Instructions to use sayed9/BanglaBERT-Region-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sayed9/BanglaBERT-Region-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sayed9/BanglaBERT-Region-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sayed9/BanglaBERT-Region-Classifier") model = AutoModelForSequenceClassification.from_pretrained("sayed9/BanglaBERT-Region-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
BanglaBERT-Region-Classifier
This model is a fine-tuned version of sagorsarker/bangla-bert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0445
- Accuracy: 0.7693
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: 8
- eval_batch_size: 8
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8054 | 1.0 | 1435 | 0.6641 | 0.7288 |
| 0.5173 | 2.0 | 2870 | 0.6709 | 0.7490 |
| 0.3177 | 3.0 | 4305 | 0.7642 | 0.7693 |
| 0.2033 | 4.0 | 5740 | 1.0445 | 0.7693 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1
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Model tree for sayed9/BanglaBERT-Region-Classifier
Base model
sagorsarker/bangla-bert-base