Text Classification
Transformers
PyTorch
Arabic
t5
text2text-generation
Classification
ArabicT5
Text Classification
Instructions to use Hezam/ArabicT5_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hezam/ArabicT5_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hezam/ArabicT5_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hezam/ArabicT5_Classification") model = AutoModelForSeq2SeqLM.from_pretrained("Hezam/ArabicT5_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -23,7 +23,9 @@ widget:
|
|
| 23 |
[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
|
| 24 |
[https://data.mendeley.com/datasets/57zpx667y9/2]
|
| 25 |
|
|
|
|
| 26 |
category_mapping = {
|
|
|
|
| 27 |
'Politics':1,
|
| 28 |
'Finance':2,
|
| 29 |
'Medical':3,
|
|
|
|
| 23 |
[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
|
| 24 |
[https://data.mendeley.com/datasets/57zpx667y9/2]
|
| 25 |
|
| 26 |
+
# # The category mapping
|
| 27 |
category_mapping = {
|
| 28 |
+
|
| 29 |
'Politics':1,
|
| 30 |
'Finance':2,
|
| 31 |
'Medical':3,
|