license: apache-2.0
tags:
- generated_from_trainer
datasets: Sakonii/nepalitext-language-model-dataset
widget:
- text: नेपाल र भारतबीच
example_title: Example 1
- text: प्रधानमन्त्री
example_title: Example 2
- text: 'दस वर्ष लामो '
example_title: Example 3
- text: 'जापानमा आज '
example_title: Example 4
- text: नेपालका धेरैजसो चाडपर्वहरूमध्ये,
example_title: Example 5
model-index:
- name: distilgpt2-nepali
results: []
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:
| Training Loss | Validation Loss | Perplexity |
|---|---|---|
| 3.3968 | 3.2705 | 26.3245 |
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
| Training Loss | Epoch | Step | Validation Loss | Perplexity |
|---|---|---|---|---|
| 3.7645 | 1.0 | 94395 | 3.6291 | 37.6789 |
| 3.5857 | 2.0 | 188790 | 3.4442 | 31.3182 |
| 3.505 | 3.0 | 283185 | 3.3749 | 29.2214 |
| 3.4688 | 4.0 | 377580 | 3.3439 | 28.3294 |
| 3.3968 | 5.0 | 471975 | 3.2705 | 26.3245 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.11.6