Instructions to use jdavit/roberta-large-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdavit/roberta-large-QA 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="jdavit/roberta-large-QA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("jdavit/roberta-large-QA") model = AutoModelForQuestionAnswering.from_pretrained("jdavit/roberta-large-QA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from jdavit/roberta-large-QA: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/jdavit/roberta-large-QA/resolve/main/README.md
- Command line
-
hf download hf://jdavit/roberta-large-QA/README.md
-
curl -L -o README.md https://huggingface.co/jdavit/roberta-large-QA/resolve/main/README.md
208 Bytes
metadata
license: apache-2.0
datasets:
- PlanTL-GOB-ES/SQAC
language:
- es
base_model:
- PlanTL-GOB-ES/roberta-large-bne-sqac
pipeline_tag: question-answering
library_name: transformers
tags:
- general-porpuse