Instructions to use BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6") model = AutoModelForQuestionAnswering.from_pretrained("BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6") - Notebooks
- Google Colab
- Kaggle
roberta-base-finetuned-squad_nlp-course-chapter7-section6
This model is a fine-tuned version of roberta-base 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
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Model tree for BanUrsus/roberta-base-finetuned-squad_nlp-course-chapter7-section6
Base model
FacebookAI/roberta-base