Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -1,3 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
# Dataset Card for DailyDialog
|
2 |
|
3 |
- **Repository:** http://yanran.li/dailydialog
|
@@ -5,6 +19,16 @@
|
|
5 |
- **Leaderboard:** None
|
6 |
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
|
7 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
8 |
### Dataset Summary
|
9 |
|
10 |
DailyDialog is a high-quality multi-turn dialog dataset. It is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information.
|
|
|
1 |
+
---
|
2 |
+
language:
|
3 |
+
- en
|
4 |
+
license:
|
5 |
+
- cc-by-nc-sa-4.0
|
6 |
+
multilinguality:
|
7 |
+
- monolingual
|
8 |
+
pretty_name: DailyDialog
|
9 |
+
size_categories:
|
10 |
+
- 10K<n<100K
|
11 |
+
task_categories:
|
12 |
+
- conversational
|
13 |
+
---
|
14 |
+
|
15 |
# Dataset Card for DailyDialog
|
16 |
|
17 |
- **Repository:** http://yanran.li/dailydialog
|
|
|
19 |
- **Leaderboard:** None
|
20 |
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
|
21 |
|
22 |
+
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
|
23 |
+
```
|
24 |
+
from convlab.util import load_dataset, load_ontology, load_database
|
25 |
+
|
26 |
+
dataset = load_dataset('dailydialog')
|
27 |
+
ontology = load_ontology('dailydialog')
|
28 |
+
database = load_database('dailydialog')
|
29 |
+
```
|
30 |
+
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
|
31 |
+
|
32 |
### Dataset Summary
|
33 |
|
34 |
DailyDialog is a high-quality multi-turn dialog dataset. It is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information.
|