Text Classification
Transformers
Safetensors
Arabic
bert
sentiment-analysis
arabic
text-embeddings-inference
Instructions to use TheMohanad1/marbert-arabic-sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheMohanad1/marbert-arabic-sentiment-analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TheMohanad1/marbert-arabic-sentiment-analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TheMohanad1/marbert-arabic-sentiment-analyzer") model = AutoModelForSequenceClassification.from_pretrained("TheMohanad1/marbert-arabic-sentiment-analyzer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "id2label": { | |
| "0": "سلبي", | |
| "1": "محايد", | |
| "2": "إيجابي" | |
| }, | |
| "label2id": { | |
| "سلبي": 0, | |
| "محايد": 1, | |
| "إيجابي": 2 | |
| }, | |
| "num_labels": 3, | |
| "task": "sentiment_analysis", | |
| "language": "arabic", | |
| "model_name": "UBC-NLP/MARBERTv2", | |
| "max_length": 100, | |
| "training_samples": 32970, | |
| "validation_samples": 7065, | |
| "test_samples": 7065, | |
| "train_runtime": 508.1498, | |
| "train_samples_per_second": 324.412, | |
| "train_loss": 0.4654327607154846, | |
| "data_info": { | |
| "original_samples": 45948, | |
| "cleaned_samples": 45948, | |
| "balanced_samples": 47100, | |
| "avg_text_length": 151.8227176220807, | |
| "sentiment_distribution": { | |
| "1": 15700, | |
| "2": 15700, | |
| "0": 15700 | |
| } | |
| } | |
| } |