Instructions to use aku47z/mt5-small-nepali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aku47z/mt5-small-nepali with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aku47z/mt5-small-nepali") model = AutoModelForSeq2SeqLM.from_pretrained("aku47z/mt5-small-nepali", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mt5-small-nepali
This model is a fine-tuned version of google/mt5-small 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
Framework versions
- Transformers 4.53.0
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.2
- Downloads last month
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