Prompt Task And Complexity Classifier
The Prompt Task and Complexity Classifier is a powerful AI model designed to analyze English text prompts across various task types and complexity dimensions. With 11 common task categories and 6 complexity dimensions, it provides a comprehensive understanding of the prompt's requirements. The model uses a DeBERTa backbone and multiple classification heads to achieve high accuracy, with an average top-1 accuracy of 98.1% across all complexity dimensions. It's ready for commercial use and can be easily integrated into various applications, making it an ideal choice for developers and businesses looking to improve their AI capabilities.
Table of Contents
Model Overview
The Prompt Task/Complexity Classifier model is a powerful tool for understanding the complexity of English text prompts. It’s designed to classify tasks into 11 common categories and evaluate complexity across 6 dimensions. But what does that mean for you?
Imagine you have a prompt, like “Write a story about a character who learns a new skill.” This model can help you understand what type of task that is (in this case, Text Generation) and how complex it is. It looks at things like:
- How creative the response needs to be
- How much reasoning is required
- How much contextual knowledge is needed
- How much domain-specific knowledge is required
- How many constraints are in the prompt
- How many examples are provided
Capabilities
This model is capable of classifying prompts into 11 common task categories, including:
- Open QA
- Closed QA
- Summarization
- Text Generation
- Code Generation
- Chatbot
- Classification
- Rewrite
- Brainstorming
- Extraction
- Other
Task Classification
The model can classify prompts into these categories with high accuracy. But how does it do it? It uses a DeBERTa backbone and multiple classification heads to make these predictions.
Complexity Analysis
The model evaluates the complexity of a prompt across 6 dimensions:
- Creativity: How creative does the response need to be?
- Reasoning: How much logical or cognitive effort is required to respond?
- Contextual Knowledge: How much background information is needed to respond?
- Domain Knowledge: How much specialized knowledge is required to respond?
- Constraints: How many constraints or conditions are provided with the prompt?
- Number of Few Shots: How many examples are provided with the prompt?
The model then calculates an overall complexity score based on these dimensions.
Example Use Cases
Here are a few examples of how the model can be used:
- Text Generation: The model can classify a prompt as a text generation task and evaluate its complexity.
- Summarization: The model can classify a prompt as a summarization task and evaluate its complexity.
- Code Generation: The model can classify a prompt as a code generation task and evaluate its complexity.
How to Use
You can use this model in NVIDIA NeMo Curator or in Transformers. The code is available on the NeMo Curator GitHub repository. Here’s an example of how to use it in Transformers:
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
from transformers import AutoConfig, AutoModel, AutoTokenizer
#... (code snippet)
prompt = ["Prompt: Write a Python script that uses a for loop."]
encoded_texts = tokenizer(prompt, return_tensors="pt", add_special_tokens=True, max_length=512, padding="max_length", truncation=True)
result = model(encoded_texts)
print(result)
This will output the task type, complexity scores, and other relevant information for the prompt.
Alternatives
If you’re looking for alternative models, you might want to consider:
- ==Other Models==: These models may have similar capabilities, but with different strengths and weaknesses.
Performance
This model showcases remarkable performance in classifying English text prompts across task types and complexity dimensions. Let’s dive into its speed, accuracy, and efficiency.
Speed
The model’s architecture uses a DeBERTa backbone, which can handle up to 12k tokens, but the default context length is set at 512 tokens. This allows for fast processing of text inputs.
Accuracy
The model achieves high accuracy in various tasks, with an average top-1 accuracy of 0.981 across 10 folds for the task categorization. The accuracy for each complexity dimension is also impressive, with the highest being 0.996 for creativity and the lowest being 0.937 for domain knowledge.
| Complexity Dimension | Average Top-1 Accuracy |
|---|---|
| Creativity | 0.996 |
| Reasoning | 0.997 |
| Contextual Knowledge | 0.981 |
| Few Shots | 0.979 |
| Domain Knowledge | 0.937 |
| Constraint | 0.991 |
Efficiency
The model’s multi-headed approach enables it to predict simultaneously during inference, making it efficient in processing multiple tasks and complexity dimensions at once.
Limitations
This model is not perfect, and it has some limitations. Let’s take a closer look:
- Limited Context Length: The model can only handle up to
512tokens, which might not be enough for longer texts or more complex prompts. - Task Type Limitations: While the model can classify tasks across
11common categories, it might struggle with more specialized or niche tasks. - Complexity Dimensions: The model evaluates complexity across
6dimensions, but these dimensions might not capture the full complexity of your prompts. - Data Quality: The model was trained on a dataset of
4024English prompts, but the quality of this data might not be perfect. - Overfitting: The model might overfit to the training data, which means it becomes too specialized to the specific prompts and tasks it was trained on.
- Lack of Human Judgment: While the model can provide useful classifications, it’s ultimately a machine. It might not be able to capture the nuances and complexities of human judgment.
- Dependence on Hardware and Software: The model requires specific hardware and software to run, which might limit its accessibility or usability.
- Limited Explainability: The model’s classifications might not be easily explainable, which could make it difficult to understand why the model made a particular decision.
By understanding these limitations, you can use this model more effectively and make more informed decisions about when to rely on its classifications.
