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--- |
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language: "en" |
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thumbnail: "https://huggingface.co/nsi319" |
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tags: |
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- distilbert |
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- pytorch |
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- text-classification |
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- mobile |
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- app |
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- descriptions |
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- playstore |
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- classification |
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license: "mit" |
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inference: true |
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--- |
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# Mobile App Classification |
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## Model description |
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DistilBERT is a transformer model, smaller and faster than BERT, which was pre-trained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. |
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The [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model is fine-tuned to classify an mobile app description into one of **6 play store categories**. |
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Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps). |
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## Fine-tuning |
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The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best evaluation f1 score achieved by the model was 0.9034534096919489, found after 4 epochs. The accuracy of the model on the test set was 0.9033. |
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## How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app") |
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model = AutoModelForSequenceClassification.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app") |
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classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
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classifier("From scores to signings, the ESPN App is here to keep you updated. Never miss another sporting moment with up-to-the-minute scores, latest news & a range of video content. Sign in and personalise the app to receive alerts for your teams and leagues. Wherever, whenever; the ESPN app keeps you connected.") |
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'''Output''' |
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[{'label': 'Sports', 'score': 0.9959789514541626}] |
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``` |
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## Limitations |
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Training data consists of apps from 6 play store categories namely Education, Entertainment, Productivity, Sports, News & Magazines and Photography. |
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