Instructions to use l3cube-pune/oriya-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/oriya-question-answering-squad-bert 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="l3cube-pune/oriya-question-answering-squad-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/oriya-question-answering-squad-bert") model = AutoModelForQuestionAnswering.from_pretrained("l3cube-pune/oriya-question-answering-squad-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with pipeline tag and library name
#1
by nielsr HF Staff - opened
This PR adds the pipeline_tag and library_name to the model card metadata, improving discoverability and usability of the model. The pipeline_tag is set to question-answering as the model is fine-tuned for question answering tasks. The library_name is set to transformers as the model is compatible with the Hugging Face Transformers library.
Thanks!
l3cube-pune changed pull request status to merged