Instructions to use UmairHere/urdu-sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UmairHere/urdu-sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UmairHere/urdu-sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UmairHere/urdu-sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("UmairHere/urdu-sentiment-model") - Notebooks
- Google Colab
- Kaggle
urdu-sentiment-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: 4.0168
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 |
|---|---|---|---|
| No log | 1.0 | 160 | 1.6822 |
| No log | 2.0 | 320 | 1.6106 |
| No log | 3.0 | 480 | 1.7488 |
| 1.2717 | 4.0 | 640 | 2.1106 |
| 1.2717 | 5.0 | 800 | 2.3260 |
| 1.2717 | 6.0 | 960 | 2.8394 |
| 0.3719 | 7.0 | 1120 | 3.3204 |
| 0.3719 | 8.0 | 1280 | 3.7366 |
| 0.3719 | 9.0 | 1440 | 3.9275 |
| 0.0962 | 10.0 | 1600 | 4.0168 |
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-sentiment-model
Base model
urduhack/roberta-urdu-small