Instructions to use dewdev/question_and_answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/question_and_answer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dewdev/question_and_answer")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dewdev/question_and_answer") model = AutoModelForQuestionAnswering.from_pretrained("dewdev/question_and_answer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from dewdev/question_and_answer: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/dewdev/question_and_answer/resolve/main/README.md
- Command line
-
hf download hf://dewdev/question_and_answer/README.md
-
curl -L -o README.md https://huggingface.co/dewdev/question_and_answer/resolve/main/README.md
261 Bytes
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-cased-distilled-squad
tags:
- generated_from_keras_callback
model-index:
- name: Docty/question_and_answer
results: []
this is a clone of Docty/question_and_answer