updated readme.md

README.md

@@ -1,30 +1,69 @@
 ---
+language: ne
 license: mit
 tags:
 - generated_from_trainer
-model-index:
-- name: gpt2-medium-ne
-  results: []
+- gpt2
+- ne
+datasets: Oscar
+widget:
+-  text: "गर्मि मौसममा चिसो खाने"
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # gpt2-medium-ne
 
-This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
+This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Oscar Dataset.
 
 ## Model description
 
-More information needed
+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)
+```
 
-## Intended uses & limitations
 
 More information needed
 
 ## Training and evaluation data
 
-More information needed
+Training data contains 197k Nepali sentences.
 
 ## Training procedure