frugal-ai-text-bert-base

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Classify text into 8 categories of climate misinformation using google/bert_uncased_L-12_H-768_A-12.

Model Details

Model Description

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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]

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• Repository: frugal-ai-text-bert-base
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Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Training Details

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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} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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{'train_loss': 0.1672610024448301, 'train_acc': 0.9589490968801314, 'test_loss': 0.967790144604522, 'test_acc': 0.6907301066447908}

Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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