Question Answering
Transformers
Safetensors
Vietnamese
qatc
SemViQA
fact-checking
information-retrieval
Instructions to use SemViQA/qatc-vimrc-viwikifc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/qatc-vimrc-viwikifc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="SemViQA/qatc-vimrc-viwikifc")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("SemViQA/qatc-vimrc-viwikifc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with pipeline tag and license
#1
by nielsr HF Staff - opened
README.md
CHANGED
|
@@ -1,12 +1,14 @@
|
|
| 1 |
---
|
|
|
|
|
|
|
| 2 |
library_name: transformers
|
| 3 |
tags:
|
| 4 |
- SemViQA
|
| 5 |
- question-answering
|
| 6 |
- fact-checking
|
| 7 |
- information-retrieval
|
| 8 |
-
|
| 9 |
-
|
| 10 |
---
|
| 11 |
|
| 12 |
# SemViQA-QATC: Vietnamese Question Answering Token Classifier
|
|
@@ -91,5 +93,4 @@ If you use **SemViQA-QATC** in your research, please cite:
|
|
| 91 |
*Built by Dien X. Tran*
|
| 92 |
[](https://www.linkedin.com/in/xndien2004/)
|
| 93 |
For more details, visit the project repository.
|
| 94 |
-
[](https://github.com/DAVID-NGUYEN-S16/SemViQA)
|
| 95 |
-
|
|
|
|
| 1 |
---
|
| 2 |
+
language:
|
| 3 |
+
- vi
|
| 4 |
library_name: transformers
|
| 5 |
tags:
|
| 6 |
- SemViQA
|
| 7 |
- question-answering
|
| 8 |
- fact-checking
|
| 9 |
- information-retrieval
|
| 10 |
+
pipeline_tag: TEXT_CLASSIFICATION
|
| 11 |
+
license: mit
|
| 12 |
---
|
| 13 |
|
| 14 |
# SemViQA-QATC: Vietnamese Question Answering Token Classifier
|
|
|
|
| 93 |
*Built by Dien X. Tran*
|
| 94 |
[](https://www.linkedin.com/in/xndien2004/)
|
| 95 |
For more details, visit the project repository.
|
| 96 |
+
[](https://github.com/DAVID-NGUYEN-S16/SemViQA)
|
|
|