---
base_model: google/bert_uncased_L-12_H-768_A-12
datasets:
- QuotaClimat/frugalaichallenge-text-train
language:
- en
license: apache-2.0
model_name: frugal-ai-text-bert-base
pipeline_tag: text-classification
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- climate
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### 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

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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

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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 -->

#### Speeds, Sizes, Times [optional]

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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



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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