Model Details
Full Model IDdistilbert/distilbert-base-cased-distilled-squad
Pipeline / Taskquestion-answering
Librarytransformers
Downloads (all-time)211.9K
Likes268
Last Modified5/6/2024
Author / Orgdistilbert
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("question-answering", model="distilbert/distilbert-base-cased-distilled-squad")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchtfrustsafetensorsopenvinodistilbertquestion-answeringendataset:squadarxiv:1910.01108arxiv:1910.09700license:apache-2.0model-indexendpoints_compatibledeploy:azureregion:us
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: question-answering
This model is designed for the question-answering task. Explore more models for this use case.
All question-answering Models →📊 Popularity
⬇ Downloads211.9K
❤️ Community Likes268
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.