Instructions to use kuppuluri/telugu_bertu_tydiqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kuppuluri/telugu_bertu_tydiqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kuppuluri/telugu_bertu_tydiqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("kuppuluri/telugu_bertu_tydiqa") model = AutoModelForQuestionAnswering.from_pretrained("kuppuluri/telugu_bertu_tydiqa") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Telugu Question-Answering model trained on Tydiqa dataset from Google
How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
from transformers.pipelines import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained("kuppuluri/telugu_bertu_tydiqa",
clean_text=False,
handle_chinese_chars=False,
strip_accents=False,
wordpieces_prefix='##')
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
result = nlp({'question': question, 'context': context})
Training data
I used Tydiqa Telugu data from Google https://github.com/google-research-datasets/tydiqa
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models.
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