safetensors bert model_hub_mixin pytorch_model_hub_mixin climate text-classification en dataset:QuotaClimat/frugalaichallenge-text-train arxiv:1910.09700 base_model:google/bert_uncased_L-4_H-256_A-4 base_model:finetune:google/bert_uncased_L-4_H-256_A-4 license:apache-2.0 region:us
---
base_model: google/bert_uncased_L-4_H-256_A-4
datasets:
- QuotaClimat/frugalaichallenge-text-train
language:
- en
license: apache-2.0
model_name: frugal-ai-text-bert-mini
pipeline_tag: text-classification
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- climate
---

# Model Card for Model ID

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Classify text into 8 categories of climate misinformation.

## 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-4_H-256_A-4

### Model Sources [optional]

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- **Repository:** frugal-ai-text-bert-mini
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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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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

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** {'max_dataset_size': 'full', 'bert_variety': 'google/bert_uncased_L-4_H-256_A-4', 'max_length': 256, 'num_epochs': 8, '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

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#### Factors

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#### Metrics

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{'train_loss': 0.3437441659755394, 'train_acc': 0.8975779967159278, 'test_loss': 1.0706236007345187, 'test_acc': 0.649712879409352}

### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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]
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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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## Glossary [optional]

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