Instructions to use UmairHere/urdu-news-5class-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UmairHere/urdu-news-5class-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UmairHere/urdu-news-5class-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UmairHere/urdu-news-5class-model") model = AutoModelForSequenceClassification.from_pretrained("UmairHere/urdu-news-5class-model") - Notebooks
- Google Colab
- Kaggle
urdu-news-5class-model
This model is a fine-tuned version of urduhack/roberta-urdu-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3072
- Accuracy: 0.9760
- F1: 0.9760
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 437 | 0.2068 | 0.9728 | 0.9728 |
| 0.3265 | 2.0 | 874 | 0.1890 | 0.9769 | 0.9768 |
| 0.1342 | 3.0 | 1311 | 0.2936 | 0.9736 | 0.9736 |
| 0.0720 | 4.0 | 1748 | 0.2746 | 0.9777 | 0.9777 |
| 0.0265 | 5.0 | 2185 | 0.3072 | 0.9760 | 0.9760 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for UmairHere/urdu-news-5class-model
Base model
urduhack/roberta-urdu-small