README.md

---
library_name: transformers
license: mit
language:
- en
base_model:
- allenai/longformer-base-4096
pipeline_tag: text-classification
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## 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]

<!-- Provide the basic links for the model. -->

- **Repository:** [Repo](https://huggingface.co/warhawkmonk/text_image_prompt_classification_model "Optional Title Here")

- **Paper [optional]:** [More Information Needed]

- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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"))
```

[More Information Needed]

## Training Details

### Training Data

[Training data](https://huggingface.co/datasets/warhawkmonk/prompt_classifier "Optional Title Here")

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## 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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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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