Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
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Model Description
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: Ravi Rajput @Next Gen Neuron
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- Model type: Sentiment Analysis
- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: bert-base-uncased
Model Sources [optional]
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Uses
The model can be used for knowledge and expertiment purposes. I have finetuned it to demostrate in my channel videos and blogs.
Direct Use
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Out-of-Scope 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.
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Training Details
Training Data
IMDB dataset
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Training Procedure
Specifically, it was trained for 1 epoch with a batch size of 16 for both training and evaluation, a learning rate of 2e-5, and a fixed random seed (42) for reproducibility, with evaluation conducted at the end of each epoch.
Preprocessing [optional]
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Training Hyperparameters
| Hyperparameter | Value |
|---|---|
| Model | BERT (bert-base-uncased) |
| Dataset | IMDB |
| Number of Training Epochs | 1 |
| Training Batch Size | 16 |
| Evaluation Batch Size | 16 |
| Learning Rate | 2e-5 |
| Evaluation Strategy | Per Epoch |
| Random Seed | 42 |
| Output Directory | next_gen_neuron/finetuned/next_gen_neuron_bert_sentimentanalysis |
Speeds, Sizes, Times [optional]
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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).
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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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Software
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