Instructions to use helenai/Palak-albert-large-v2_squad-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/Palak-albert-large-v2_squad-ov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="helenai/Palak-albert-large-v2_squad-ov")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("helenai/Palak-albert-large-v2_squad-ov") model = AutoModelForQuestionAnswering.from_pretrained("helenai/Palak-albert-large-v2_squad-ov") - Notebooks
- Google Colab
- Kaggle
Palak/albert-large-v2_squad
This is the Palak/albert-large-v2_squad model converted to OpenVINO, for accellerated inference.
An example of how to do inference on this model:
from optimum.intel.openvino import OVModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/Palak-albert-large-v2_squad-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForQuestionAnswering.from_pretrained(model_id)
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
result = pipe("What is OpenVINO?", "OpenVINO is a framework that accelerates deep learning inferencing")
print(result)
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