Instructions to use Naimul/banglabert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Naimul/banglabert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Naimul/banglabert-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Naimul/banglabert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("Naimul/banglabert-finetuned-squad") - Notebooks
- Google Colab
- Kaggle
banglabert-finetuned-squad
This model is a fine-tuned version of csebuetnlp/banglabert on the squad_bn dataset. It achieves the following results on the evaluation set:
- Loss: 1.4421
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3649 | 1.0 | 7397 | 1.4421 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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