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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Download README.md from tiya1012/distilka_applied: direct link, hf CLI and curl.
- Browser
- Download file 448 Bytes
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https://huggingface.co/tiya1012/distilka_applied/resolve/main/README.md
- Command line
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hf download hf://tiya1012/distilka_applied/README.md
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curl -L -o README.md https://huggingface.co/tiya1012/distilka_applied/resolve/main/README.md
448 Bytes
| # code-mixed Kannada-English word-level identification trained on Kanglish dataset from ICON2022 | |
| #### How to use | |
| Use the below script from your python terminal | |
| ```python | |
| import transformers | |
| from transformers import AutoModelForSequenceClassification | |
| model= AutoModelForSequenceClassification.from_pretrained("tiya1012/distilka_applied") | |
| ``` | |
| ## Training data | |
| I used code-mixed dataset from https://sites.google.com/view/kanglishicon2022/home | |