Question Answering
Transformers
Safetensors
Tigrinya
extractive-qa
xlm-roberta
vexmlm
geez
low-resource
Instructions to use Hailay/VEXMLM-TiQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hailay/VEXMLM-TiQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Hailay/VEXMLM-TiQuAD")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hailay/VEXMLM-TiQuAD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7419639d4f9a5ce1663035fc017c9841cacef218cd64264cd1ce92f8e4440083
- Size of remote file:
- 19.1 MB
- SHA256:
- f0716aee39e2114af7e19e4025f1ad23ecd756c0afa8de5cfbeaf45d4efe7547
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.