Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
Model Card for Model ID
The model classifies DNA sequences from 31 classes. So, it is metagenomic.
Model Details
Model Description
This model takes sequences of varying size and classify them.
- Developed by: Md Rasheduzzaman
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: Md Rasheduzzaman
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: InstaDeepAI/nucleotide-transformer-v2-50m-multi-species
Model Sources [optional]
InstaDeepAI/nucleotide-transformer-v2-50m-multi-species
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- 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
It is trained only on 31 accession ids as a proof of concept.
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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: fp16 mixed precision
Speeds, Sizes, Times [optional]
- epoch: 3.0
- eval_accuracy: 0.9151175
- eval_loss: 0.7366227507591248
- eval_macro_f1: 0.6849655304585746
- eval_runtime: 15257.5132
- eval_samples_per_second: 26.217
- eval_steps_per_second: 3.277
- step: 399999
- logging_steps: 50
- max_steps: 666665
- num_input_tokens_seen: 0
- num_train_epochs: 5
- save_steps: 500
- early_stopping_patience: 3
Evaluation
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: GPU
- Hours used: [More Information Needed]
- Cloud Provider: FLI Insel Riems
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- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
Tesla V100S-PCIE-32GB, 32768 MiB, 530.30.02, 7.0
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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Framework versions
- PEFT 0.17.1
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