Question Answering
Transformers
Safetensors
English
Hindi
bert
extractive-question-answering
completeness
split-domain-cognition
synthetic-data
cpu
hinglish
code-mixed
Instructions to use prayasabhinav/rai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prayasabhinav/rai with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="prayasabhinav/rai")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("prayasabhinav/rai") model = AutoModelForQuestionAnswering.from_pretrained("prayasabhinav/rai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
rai 0.3.8: counts some answers typed in Hinglish; 152, 18, 64, 48, 21, 42 and 27 of 58 Hinglish at 11.53
b128387 verified