distilgpt2-nepali

distilgpt2-nepali

This model is pre-trained on nepalitext dataset consisting of over 13 million Nepali text sequences using a Causal language modeling (CLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization similar to XLM-ROBERTa and trains distilgpt2 for language modeling.

It achieves the following results on the evaluation set:

Model description

Refer to original distilgpt2

Intended uses & limitations

This raw model can be used for Nepali text generation and intends to be fine-tuned on Nepali language focused 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 and may not perform satisfactorily on shorter sequences.

Usage

This model can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:

>>> from transformers import pipeline, set_seed
>>> set_seed(42)
>>> generator = pipeline('text-generation', model='Sakonii/distilgpt2-nepali')
>>> generator("नेपालका धेरैजसो चाडपर्वहरूमध्ये,", max_length=30, num_return_sequences=5)

Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, तिहार र छठपर्व विशेष रूपमा मनाइने भएकाले नेपाली मौलिक पर्व पनि हो । हिन्दू धर्म र संस्कृतिक... काठमाडौं ।'},
 {'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, तिहारको मुख्य दिन आज साँझ अस्ताउँदो सूर्यलाई अर्घ्य दिइएको छ । वैदिक विधि...विस्तृतमा पढ्नुस् काठमाडौं । नेपाल चिकित्सक संघका'},
 {'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, चाडपर्व, विवाह,... नेपाली काँग्रेसका प्रवक्ता विश्वप्रकाश शर्माले पार्टीभित्र आन्तरिक झगडा हुने निश्चित भएको र गुटबन्दीका कारण चुनावमा हार बेहोर्नु'},
 {'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, दशैं नेपालीहरूको मौलिक पर्वका रूपमा मनाउँछन् । नेपालीहरूको दोस्रो महान् पर्व तिहार हो । तिहारले दाजुभाइ तथा दिदीबहिनीहरूको बीचमा प्रगाढ सम्बन्ध स्थापित'},
 {'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, माघे संक्रान्ति र माघे संक्रान्तिमा माघे संक्रान्तिमा मात्र नभएर फागुन महिनाभर नै विशेष महत्व रहने गरेको छ । काठमाडौं ।'}]

Here is how we can use the model to get the features of a given text in PyTorch:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('Sakonii/distilgpt2-nepali')
model = AutoModelForCausalLM.from_pretrained('Sakonii/distilgpt2-nepali')

# prepare input
text = "चाहिएको text यता राख्नु होला।"
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)

Training data

This model is trained on nepalitext language modeling dataset which combines the datasets: OSCAR , cc100 and a set of scraped Nepali articles on Wikipedia. As for training the language model, the texts are tokenized using Sentence Piece Model (SPM), a vocabulary size of 24,576 and texts are are grouped to a block of 512 tokens.

Training procedure

The model is trained with the same configuration as the original distilgpt2; but with 512 tokens per instance, 12 instances per batch, and around 188.8K training steps.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.17.0
• Pytorch 1.9.1
• Datasets 2.0.0
• Tokenizers 0.11.6