Instructions to use tiya1012/distilka_applied with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/distilka_applied with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/distilka_applied")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/distilka_applied") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/distilka_applied", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This model has been trained on The CoLI-Kenglish dataset which consists of English and Kannada words in Roman script and are grouped into six major categories, namely, “Kannada”, “English”, “Mixed-language”, “Name”, “Location” and “Other” and submit their methods in Kanglish shared task where each word will be identified and categorized in one of earlier mentioned categories. The dataset has been used in shared-task" CoLI-Kanglish: Word Level Language Identification in Code-mixed Kannada-English Texts" https://sites.google.com/view/kanglishicon2022/dataseta835aa8
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