Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ahmed792002/arabic_sentiment_reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmed792002/arabic_sentiment_reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmed792002/arabic_sentiment_reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmed792002/arabic_sentiment_reviews") model = AutoModelForSequenceClassification.from_pretrained("ahmed792002/arabic_sentiment_reviews") - Notebooks
- Google Colab
- Kaggle
arabic_sentiment_reviews
This model is a fine-tuned version of ahmed792002/arabic_sentiment_reviews on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3165
- Accuracy: 0.8770
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3547 | 1.0 | 8250 | 0.3479 | 0.8673 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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