Instructions to use duraad/nep-spell-hft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duraad/nep-spell-hft with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("duraad/nep-spell-hft") model = AutoModelForSeq2SeqLM.from_pretrained("duraad/nep-spell-hft") - Notebooks
- Google Colab
- Kaggle
nep-spell-hft
This model was trained from scratch 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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