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https://github.com/centre-for-humanities-computing/stormtrooper

Zero/few shot learning components for scikit-learn pipelines with LLMs and transformers.
https://github.com/centre-for-humanities-computing/stormtrooper

chatgpt few-shot-learning gpt-4 large-language-models llm scikit-learn transformer transformers zero-shot-learning

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Zero/few shot learning components for scikit-learn pipelines with LLMs and transformers.

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README 

        



# stormtrooper






Zero/few shot learning components for scikit-learn pipelines with large-language models and transformers.



[Documentation](https://centre-for-humanities-computing.github.io/stormtrooper/)



## New in 1.0.0



### `Trooper`

The brand new `Trooper` interface allows you not to have to specify what model type you wish to use.

Stormtrooper will automatically detect the model type from the specified name.



```python

from stormtrooper import Trooper



# This loads a setfit model

model = Trooper("all-MiniLM-L6-v2")



# This loads an OpenAI model

model = Trooper("gpt-4")



# This loads a Text2Text model

model = Trooper("google/flan-t5-base")

```



### Unified zero and few-shot classification



You no longer have to specify whether a model should be a few or a zero-shot classifier when initialising it.

If you do not pass any training examples, it will be automatically assumed that the model should be zero-shot.



```python

# This is a zero-shot model

model.fit(None, ["dog", "cat"])



# This is a few-shot model

model.fit(["he was a good boy", "just lay down on my laptop"], ["dog", "cat"])



```

## Model types



You can use all sorts of transformer models for few and zero-shot classification in Stormtrooper.



1. Instruction fine-tuned generative models, e.g. `Trooper("HuggingFaceH4/zephyr-7b-beta")`

2. Encoder models with SetFit, e.g. `Trooper("all-MiniLM-L6-v2")`

3. Text2Text models e.g. `Trooper("google/flan-t5-base")`

4. OpenAI models e.g. `Trooper("gpt-4")`

5. NLI models e.g. `Trooper("facebook/bart-large-mnli")`



## Example usage



Find more in our [docs](https://centre-for-humanities-computing.github.io/stormtrooper/).



```bash

pip install stormtrooper

```



```python

from stormtrooper import Trooper



class_labels = ["atheism/christianity", "astronomy/space"]

example_texts = [

    "God came down to earth to save us.",

    "A new nebula was recently discovered in the proximity of the Oort cloud."

]

new_texts = ["God bless the reailway workers", "The frigate is ready to launch from the spaceport"]



# Zero-shot classification

model = Trooper("google/flan-t5-base")

model.fit(None, class_labels)

model.predict(new_texts)

# ["atheism/christianity", "astronomy/space"]



# Few-shot classification

model = Trooper("google/flan-t5-base")

model.fit(example_texts, class_labels)

model.predict(new_texts)

# ["atheism/christianity", "astronomy/space"]

```



### Fuzzy Matching



Generative and text2text models by default will fuzzy match results to the closest class label, you can disable this behavior

by specifying `fuzzy_match=False`.



If you want fuzzy matching speedup, you should install `python-Levenshtein`.



### Inference on GPU



From version 0.2.2 you can run models on GPU.

You can specify the device when initializing a model:



```python

classifier = Trooper("all-MiniLM-L6-v2", device="cuda:0")

```



### Inference on multiple GPUs



You can run a model on multiple devices in order of device priority `GPU -> CPU + Ram -> Disk` and on multiple devices by using the `device_map` argument.

Note that this only works with text2text and generative models.



```

model = Trooper("HuggingFaceH4/zephyr-7b-beta", device_map="auto")

```