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https://github.com/davidberenstein1957/classy-classification
This repository contains an easy and intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.
https://github.com/davidberenstein1957/classy-classification
few-shot-classifcation hacktoberfest machine-learning natural-language-processing nlp nlu sentence-transformers spacy text-classification
Last synced: 4 days ago
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This repository contains an easy and intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.
- Host: GitHub
- URL: https://github.com/davidberenstein1957/classy-classification
- Owner: davidberenstein1957
- License: mit
- Created: 2022-02-21T11:02:17.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-27T09:16:40.000Z (27 days ago)
- Last Synced: 2024-12-13T19:09:13.845Z (11 days ago)
- Topics: few-shot-classifcation, hacktoberfest, machine-learning, natural-language-processing, nlp, nlu, sentence-transformers, spacy, text-classification
- Language: Python
- Homepage:
- Size: 605 KB
- Stars: 211
- Watchers: 6
- Forks: 15
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Classy Classification
Have you ever 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).
[](https://github.com/pandora-intelligence/classy-classification/releases)
[](https://pypi.org/project/classy-classification/)
[](https://pypi.org/project/classy-classification/)
[](https://github.com/ambv/black)
# Install
``` pip install classy-classification```
## SetFit support
I got a lot of requests for SetFit support, but I decided to create a [separate package](https://github.com/davidberenstein1957/spacy-setfit) for this. Feel free to check it out. ❤️
# Quickstart
## SpaCy embeddings
```python
import spacy
# or import standalone
# 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."]
}
nlp = spacy.load("en_core_web_trf")
nlp.add_pipe(
"classy_classification",
config={
"data": data,
"model": "spacy"
}
)
print(nlp("I am looking for kitchen appliances.")._.cats)
# Output:
#
# [{"furniture" : 0.21}, {"kitchen": 0.79}]
```
### Sentence level classification
```python
import spacy
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.add_pipe(
"classy_classification",
config={
"data": data,
"model": "spacy",
"include_sent": True
}
)
print(nlp("I am looking for kitchen appliances. And I love doing so.").sents[0]._.cats)
# Output:
#
# [[{"furniture" : 0.21}, {"kitchen": 0.79}]
```
### Define random seed and verbosity
```python
nlp.add_pipe(
"classy_classification",
config={
"data": data,
"verbose": True,
"config": {"seed": 42}
}
)
```
### Multi-label classification
Sometimes multiple labels are necessary to fully describe the contents of a text. In that case, we want to make use of the **multi-label** implementation, here the sum of label scores is not limited to 1. Just pass the same training data to multiple keys.
```python
import spacy
data = {
"furniture": ["This text is about chairs.",
"Couches, benches and televisions.",
"I really need to get a new sofa.",
"We have a new dinner table.",
"There also exist things like fridges.",
"I hope to be getting a new stove today.",
"Do you also have some ovens.",
"We have a new dinner table."],
"kitchen": ["There also exist things like fridges.",
"I hope to be getting a new stove today.",
"Do you also have some ovens.",
"We have a new dinner table.",
"There also exist things like fridges.",
"I hope to be getting a new stove today.",
"Do you also have some ovens.",
"We have a new dinner table."]
}
nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
"classy_classification",
config={
"data": data,
"model": "spacy",
"multi_label": True,
}
)
print(nlp("I am looking for furniture and kitchen equipment.")._.cats)
# Output:
#
# [{"furniture": 0.92}, {"kitchen": 0.91}]
```
### Outlier detection
Sometimes it is worth to be able to do outlier detection or binary classification. This can either be approached using
a binary training dataset, however, I have also implemented support for a `OneClassSVM` for [outlier detection using a single label](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html). Not that this method does not return probabilities, but that the data is formatted like label-score value pair to ensure uniformity.
Approach 1:
```python
import spacy
data_binary = {
"inlier": ["This text is about chairs.",
"Couches, benches and televisions.",
"I really need to get a new sofa."],
"outlier": ["Text about kitchen equipment",
"This text is about politics",
"Comments about AI and stuff."]
}
nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
"classy_classification",
config={
"data": data_binary,
}
)
print(nlp("This text is a random text")._.cats)
# Output:
#
# [{'inlier': 0.2926672385488411, 'outlier': 0.707332761451159}]
```
Approach 2:
```python
import spacy
data_singular = {
"furniture": ["This text is about chairs.",
"Couches, benches and televisions.",
"I really need to get a new sofa.",
"We have a new dinner table."]
}
nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
"classy_classification",
config={
"data": data_singular,
}
)
print(nlp("This text is a random text")._.cats)
# Output:
#
# [{'furniture': 0, 'not_furniture': 1}]
```
## Sentence-transfomer embeddings
```python
import spacy
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(
"classy_classification",
config={
"data": data,
"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"device": "gpu"
}
)
print(nlp("I am looking for kitchen appliances.")._.cats)
# Output:
#
# [{"furniture": 0.21}, {"kitchen": 0.79}]
```
## Hugginface zero-shot classifiers
```python
import spacy
data = ["furniture", "kitchen"]
nlp = spacy.blank("en")
nlp.add_pipe(
"classy_classification",
config={
"data": data,
"model": "typeform/distilbert-base-uncased-mnli",
"cat_type": "zero",
"device": "gpu"
}
)
print(nlp("I am looking for kitchen appliances.")._.cats)
# Output:
#
# [{"furniture": 0.21}, {"kitchen": 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
[](https://www.buymeacoffee.com/98kf2552674)
# Standalone usage without spaCy
```python
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_classification_model(
config={
"C": [1, 2, 5, 10, 20, 100],
"kernel": ["linear"],
"max_cross_validation_folds": 5
}
)
classifier("I am looking for kitchen appliances.")
```
## Save and load models
```python
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)
with open("./classifier.pkl", "wb") as f:
pickle.dump(classifier, f)
f = open("./classifier.pkl", "rb")
classifier = pickle.load(f)
classifier("I am looking for kitchen appliances.")
```