Create README.md

README.md

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+---
+language: "en"
+thumbnail: "https://huggingface.co/nsi319"
+tags:
+- distilbert
+- pytorch
+- text-classification
+- mobile
+- app
+- descriptions
+- playstore
+- multi-class 
+- classification
+liscence: "mit"
+inference: true
+---
+
+# Mobile App Classification
+
+## Model description
+
+DistilBERT is a transformers 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.
+
+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**. 
+Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps).
+
+## Fine-tuning
+
+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 90.33.  
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app")  
+model = AutoModelForSequenceClassification.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app")
+
+classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+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.")
+
+'''Output'''
+[{'label': 'Sports', 'score': 0.9959789514541626}]
+```
+
+## Limitations
+Training data consists of apps from 6 play store categories namely Education, Entertainment, Productivity, Sports, News & Magazines and Photography.
+