Instructions to use AyoubChLin/ESG-bert-BBC_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use AyoubChLin/ESG-bert-BBC_news with Flair:
from flair.data import Sentence from flair.models import SequenceTagger tagger = SequenceTagger.load("AyoubChLin/ESG-bert-BBC_news") sentence = Sentence("George Washington went to Washington.") tagger.predict(sentence) print(sentence) - Notebooks
- Google Colab
- Kaggle
Commit ·
c5dde2a
1
Parent(s): 8d76a3f
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,10 +8,12 @@ metrics:
|
|
| 8 |
- accuracy
|
| 9 |
pipeline_tag: text-classification
|
| 10 |
widget:
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
|
|
|
|
|
|
| 15 |
---
|
| 16 |
|
| 17 |
label = ['business', 'entertainment', 'politics', 'sport', 'tech']
|
|
|
|
| 8 |
- accuracy
|
| 9 |
pipeline_tag: text-classification
|
| 10 |
widget:
|
| 11 |
+
- text: money in the pocket
|
| 12 |
+
- text: no one can win this cup in quatar..
|
| 13 |
+
- text: >-
|
| 14 |
+
new transformers architicture can build a large language model with low
|
| 15 |
+
ressources
|
| 16 |
+
library_name: flair
|
| 17 |
---
|
| 18 |
|
| 19 |
label = ['business', 'entertainment', 'politics', 'sport', 'tech']
|