Instructions to use RyyDer/I-BERT_SQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyyDer/I-BERT_SQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="RyyDer/I-BERT_SQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("RyyDer/I-BERT_SQuAD") model = AutoModelForQuestionAnswering.from_pretrained("RyyDer/I-BERT_SQuAD") - Notebooks
- Google Colab
- Kaggle
I-BERT_SQuAD
This model is a fine-tuned version of kssteven/ibert-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2617
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: 4
- eval_batch_size: 4
- 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2253 | 1.0 | 1000 | 1.0948 |
| 0.7246 | 2.0 | 2000 | 1.1226 |
| 0.4614 | 3.0 | 3000 | 1.2617 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu118
- Datasets 3.4.1
- Tokenizers 0.21.1
- Downloads last month
- -
Model tree for RyyDer/I-BERT_SQuAD
Base model
kssteven/ibert-roberta-base