
# sinhala-gpt2

This particular model has undergone fine-tuning based on the [gpt2](https://huggingface.co/gpt2) architecture, utilizing a dataset of Sinhala NEWS from various sources.

## Training procedure
The model was trained for 12+ hours on Kaggle GPUs.

## Usage Details

```python
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-gpt2")
model = AutoModelForCausalLM.from_pretrained("Ransaka/sinhala-gpt2")
generator("දුර")
```
or using git
```bash
git lfs install
git clone https://huggingface.co/Ransaka/sinhala-gpt2
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0233        | 1.0   | 15323 | 2.3348             |
| 1.6938        | 2.0   | 30646 | 1.8377             |
| 1.4938        | 3.0   | 45969 | 1.6498             |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
{"language": ["si"], "license": "mit", "tags": ["pytorch", "gpt2"], "widget": [{"text": "\u0db8\u0dc4"}, {"text": "\u0dc3\u0d82\u0dc0\u0dd2\u0db0"}, {"text": "\u0daf\u0dd4\u0dbb\u0dca\u0dbd\u0db7"}, {"text": "\u0dad\u0db1\u0dd2\u0dc0\u0dd3\u0dbd\u0dcf"}, {"text": "\u0d94\u0db6"}], "model-index": [{"name": "sinhala-gpt2", "results": []}]}
transformers pytorch tensorboard gpt2 text-generation si license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #si #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

sinhala-gpt2
============


This particular model has undergone fine-tuning based on the gpt2 architecture, utilizing a dataset of Sinhala NEWS from various sources.


Training procedure
------------------


The model was trained for 12+ hours on Kaggle GPUs.


Usage Details
-------------


or using git


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #si #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #si #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2