
# sinhala-gpt-lyrics

This particular model has undergone fine-tuning based on the [gpt2](https://huggingface.co/gpt2) architecture, utilizing a dataset of around 500k Sinhala lyrics from various sources. 
This Model is not sophisticated at all. Created while following one of fine-tuning docs.

## Usage Details

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

tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-gpt-lyrics")
model = AutoModelForCausalLM.from_pretrained("Ransaka/sinhala-gpt-lyrics")
generator = pipeline('text-generation',model=model, tokenizer=tokenizer)
generator("දුර") #දුර ඈත පාසැල් වියේ.
```
or using git
```bash
git lfs install
git clone https://huggingface.co/Ransaka/sinhala-gpt-lyrics
```

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3015        | 1.0   | 15323 | 2.3498             |
| 1.8582        | 2.0   | 30646 | 1.9921             |
| 1.5491        | 3.0   | 45969 | 1.9376             |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "\u0db8\u0db8"}, {"text": "\u0db8\u0dbd\u0dca"}, {"text": "\u0d94\u0db6"}, {"text": "\u0dad\u0db1\u0dd2\u0dc0\u0dd3\u0dbd\u0dcf"}, {"text": "\u0dc4\u0dd3\u0db1"}], "model-index": [{"name": "sinhala-gpt-lyrics", "results": []}]}
transformers pytorch tensorboard gpt2 text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

sinhala-gpt-lyrics
==================


This particular model has undergone fine-tuning based on the gpt2 architecture, utilizing a dataset of around 500k Sinhala lyrics from various sources.
This Model is not sophisticated at all. Created while following one of fine-tuning docs.


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 #generated_from_trainer #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 #generated_from_trainer #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