Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 53
Model Card for Model ID
This modelcard aims to detect text that was generated by LLMs.
Model Details
Model Description
- Developed by: huolongguo10
- Model type: bert
- Language(s) (NLP): Chinese
- License: [More Information Needed]
- Finetuned from model [optional]: bert-base-chinese
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("huolongguo10/LLM_detect")
model = AutoModelForMaskedLM.from_pretrained("huolongguo10/LLM_detect")
Training Details
Training Data
Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: fp32
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: P100
- Hours used: 4h
- Cloud Provider: kaggle
Technical Specifications [optional]
Model Architecture and Objective
bert
Compute Infrastructure
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Hardware
P100
Software
transformers
Citation [optional]
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