Instructions to use ArisuNguyen/bart_finetuned_5e_5_7epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArisuNguyen/bart_finetuned_5e_5_7epoch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ArisuNguyen/bart_finetuned_5e_5_7epoch")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ArisuNguyen/bart_finetuned_5e_5_7epoch") model = AutoModelForQuestionAnswering.from_pretrained("ArisuNguyen/bart_finetuned_5e_5_7epoch") - Notebooks
- Google Colab
- Kaggle
bart_finetuned_5e_5_7epoch
This model is a fine-tuned version of ArisuNguyen/bart_finetuned_5e_5 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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