readme: ---
  language: ne
  license: mit
  tags:
  - generated_from_trainer
  - gpt2
  - ne
  datasets: Oscar
  widget:
  -  text: "गर्मि मौसममा चिसो खाने"
  ---
  
  # gpt2-medium-ne
  
  This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Oscar Dataset.
  
  ## Model description
  
  This model is trained on Oscar Nepali Dataset.
  
  ## How to use
  
  You can use this model directly with a pipeline for text generation.
  
  ```python
  >>> from transformers import pipeline, set_seed
  >>> generator = pipeline('text-generation', model='Someman/gpt2-medium-ne')
  >>> set_seed(42)
  >>> generator("उच्च अदालतले बिहीबार दिएको आदेशले", max_length=30, num_return_sequences=5)
  
  [{'generated_text': 'उच्च अदालतले बिहीबार दिएको आदेशले महिनात्रि'},
   {'generated_text': 'उच्च अदालतले बिहीबार दिएको आदेशले बिहानैदे'},
   {'generated_text': 'उच्च अदालतले बिहीबार दिएको आदेशले गिरिजाली'},
   {'generated_text': 'उच्च अदालतले बिहीबार दिएको आदेशले गरेको प्रथम त'},
   {'generated_text': 'उच्च अदालतले बिहीबार दिएको आदेशले कुनै साथी'}]
  ```
  
  
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import GPT2Tokenizer, GPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('Someman/gpt2-medium-ne')
  model = GPT2Model.from_pretrained('Someman/gpt2-medium-ne')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in TensorFlow:
  
  ```python
  from transformers import GPT2Tokenizer, TFGPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('Someman/gpt2-medium-ne')
  model = TFGPT2Model.from_pretrained('Someman/gpt2-medium-ne')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  
  More information needed
  
  ## Training and evaluation data
  
  Training data contains 197k Nepali sentences.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 32
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 1
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  