Metadata-Version: 2.1
Name: classy-fire
Version: 0.1.4
Summary: Classy-fire is multiclass text classification approach leveraging OpenAI LLM model APIs optimally using clever parameter tuning and prompting.
Author-email: Shay Ben-Elazar <shbenela@microsoft.com>
License:     MIT License
        
            Classy-Fire
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Project-URL: Homepage, https://github.com/microsoft/classy-fire
Keywords: llm,classification,machine-learning
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# 🤵🔥 Classy-Fire 🔥🤵
Classy-fire is a pretrained multiclass text classification approach that leverages Azure OpenAI's LLM APIs using clever parameter tuning and prompting for classification.

# Start here

## Installation
```
pip install classy-fire
```
## Usage example

```python
from classy_fire import LLMClassifier
classifier = LLMClassifier(["Banana", "Watermelon", "Apple", "Grape"])

result = classifier("Has an elongated shape")
print(result)
>>> ('Banana', 0)
```

## The premise behind classy-fire
In Classy-fire, we instruct the LLM to provide the most likely classification for an input string to a set of predetermined classes (also strings).
Formally, given a string instance $x_i$ and a set of $k$ classes provided as strings, $C=(C_1, ..., C_k)$, classy-fire determines 

$argmax_j Pr[x_i \in C_j | C, \Theta]$

Where $\Theta$ is the parameters (knowledge of the world) of the language model.

* Classy-fire does this efficiently by mapping class strings to single tokens and providing a strong prior probability for these tokens. We instruct the model to generate a single token response, which allows for optimized inference runtime.
* Classy-fire does this deterministically and with less sensitivity to confabulation (hallucination) by setting the model temperature to 0, thereby guaranteeing the returned response is the argmax of the model posterior probability.

## LLMClassifier optional parameters
LLMClassifier can be initialized with added parameters that can help instruct and ground it to the classification task at hand.
* task_description = "Ability to provide additional context on the classification options and overall context on inputs"
* few_shot_examples = "Ability to provide instances of inputs and corresponding expected output values as a string"


## Quality of results
We ran an experiment to classify a sample of 100 tweets from the [tweet_eval dataset](https://huggingface.co/datasets/tweet_eval/viewer/emotion/train).
The results [appear to beat the SOTA](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=tweet_eval&only_verified=0&task=-any-&config=emotion&split=test&metric=f1).
             
|              | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| anger        | 0.97      | 0.81   | 0.88     | 42      |
| joy          | 0.74      | 0.92   | 0.82     | 25      |
| optimism     | 1.00      | 0.25   | 0.40     | 8       |
| sadness      | 0.75      | 1.00   | 0.86     | 21      |
|              |           |        |          |         |
| accuracy     |           |        | 0.83     | 96      |
| macro avg    | 0.87      | 0.74   | 0.74     | 96      |
| weighted avg | 0.87      | 0.83   | 0.82     | 96      |

See evaluate.ipynb for a sample showcasing the power of this method on a public text classification dataset.



## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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trademarks or logos is subject to and must follow 
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
 
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