Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 47
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ZeroUniqueness/longt5-transformers-finetuned", dtype="auto")Quick Links
Model Card for Model ID
Summarize long context english articles
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
Trained at 16384 context length to summarize articles from arxiv-summarization dataset. Fine tuning was run using QLoRA techniques to limit memory usage while targetting as many layers as possible.
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- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: [More Information Needed]
Uses
Use for simple article summarization tasks:
summarize:
[article]
summary:
...
Direct Use
TODO
Out-of-Scope Use
Not a regular instruction model. Tasks other than summarizations may not work as expected.
Bias, Risks, and Limitations
TODO
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
TODO
Training Details
Training Data
[More Information Needed]
Training Procedure
In progress
Training Hyperparameters
- Training regime: [More Information Needed]
Evaluation
In progress
Testing Data, Factors & Metrics
Testing Data
Factors
Metrics
Results
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- 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]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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Model tree for ZeroUniqueness/longt5-transformers-finetuned
Base model
google/long-t5-tglobal-base
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="ZeroUniqueness/longt5-transformers-finetuned")