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# tulio-gpt-neo

This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6494
- Accuracy: 0.6498

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 13
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0707        | 0.06  | 2000  | 1.8760          | 0.6169   |
| 2.0158        | 0.12  | 4000  | 1.8242          | 0.6244   |
| 1.9811        | 0.17  | 6000  | 1.7930          | 0.6289   |
| 1.9313        | 0.23  | 8000  | 1.7695          | 0.6320   |
| 1.9437        | 0.29  | 10000 | 1.7510          | 0.6349   |
| 1.9157        | 0.35  | 12000 | 1.7363          | 0.6371   |
| 1.9071        | 0.41  | 14000 | 1.7227          | 0.6388   |
| 1.871         | 0.47  | 16000 | 1.7100          | 0.6409   |
| 1.8679        | 0.52  | 18000 | 1.7012          | 0.6419   |
| 1.848         | 0.58  | 20000 | 1.6904          | 0.6440   |
| 1.8353        | 0.64  | 22000 | 1.6836          | 0.6449   |
| 1.8603        | 0.7   | 24000 | 1.6768          | 0.6460   |
| 1.844         | 0.76  | 26000 | 1.6699          | 0.6468   |
| 1.8396        | 0.81  | 28000 | 1.6699          | 0.6464   |
| 1.8095        | 0.87  | 30000 | 1.6592          | 0.6483   |
| 1.8149        | 0.93  | 32000 | 1.6553          | 0.6488   |
| 1.8098        | 0.99  | 34000 | 1.6494          | 0.6498   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "tulio-gpt-neo", "results": []}]}
transformers pytorch gpt_neo text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

tulio-gpt-neo
=============


This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6494
* Accuracy: 0.6498


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 13
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0+cu116
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 13
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0+cu116
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 13
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0+cu116
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 13
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.0+cu116
* Datasets 2.7.1
* Tokenizers 0.13.2