# autolabel
**Repository Path**: hpd84_0321/autolabel
## Basic Information
- **Project Name**: autolabel
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-13
- **Last Updated**: 2024-07-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://github.com/refuel-ai/autolabel/actions/workflows/black.yaml/badge.svg)   [](https://discord.gg/fweVnRx6CU) [](https://colab.research.google.com/drive/1t-9vNLkyoyySAG_0w3eR98biBOXlMO-E?usp=sharing)
## ⚡ Quick Install
`pip install refuel-autolabel`
## 📖 Documentation
[https://docs.refuel.ai/](https://docs.refuel.ai/)
## 🏷 What is Autolabel
Access to [large, clean and diverse](https://twitter.com/karpathy/status/1528443124577513472?lang=en) 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](https://arxiv.org/abs/2303.15056) with [high accuracy](https://arxiv.org/abs/2303.16854), 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.
## 🌟 (New!) Benchmark models on Refuel's Benchmark
Check out our [technical report](https://refuel.ai/blog-posts/announcing-refuel-llm-2) to learn more about the performance of RefuelLLM-v2 on our benchmark. You can replicate the benchmark yourself by following the steps below
```python
cd autolabel/benchmark
curl https://autolabel-benchmarking.s3.us-west-2.amazonaws.com/data.zip -o data.zip
unzip data.zip
python benchmark.py --model $model --base_dir benchmark-results
python results.py --eval_dir benchmark-results
cat results.csv
```
You can benchmark the relevant model by replacing $model with the name of the model needed to be benchmarked. If it is an API hosted model like `gpt-3.5-turbo`, `gpt-4-1106-preview`, `claude-3-opus-20240229`, `gemini-1.5-pro-preview-0409` or some other Autolabel supported model, just write the name of the model. If the model to be benchmarked is a [vLLM supported model](https://docs.vllm.ai/en/latest/models/supported_models.html) then pass the local path or the huggingface path corresponding to the model. This will run the benchmark along with the _same_ prompts for all models.
The `results.csv` will contain a row with every model that was benchmarked as a row. Look at `benchmark/results.csv` for an example.
## 🚀 Getting started
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:
```python
{
"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:
```python
from autolabel import LabelingAgent, AutolabelDataset
agent = LabelingAgent(config='config.json')
```
Preview an example prompt that will be sent to the LLM:
```python
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:
```python
ds = agent.run(ds)
```
The output dataframe contains the label column:
```python
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
...
```
## Features
1. Label data for [NLP tasks](https://docs.refuel.ai/guide/tasks/classification_task/) such as classification, question-answering and named entity-recognition, entity matching and more.
2. Use commercial or open source [LLMs](https://docs.refuel.ai/guide/llms/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](https://docs.refuel.ai/guide/accuracy/confidence/) and explanations out of the box for every single output label
5. [Caching and state management](https://docs.refuel.ai/guide/reliability/state-management/) to minimize costs and experimentation time
## Access to Refuel hosted LLMs
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](https://refuel-ai.typeform.com/llm-access).
## 🛠️ Roadmap
Check out our [public roadmap](https://github.com/orgs/refuel-ai/projects/15) 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](https://discord.gg/fweVnRx6CU) or open a [Github issue](https://github.com/refuel-ai/autolabel/issues) to report bugs and request features.
## 🙌 Contributing
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](https://discord.gg/fweVnRx6CU)
2. Open an [issue](https://github.com/refuel-ai/autolabel/issues) on Github for bugs and request features.
3. Grab an open issue, and submit a [pull request](https://github.com/refuel-ai/autolabel/blob/main/CONTRIBUTING.md).