🤖 question-answering

tiny-distilbert-base-cased-distilled-squad

sshleifer/tiny-distilbert-base-cased-distilled-squad

Get AI Model →
21.6K
Downloads
❤️
1
Likes
🏷️
8
Tags
📦
transformers
Library
Model Details
Full Model IDsshleifer/tiny-distilbert-base-cased-distilled-squad
Pipeline / Taskquestion-answering
Librarytransformers
Downloads (all-time)21.6K
Likes1
Last Modified5/14/2020
Author / Orgsshleifer
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="sshleifer/tiny-distilbert-base-cased-distilled-squad")

# Run inference
result = pipe("Your input here")
print(result)
🏷️ Tags
transformerspytorchtfdistilbertquestion-answeringendpoints_compatibledeploy:azureregion:us
More question-answering Models
See all →
electra_large_discriminator_squad2_512

ahotrod/electra_large_discriminator_squad2_512

888.7K❤️ 7
Get AI Model →
roberta-base-squad2

deepset/roberta-base-squad2

512.2K❤️ 946
Get AI Model →
mdeberta-v3-base-squad2

timpal0l/mdeberta-v3-base-squad2

288.4K❤️ 259
Get AI Model →
🚀 Use This Model

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
Downloads21.6K
❤️ Community Likes1
🛠️ 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.
👋 Need help with code?