Instructions to use Yannis98/squad_albert_finetuned3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/squad_albert_finetuned3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Yannis98/squad_albert_finetuned3")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/squad_albert_finetuned3") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/squad_albert_finetuned3") - Notebooks
- Google Colab
- Kaggle
squad_albert_finetuned3
This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset.
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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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: linear
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 7
Model tree for Yannis98/squad_albert_finetuned3
Base model
albert/albert-base-v2