Instructions to use l3cube-pune/gujarati-question-answering-squad-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/gujarati-question-answering-squad-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="l3cube-pune/gujarati-question-answering-squad-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/gujarati-question-answering-squad-bert") model = AutoModelForQuestionAnswering.from_pretrained("l3cube-pune/gujarati-question-answering-squad-bert") - Notebooks
- Google Colab
- Kaggle
Add library_name, pipeline_tag metadata, and Github link
#1
by nielsr HF Staff - opened
This PR adds missing metadata to the model card:
library_name: transformers: Specifies that the model is compatible with the Hugging Face Transformers library.pipeline_tag: question-answering: Correctly identifies the model's intended use.- Adds a link to the Github repository for easier access to the code.
Thanks!
l3cube-pune changed pull request status to merged