Instructions to use rkgupta3/bart-base-text-to-sql-smoke-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rkgupta3/bart-base-text-to-sql-smoke-test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rkgupta3/bart-base-text-to-sql-smoke-test") model = AutoModelForSeq2SeqLM.from_pretrained("rkgupta3/bart-base-text-to-sql-smoke-test") - Notebooks
- Google Colab
- Kaggle
Quick Links
bart-base-text-to-sql-smoke-test
This model is a fine-tuned version of facebook/bart-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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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
Training results
Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
- Downloads last month
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Model tree for rkgupta3/bart-base-text-to-sql-smoke-test
Base model
facebook/bart-base
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rkgupta3/bart-base-text-to-sql-smoke-test") model = AutoModelForSeq2SeqLM.from_pretrained("rkgupta3/bart-base-text-to-sql-smoke-test")