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# gptneo-txt2ARXMLv1.2

This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1490

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3201        | 0.99  | 45   | 0.3718          |
| 0.2257        | 1.98  | 90   | 0.2754          |
| 0.2671        | 3.0   | 136  | 0.2291          |
| 0.1632        | 3.99  | 181  | 0.1854          |
| 0.1044        | 4.96  | 225  | 0.1490          |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gptneo-txt2ARXMLv1.2", "results": []}]}
transformers pytorch tensorboard gpt_neo text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

gptneo-txt2ARXMLv1.2
====================


This model is a fine-tuned version of EleutherAI/gpt-neo-125m on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1490


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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2