Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use smerchi/darija_test8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smerchi/darija_test8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smerchi/darija_test8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smerchi/darija_test8") model = AutoModelForSequenceClassification.from_pretrained("smerchi/darija_test8") - Notebooks
- Google Colab
- Kaggle
darija_test8
This model is a fine-tuned version of SI2M-Lab/DarijaBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 1.0
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 92 | 0.0000 | 1.0 |
| No log | 2.0 | 184 | 0.0 | 1.0 |
| No log | 3.0 | 276 | 0.0 | 1.0 |
| No log | 4.0 | 368 | 0.0 | 1.0 |
| No log | 5.0 | 460 | 0.0 | 1.0 |
| 0.0 | 6.0 | 552 | 0.0 | 1.0 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.0
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SI2M-Lab/DarijaBERT