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bangla-sentiment-analysis-v2_2025
DipsankarSinha/bangla-sentiment-analysis-v2_2025
bangla-sentiment-analysis-v2_2025 is a text classification model from DipsankarSinha. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- electra
- Access
- Public
- Created
- Mar 20, 2025
- Updated
- Mar 20, 2025
- SHA
- 2eff7d48
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
repo_name
This model is a fine-tuned version of csebuetnlp/banglabert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4297
- Accuracy: 0.8705
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 645
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3313 | 0.4735 | 1000 | 0.3475 | 0.8446 |
| 0.3384 | 0.9470 | 2000 | 0.3331 | 0.8630 |
| 0.2469 | 1.4205 | 3000 | 0.3431 | 0.8615 |
| 0.2392 | 1.8939 | 4000 | 0.3347 | 0.8705 |
| 0.1737 | 2.3674 | 5000 | 0.4186 | 0.8659 |
| 0.1481 | 2.8409 | 6000 | 0.4297 | 0.8705 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- electra
- Created
- Mar 20, 2025
- Updated
- Mar 20, 2025