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") - Notebooks
- Google Colab
- Kaggle
basic training
Browse files- config.json +1 -1
- pytorch_model.bin +3 -0
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
"T5ForConditionalGeneration"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "/content/drive/MyDrive/model_vs",
|
| 3 |
"architectures": [
|
| 4 |
"T5ForConditionalGeneration"
|
| 5 |
],
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:153858924cea1aefe965a5daca36ddc67ce66dca57579880f085c3d489fa0840
|
| 3 |
+
size 653096497
|