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--- |
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language: ur |
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license: afl-3.0 |
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--- |
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# XLM-RoBERTa-Urdu-Classification |
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This [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) text classification model trained on Urdu sentiment [data-set](https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data) performs binary sentiment classification on any given Urdu sentence. The model has been fine-tuned for better results in manageable time frames. |
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## Model description |
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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. |
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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. |
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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. |
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### How to use |
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You can import this model directly from the transformers library: |
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```python |
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>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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>>> tokenizer = AutoTokenizer.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu") |
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>>> model = AutoModelForSequenceClassification.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu") |
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``` |
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Here is how to use this model to get the label of a given text: |
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```python |
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>>> from transformers import TextClassificationPipeline |
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>>> text = "وہ ایک برا شخص ہے" |
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>>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device = 0) |
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>>> pipe(text) |
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``` |
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