Instructions to use leomaurodesenv/roberta-soccer-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/roberta-soccer-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="leomaurodesenv/roberta-soccer-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/roberta-soccer-qa") model = AutoModelForQuestionAnswering.from_pretrained("leomaurodesenv/roberta-soccer-qa") - Notebooks
- Google Colab
- Kaggle
roberta-soccer-qa
This model is a fine-tuned version of deepset/roberta-base-squad2 on the None 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 100
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1
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