Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 45
Model Card for Model ID
Model Details
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: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & metricss
Testing Data
[More Information Needed]
Factors
[More Information Needed]
metricss
[More Information Needed]
Results
[More Information Needed]
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
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
- Downloads last month
- 163
Spaces using w601sxs/b1ade-embed-kd_3 11
Paper for w601sxs/b1ade-embed-kd_3
Evaluation results
- accuracy on MTEB MassiveScenarioClassificationtest set self-reported0.759
- accuracy on MTEB ImdbClassificationtest set self-reported0.737
- ndcg_at_10 on MTEB SCIDOCStest set self-reported0.146
- cosine_spearman on MTEB BIOSSEStest set self-reported0.743
- v_measure on MTEB TwentyNewsgroupsClusteringtest set self-reported0.460
- ndcg_at_10 on MTEB FEVERtest set self-reported0.254
- ap on MTEB TwitterSemEval2015test set self-reported0.672
- ndcg_at_10 on MTEB NFCorpustest set self-reported0.233
- ndcg_at_10 on MTEB HotpotQAtest set self-reported0.298
- cosine_spearman on MTEB STS15test set self-reported0.824
- ndcg_at_10 on MTEB CQADupstackMathematicaRetrievaltest set self-reported0.154
- ndcg_at_10 on MTEB CQADupstackEnglishRetrievaltest set self-reported0.249
- v_measure on MTEB ArxivClusteringP2Ptest set self-reported0.444
- ndcg_at_10 on MTEB FiQA2018test set self-reported0.159
- map on MTEB SciDocsRRtest set self-reported0.789
- v_measure on MTEB RedditClusteringtest set self-reported0.497