Text Classification
Transformers
Safetensors
Arabic
bert
sentiment
arabic
classification
text-embeddings-inference
Instructions to use Nadasr/sentAnalysisModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadasr/sentAnalysisModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nadasr/sentAnalysisModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nadasr/sentAnalysisModel") model = AutoModelForSequenceClassification.from_pretrained("Nadasr/sentAnalysisModel") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,12 @@
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
-
tags:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
|
| 6 |
# Model Card for Model ID
|
|
|
|
| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
+
tags:
|
| 4 |
+
- sentiment
|
| 5 |
+
- arabic
|
| 6 |
+
- classification
|
| 7 |
+
language:
|
| 8 |
+
- ar
|
| 9 |
+
pipeline_tag: text-classification
|
| 10 |
---
|
| 11 |
|
| 12 |
# Model Card for Model ID
|