Instructions to use UWB-AIR/MQDD-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UWB-AIR/MQDD-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UWB-AIR/MQDD-pretrained")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-pretrained") model = AutoModel.from_pretrained("UWB-AIR/MQDD-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# MQDD - Multimodal Question Duplicity Detection
|
| 2 |
|
| 3 |
This repository publishes pre-trained model for the paper
|
|
@@ -33,7 +37,3 @@ For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
|
|
| 33 |
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
|
| 34 |
}
|
| 35 |
```
|
| 36 |
-
|
| 37 |
-
---
|
| 38 |
-
license: cc-by-nc-sa-4.0
|
| 39 |
-
---
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-sa-4.0
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
# MQDD - Multimodal Question Duplicity Detection
|
| 6 |
|
| 7 |
This repository publishes pre-trained model for the paper
|
|
|
|
| 37 |
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
|
| 38 |
}
|
| 39 |
```
|
|
|
|
|
|
|
|
|
|
|
|