Instructions to use ngocnamk3er/dsi_transformers_vault_t5_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ngocnamk3er/dsi_transformers_vault_t5_large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ngocnamk3er/dsi_transformers_vault_t5_large") model = AutoModelForSeq2SeqLM.from_pretrained("ngocnamk3er/dsi_transformers_vault_t5_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dsi_transformers_vault_t5_large
This model is a fine-tuned version of t5-large 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
Training results
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.20.3
- Downloads last month
- 8
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Model tree for ngocnamk3er/dsi_transformers_vault_t5_large
Base model
google-t5/t5-large