassifier

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

classifier = classyClassifier(data=data)
classifier("I am looking for kitchen appliances.")
classifier.pipe(["I am looking for kitchen appliances."])

# overwrite training data
classifier.set_training_data(data=data)
classifier("I am looking for kitchen appliances.")

# overwrite [embedding model](https://www.sbert.net/docs/pretrained_models.html)
classifier.set_embedding_model(model="paraphrase-MiniLM-L3-v2")
classifier("I am looking for kitchen appliances.")

# overwrite SVC config
classifier.set_svc(
    config={                              
        "C": [1, 2, 5, 10, 20, 100],
        "kernels": ["linear"],                              
        "max_cross_validation_folds": 5
    }
)
classifier("I am looking for kitchen appliances.")
```

# Todo

[ ] look into a way to integrate spacy trf models.

[ ] multiple clasifications datasets for a single input e.g. emotions and topic.

