Package: refuel-autolabel
Discord|Twitter|Website|Benchmark⚡ Quick Installpip install refuel-autolabel📖 Documentationhttps://docs.refuel.ai/🏷 What is AutolabelAccess tolarge, clean and diverselabeled datasets is a critical component for any machine learning effort to be successful. State-of-the-art LLMs like GPT-4 are able toautomatically label datawithhigh 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.🚀 Getting startedAutolabel provides a simple 3-step process for labeling data:Specify the labeling guidelines and LLM model to use in a JSON config.Dry-run to make sure the final prompt looks good.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:fromautolabelimportLabelingAgent,AutolabelDatasetagent=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 │
└──────────────────────────┴─────────┘
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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_label0Iwasveryexcitedaboutseeingthisfilm,ant......negative1Serumisaboutacrazydoctorthatfindsaser......negative4Ilovedthismovie.Iknewitwouldbechocked......positive...FeaturesLabel data forNLP taskssuch as classification, question-answering and named entity-recognition, entity matching and more.Use commercial or open sourceLLMsfrom providers such as OpenAI, Anthropic, HuggingFace, Google and more.Support for research-proven LLM techniques to boost label quality, such as few-shot learning and chain-of-thought prompting.Confidence estimationand explanations out of the box for every single output labelCaching and state managementto minimize costs and experimentation timeAccess to Refuel hosted LLMsRefuel 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 canrequest access here.BenchmarkCheck out ourtechnical reportto learn more about the performance of various LLMs, and human annoators, on label quality, turnaround time and cost.🛠️ RoadmapCheck out ourpublic roadmapto 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 onDiscordor open aGithub issueto report bugs and request features.🙌 ContributingAutolabel is a rapidly developing project. We welcome contributions in all forms - bug reports, pull requests and ideas for improving the library.Join the conversation onDiscordOpen anissueon Github for bugs and request features.Grab an open issue, and submit apull request.