Feature Extraction
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use s3h/finetuned-arabert-gec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s3h/finetuned-arabert-gec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="s3h/finetuned-arabert-gec")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("s3h/finetuned-arabert-gec") model = AutoModel.from_pretrained("s3h/finetuned-arabert-gec") - Notebooks
- Google Colab
- Kaggle
s3h/finetuned-arabert-gec
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: -0.1214
- Train Pooler Output Loss: -0.1214
- Validation Loss: -0.1303
- Validation Pooler Output Loss: -0.1303
- Epoch: 2
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
Training results
| Train Loss | Train Pooler Output Loss | Validation Loss | Validation Pooler Output Loss | Epoch |
|---|---|---|---|---|
| -0.0492 | -0.0492 | -0.0918 | -0.0918 | 0 |
| -0.0949 | -0.0949 | -0.1146 | -0.1146 | 1 |
| -0.1214 | -0.1214 | -0.1303 | -0.1303 | 2 |
Framework versions
- Transformers 4.14.1
- TensorFlow 2.6.2
- Datasets 1.17.0
- Tokenizers 0.10.3
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