distilgpt2-nepali-qa

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distilgpt2-nepali-patrakar-qa

This model is a fine-tuned version of Sakonii/distilgpt2-nepali on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.9077

Model description

Refer to original distilgpt2

Intended uses & limitations

This marginally fine-tuned model can be used for Nepali text generation and possibly question answering and intends to be fine-tuned on Nepali language focused generative downstream task. The language model being trained on a data with texts grouped to a block size of 512, it handles text sequence up to 512 tokens.

Training procedure

The model is trained with the same configuration as the original distilgpt2; but with 512 tokens per instance, 72 instances per batch, and around 34.14K training steps (excluding the pre-training with CLM Objective).

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 72
• eval_batch_size: 72
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3