Xlm Roberta Base Finetuned Urdu

🚀 XLM-RoBERTa-Urdu-Classification

This text classification model, based on xlm-roberta-base and trained on Urdu sentiment data-set, can perform binary sentiment classification on any given Urdu sentence. It has been fine-tuned for better results within a reasonable time frame.

🚀 Quick Start

This model is a text classification model based on xlm-roberta-base, trained on Urdu sentiment data. It can perform binary sentiment classification on any given Urdu sentence.

✨ Features

• Trained on Urdu sentiment data for binary sentiment classification.
• Fine - tuned for better performance in a manageable time.

📦 Installation

To use this model, you need to install the transformers library. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

You can import this model directly from the transformers library:

>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")
>>> model = AutoModelForSequenceClassification.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")

Advanced Usage

Here is how to use this model to get the label of a given text:

>>> from transformers import TextClassificationPipeline
>>> text = "وہ ایک برا شخص ہے"
>>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device = 0)
>>> pipe(text)

📚 Documentation

Model description

XLM - RoBERTa is a scaled cross - lingual sentence encoder. It is trained on 2.5T of data across 100 languages data filtered from Common Crawl. XLM - R achieves state - of - the - arts results on multiple cross - lingual benchmarks.

The XLM - RoBERTa model was proposed in Unsupervised Cross - lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.

It is based on Facebook’s RoBERTa model released in 2019. It is a large multi - lingual language model, trained on 2.5TB of filtered CommonCrawl data.

📄 License

This project is licensed under the AFL - 3.0 license.