Instructions to use methodya/Bookmus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use methodya/Bookmus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="methodya/Bookmus")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("methodya/Bookmus") model = AutoModelForSequenceClassification.from_pretrained("methodya/Bookmus") - Notebooks
- Google Colab
- Kaggle
Book Genre Prediction Model
This model predicts book genres based on their titles. It's trained on Arabic and English book titles.
Overview
The model is built using a BERT-based architecture and is trained to classify book titles into various genres. It leverages tokenization specific to Arabic and English languages for effective processing.
How to Use
Installation
To use this model, first, install the transformers library:
pip install transformers
### Usage
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
model_name = "Book Genre Prediction Model"
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
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