Instructions to use ibokajordan/MT5_rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibokajordan/MT5_rag with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ibokajordan/MT5_rag") model = AutoModelForMultimodalLM.from_pretrained("ibokajordan/MT5_rag") - Notebooks
- Google Colab
- Kaggle
MT5_rag
This model is a fine-tuned version of mukayese/mt5-base-turkish-summarization 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Downloads last month
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Model tree for ibokajordan/MT5_rag
Base model
google/mt5-base Finetuned
mukayese/mt5-base-turkish-summarization