Instructions to use yash-srivastava19/assamese_RoBERTa-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yash-srivastava19/assamese_RoBERTa-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yash-srivastava19/assamese_RoBERTa-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yash-srivastava19/assamese_RoBERTa-tiny") model = AutoModelForMaskedLM.from_pretrained("yash-srivastava19/assamese_RoBERTa-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
This is a tiny model used for testing language models from scratch for Indic Languages. Started with assamese, as the data was short(and trimmed it to be in limits of Google Colab Free Tier) The final goal is to do this for other indic languages, and to use BART architecture, to extend IndicBART.
The model uses RoBERTa, with Byte Level Byte Pair Encoding for the Tokenizer part
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