Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="warhawkmonk/text_image_prompt_classification_model")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("warhawkmonk/text_image_prompt_classification_model")
model = AutoModelForSequenceClassification.from_pretrained("warhawkmonk/text_image_prompt_classification_model", device_map="auto")Quick Links
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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
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Uses
Direct Use
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Bias, Risks, and Limitations
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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
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Training Procedure
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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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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]
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Base model
allenai/longformer-base-4096
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