Nonnormalizable/frugal-ai-text-bert-tiny

Model Card for Model ID

Classify text into 8 categories of climate misinformation.

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-2_H-128_A-2

Model Sources [optional]

• Repository: frugal-ai-text-bert-tiny
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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

Use the code below 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-2_H-128_A-2', 'max_length': 256, 'num_epochs': 15, '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': 0.5085738757594687, 'train_acc': 0.8565270935960592, 'test_loss': 1.1659069603139705, 'test_acc': 0.5972108285479901}

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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