
# 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

{"language": "ne", "license": "mit", "tags": ["generated_from_trainer", "gpt2", "ne"], "datasets": "Oscar", "widget": [{"text": "\u0917\u0930\u094d\u092e\u093f \u092e\u094c\u0938\u092e\u092e\u093e \u091a\u093f\u0938\u094b \u0916\u093e\u0928\u0947"}]}
transformers pytorch safetensors gpt2 text-generation generated_from_trainer ne dataset:Oscar license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #ne #dataset-Oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# gpt2-medium-ne

This model is a fine-tuned version of 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.




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



and in TensorFlow:




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

# gpt2-medium-ne

This model is a fine-tuned version of 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.




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



and in TensorFlow:




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
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #ne #dataset-Oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # gpt2-medium-ne

This model is a fine-tuned version of 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.




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



and in TensorFlow:




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
passage: TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #ne #dataset-Oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# gpt2-medium-ne

This model is a fine-tuned version of 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.




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



and in TensorFlow:




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