pip install refuel-autolabel

https://docs.refuel.ai/

Access to large, clean and diverse labeled datasets is a critical component for any machine learning effort to be successful. State-of-the-art LLMs like GPT-4 are able to automatically label data with high accuracy, and at a fraction of the cost and time compared to manual labeling.

Autolabel is a Python library to label, clean and enrich text datasets with any Large Language Models (LLM) of your choice.

You can access RefuelLLM, our recently announced LLM purpose built for data labeling, through Autolabel (Read more about it in this blog post). RefuelLLM is a Llama-v2-13b base model, instruction tuned on over 2500 unique (5.24B tokens) labeling tasks spanning categories such as classification, entity resolution, matching, reading comprehension and information extraction. You can experiment with the model in the playground here.

You can request access to RefuelLLM here. Read the docs about using RefuelLLM in autolabel here.

Autolabel provides a simple 3-step process for labeling data:

1. Specify the labeling guidelines and LLM model to use in a JSON config.
2. Dry-run to make sure the final prompt looks good.
3. Kick off a labeling run for your dataset!

Let's imagine we are building an ML model to analyze sentiment analysis of movie review. We have a dataset of movie reviews that we'd like to get labeled first. For this case, here's what the example dataset and configs will look like:

{
    "task_name": "MovieSentimentReview",
    "task_type": "classification",
    "model": {
        "provider": "openai",
        "name": "gpt-3.5-turbo"
    },
    "dataset": {
        "label_column": "label",
        "delimiter": ","
    },
    "prompt": {
        "task_guidelines": "You are an expert at analyzing the sentiment of movie reviews. Your job is to classify the provided movie review into one of the following labels: {labels}",
        "labels": [
            "positive",
            "negative",
            "neutral"
        ],
        "few_shot_examples": [
            {
                "example": "I got a fairly uninspired stupid film about how human industry is bad for nature.",
                "label": "negative"
            },
            {
                "example": "I loved this movie. I found it very heart warming to see Adam West, Burt Ward, Frank Gorshin, and Julie Newmar together again.",
                "label": "positive"
            },
            {
                "example": "This movie will be played next week at the Chinese theater.",
                "label": "neutral"
            }
        ],
        "example_template": "Input: {example}\nOutput: {label}"
    }
}

Initialize the labeling agent and pass it the config:

from autolabel import LabelingAgent, AutolabelDataset

agent = LabelingAgent(config='config.json')

Preview an example prompt that will be sent to the LLM:

ds = AutolabelDataset('dataset.csv', config = config)
agent.plan(ds)

This prints:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100/100 0:00:00 0:00:00
┌──────────────────────────┬─────────┐
│ Total Estimated Cost     │ $0.538  │
│ Number of Examples       │ 200     │
│ Average cost per example │ 0.00269 │
└──────────────────────────┴─────────┘
─────────────────────────────────────────

Prompt Example:
You are an expert at analyzing the sentiment of movie reviews. Your job is to classify the provided movie review into one of the following labels: [positive, negative, neutral]

Some examples with their output answers are provided below:

Example: I got a fairly uninspired stupid film about how human industry is bad for nature.
Output:
negative

Example: I loved this movie. I found it very heart warming to see Adam West, Burt Ward, Frank Gorshin, and Julie Newmar together again.
Output:
positive

Example: This movie will be played next week at the Chinese theater.
Output:
neutral

Now I want you to label the following example:
Input: A rare exception to the rule that great literature makes disappointing films.
Output:

─────────────────────────────────────────────────────────────────────────────────────────

Finally, we can run the labeling on a subset or entirety of the dataset:

ds = agent.run(ds)

The output dataframe contains the label column:

ds.df.head()
                                                text  ... MovieSentimentReview_llm_label
0  I was very excited about seeing this film, ant...  ...                       negative
1  Serum is about a crazy doctor that finds a ser...  ...                       negative
4  I loved this movie. I knew it would be chocked...  ...                       positive
...

1. Label data for NLP tasks such as classification, question-answering and named entity-recognition, entity matching and more.
2. Use commercial or open source LLMs from providers such as OpenAI, Anthropic, HuggingFace, Google and more.
3. Support for research-proven LLM techniques to boost label quality, such as few-shot learning and chain-of-thought prompting.
4. Confidence estimation and explanations out of the box for every single output label
5. Caching and state management to minimize costs and experimentation time

Refuel provides access to hosted open source LLMs for labeling, and for estimating confidence This is helpful, because you can calibrate a confidence threshold for your labeling task, and then route less confident labels to humans, while you still get the benefits of auto-labeling for the confident examples.

In order to use Refuel hosted LLMs, you can request access here.

Check out our technical report to learn more about the performance of various LLMs, and human annoators, on label quality, turnaround time and cost.

Check out our public roadmap to learn more about ongoing and planned improvements to the Autolabel library.

We are always looking for suggestions and contributions from the community. Join the discussion on Discord or open a Github issue to report bugs and request features.

Autolabel is a rapidly developing project. We welcome contributions in all forms - bug reports, pull requests and ideas for improving the library.

1. Join the conversation on Discord
2. Open an issue on Github for bugs and request features.
3. Grab an open issue, and submit a pull request.