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")# 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
|
@@ -19,10 +19,31 @@ widget:
|
|
| 19 |
---
|
| 20 |
|
| 21 |
# # Arabic text classification using deep learning (ArabicT5)
|
|
|
|
| 22 |
- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
|
|
|
|
|
|
|
|
|
|
| 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 |
|
|
|
|
| 19 |
---
|
| 20 |
|
| 21 |
# # Arabic text classification using deep learning (ArabicT5)
|
| 22 |
+
|
| 23 |
- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
|
| 24 |
+
|
| 25 |
+
- Paper
|
| 26 |
+
-
|
| 27 |
[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
|
| 28 |
+
|
| 29 |
+
-Dataset
|
| 30 |
+
|
| 31 |
[https://data.mendeley.com/datasets/57zpx667y9/2]
|
| 32 |
|
| 33 |
+
# # Their experiment'
|
| 34 |
+
|
| 35 |
+
[https://www.sciencedirect.com/science/article/abs/pii/S0306457319303413]
|
| 36 |
+
|
| 37 |
+
| Model || Accuracy |
|
| 38 |
+
| :---------------------: || :---------------------: |
|
| 39 |
+
| CGRU || 93.43% |
|
| 40 |
+
| HANGRU || 95.81% |
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
[https://www.sciencedirect.com/science/article/abs/pii/S0306457319303413]
|
| 44 |
+
|
| 45 |
+
# # Our experiment
|
| 46 |
+
|
| 47 |
# # The category mapping
|
| 48 |
category_mapping = {
|
| 49 |
|