from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-gpt2")
model = AutoModelForCausalLM.from_pretrained("Ransaka/sinhala-gpt2")
generator("දුර")

or using git

bash

git lfs install
git clone https://huggingface.co/Ransaka/sinhala-gpt2

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.0233	1.0	15323	2.3348
1.6938	2.0	30646	1.8377
1.4938	3.0	45969	1.6498

Framework versions

Transformers 4.26.1
Pytorch 1.13.0
Datasets 2.1.0
Tokenizers 0.13.2