Text Classification
Transformers
Safetensors
Arabic
bert
Generated from Trainer
text-embeddings-inference
Instructions to use 7beshoyarnest/arabic-sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 7beshoyarnest/arabic-sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="7beshoyarnest/arabic-sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("7beshoyarnest/arabic-sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("7beshoyarnest/arabic-sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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### Framework versions
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1609
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- Accuracy: 0.9424
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- F1: 0.9424
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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| 0.3225 | 1.0 | 588 | 0.2746 | 0.8922 | 0.8922 |
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| 0.1568 | 2.0 | 1176 | 0.1639 | 0.9417 | 0.9417 |
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| 0.1301 | 3.0 | 1764 | 0.1609 | 0.9424 | 0.9424 |
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### Framework versions
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