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--- |
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title: GPTagger |
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emoji: 🏷️ |
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colorFrom: red |
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colorTo: pink |
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sdk: gradio |
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sdk_version: 3.40.1 |
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app_file: app.py |
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pinned: false |
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license: gpl-3.0 |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# [GPTagger](https://github.com/hnliu-git/GPTagger) :label: |
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GPT Tagger is a powerful text tagger that makes use of the GPT model. This tool allows you to extract tags from a given text by leveraging the capabilities of GPT. However, using GPT as a text tagger is not a trivial task. GPT has the tendency to generate non-existing, fabricated, or processed text. To mitigate this issue, GPT Tagger provides a reliable method to ensure that the generated tags are derived from the input text while allowing GPT to process the extracted tags to some extent. |
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Below is an example of how GPT may respond wrong. |
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```md |
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Text: "I earn $1000 this week!" |
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Prompt: "Extract how much he/she earns" |
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# Non-existent text |
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GPT: "one thousand dollar" |
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# Make-up text |
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GPT: "$999999" |
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# Processed text |
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GPT: "$1,000" |
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``` |
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## Introduction |
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These incorrect responses highlight the importance of using a reliable tag extraction tool like GPT Tagger. To do that, GPT Tagger follows a set of main steps: |
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1. 🕵️♀️ Extraction: GPT Tagger sniffs out all possible tags by following your instructions to GPT. |
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2. 🔍 Indexing: It spots the exact locations of these tags within the text. |
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3. ✅ Validator: GPT Tagger's trusty validator steps in to validate if the extracted tags pass the rule-based and ML-based checks. |
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Check the example above how we extract ingredients from a yummy recipe text. 😋 |
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## Features ✨ |
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### Scale up GPT annotators and use switch between GPT3.5 and GPT4 easily |
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- Want to have a higher precision? try using GPT-4! |
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- Want to have a higher recall? Scale up the number of GPT annotators! |
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### Instead of making a perfect prompt, use validator to shave off bad extractions |
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- Simple validator: Length, Regex... |
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- ML validator: GPT validator (Consider it like a chain of GPTs!) |
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## How to Use 🚀 |
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### Setup |
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```shell |
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make install |
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export OPENAI_API_KEY=<your-key> |
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``` |
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### Pre-defined NER pipeline |
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The easiest way to dive into the GPT Tagger is through the Gradio web demo! Fire it up with a single command: |
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```shell |
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poetry run python GPTagger/app.py |
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``` |
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If you prefer having the power of GPT Tagger at your fingertips in Python, check out this snippet: |
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```python |
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from pathlib import Path |
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from GPTagger import * |
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cfg = NerConfig( |
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tag_name='date', |
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tag_regex=r"\d", |
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tag_max_len=128, |
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) |
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prompt = PromptTemplate.from_template(Path('<path-to-prompt>').read_text()) |
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pipeline = NerPipeline.from_config(cfg) |
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doc = Path('<path-to-doc>').read_text() |
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tags = pipeline(doc, prompt) |
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``` |
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### Build Custom Pipelines 🎉 |
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We believe that the possibilities of using GPT as a text tagger are endless! We invite you to contribute your own custom pipelines. Together, we'll unlock the true potential of GPT Tagger and make text tagging an better experience. |
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Leave a star if you find GPTagger is useful for your product or company! 🌟 |
