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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- matthews_correlation |
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widget: |
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- text: "Highway work zones create potential risks for both traffic and workers in addition to traffic congestion and delays that result in increased road user delay." |
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- text: "A circular economy is a way of achieving sustainable consumption and production, as well as nature positive outcomes." |
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--- |
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# sadickam/sdg-classification-bert |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model (sgdBERT) is for classifying text with respect to the United Nations sustainable development goals (SDG). |
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Source:https://www.un.org/development/desa/disabilities/about-us/sustainable-development-goals-sdgs-and-disability.html |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This text classification model was developed by fine-tuning the bert-base-uncased pre-trained model. The training data for this fine-tuned model was sourced from the publicly available OSDG Community Dataset (OSDG-CD) at https://zenodo.org/record/5550238#.ZBulfcJByF4. |
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This model was made as part of academic research at Deakin University. The goal was to make a transformer-based SDG text classification model that anyone could use. Only the first 16 UN SDGs supported. The primary model details are highlighted below: |
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- **Model type:** Text classification |
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- **Language(s) (NLP):** English |
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- **License:** mit |
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- **Finetuned from model [optional]:** bert-base-uncased |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://huggingface.co/sadickam/sdg-classification-bert |
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- **Demo [optional]:** option 1: https://sadickam-sdg-text-classifier.hf.space/; option 2: https://sadickam-sdg-classification-bert-main-qxg1gv.streamlit.app/ |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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This is a fine-tuned model and therefore requires no further training. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("sadickam/sdg-classification-bert") |
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model = AutoModelForSequenceClassification.from_pretrained("sadickam/sdg-classification-bert") |
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``` |
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## Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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The training data includes text from a wide range of industries and academic research fields. Hence, this fine-tuned model is not for a specific industry. |
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See training here: https://zenodo.org/record/5550238#.ZBulfcJByF4 |
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## Training Hyperparameters |
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- Num_epoch = 3 |
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- Learning rate = 5e-5 |
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- Batch size = 16 |
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## Evaluation |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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- Accuracy = 0.9 |
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- Matthews correlation = 0.89 |
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## Citation |
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Sadick, A.M. (2023). SDG classification with BERT. https://huggingface.co/sadickam/sdg-classification-bert |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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## Model Card Contact |
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[email protected] |
