### Overview

This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. 

## Model Specification


The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024
 
## How to Use
You can use this model directly with a pipeline for casual language modeling:

```py
from transformers import pipeline
generator = pipeline('text-generation', model='keshan/sinhala-gpt2')
generator("මම", max_length=50, num_return_sequences=5)
```

{"language": "si", "tags": ["Sinhala", "text-generation", "gpt2"], "datasets": ["mc4"]}
transformers pytorch tf jax tensorboard gpt2 feature-extraction Sinhala text-generation si dataset:mc4 endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #text-generation-inference #region-us 

### Overview

This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. 

## Model Specification


The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024
 
## How to Use
You can use this model directly with a pipeline for casual language modeling:



### Overview

This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. ## Model Specification


The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024 ## How to Use
You can use this model directly with a pipeline for casual language modeling:
TAGS
#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #text-generation-inference #region-us 
 ### Overview

This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. ## Model Specification


The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024 ## How to Use
You can use this model directly with a pipeline for casual language modeling:
passage: TAGS
#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #text-generation-inference #region-us 
### Overview

This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.## Model Specification


The model chosen for training is GPT2 with the following specifications:
 1. vocab_size=50257
 2. n_embd=768
 3. n_head=12
 4. n_layer=12
 5. n_positions=1024## How to Use
You can use this model directly with a pipeline for casual language modeling: