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text_image_prompt_classification_model

Text Classification
Transformers
Safetensors
English
longformer
feature-extraction
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metadata
library_name: transformers
license: mit
language:
  - en
base_model:
  - allenai/longformer-base-4096
pipeline_tag: text-classification

Model Card for Model ID

Model Details

Model Description

This model is specifically designed to identify whether a user is requesting text or image generation via prompts in a large language model. It leverages advanced techniques to interpret complex inputs and accurately determine the user's intent.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: warhawkmonk
  • Funded by [optional]: warhawkmonk
  • Shared by [optional]: warhawkmonk
  • Model type: Text classification
  • Language(s) (NLP): English
  • License: MIT
  • Finetuned from model [optional]: allenai/longformer-base-4096

Model Sources [optional]

  • Repository: Repo

  • Paper [optional]: [More Information Needed]

  • Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

classifier = pipeline("text-classification", model = "warhawkmonk/text_image_prompt_classification_model")

print(classifier("show me photo of a forest"))

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Training Details

Training Data

Training data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

The following are the evaluation results for the model after training:

Metric Value
Evaluation Loss 0.034379348158836365
Evaluation Accuracy 99.02%
F1 Score 0.9901913554707941
Precision 0.9903776325344953
Recall 0.9901960784313726
Evaluation Runtime 8.6552 seconds
Samples per Second 23.57
Steps per Second 5.892

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: rtx-4060 ti
  • Hours used: 5 hr
  • Cloud Provider: Na
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Model Card Authors [optional]

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Model Card Contact

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