Instructions to use rizukim/albert-base-v2-finetuned-squad-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rizukim/albert-base-v2-finetuned-squad-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rizukim/albert-base-v2-finetuned-squad-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rizukim/albert-base-v2-finetuned-squad-v2") model = AutoModelForQuestionAnswering.from_pretrained("rizukim/albert-base-v2-finetuned-squad-v2") - Notebooks
- Google Colab
- Kaggle
albert-base-v2-finetuned-squad-v2
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8436
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8137 | 1.0 | 8248 | 0.8436 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for rizukim/albert-base-v2-finetuned-squad-v2
Base model
albert/albert-base-v2