Model Card for Model ID
Classify text into 8 categories of climate misinformation using google/bert_uncased_L-12_H-768_A-12.
Model Details
Model Description
Fine trained BERT for classifying climate information as part of the Frugal AI Challenge, for submission to https://huggingface.co/frugal-ai-challenge and scoring on accuracy and efficiency. Trainied on only the non-evaluation 80% of the data, so it's (non-cheating) score will be lower.
- Developed by: Andre Bach
- Funded by [optional]: N/A
- Shared by [optional]: Andre Bach
- Model type: Text classification
- Language(s) (NLP): ['en']
- License: apache-2.0
- Finetuned from model [optional]: google/bert_uncased_L-12_H-768_A-12
Model Sources [optional]
- Repository: frugal-ai-text-bert-base
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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
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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
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: {'max_dataset_size': 'full', 'bert_variety': 'google/bert_uncased_L-12_H-768_A-12', 'max_length': 256, 'num_epochs': 3, 'batch_size': 16}
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
{'train_loss': 0.1672610024448301, 'train_acc': 0.9589490968801314, 'test_loss': 0.967790144604522, 'test_acc': 0.6907301066447908}
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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Technical Specifications [optional]
Model Architecture and Objective
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Software
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