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# gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v4

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step   | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 0.1593        | 21.46  | 5000   | 0.0853          |
| 0.0847        | 42.92  | 10000  | 0.1198          |
| 0.0406        | 64.38  | 15000  | 0.0613          |
| 0.0324        | 85.83  | 20000  | 0.0307          |
| 0.0238        | 107.3  | 25000  | 0.0211          |
| 0.0207        | 128.75 | 30000  | 0.0184          |
| 0.0193        | 150.21 | 35000  | 0.0176          |
| 0.0185        | 171.67 | 40000  | 0.0171          |
| 0.018         | 193.13 | 45000  | 0.0170          |
| 0.0177        | 214.59 | 50000  | 0.0167          |
| 0.0174        | 236.05 | 55000  | 0.0167          |
| 0.0172        | 257.51 | 60000  | 0.0166          |
| 0.017         | 278.97 | 65000  | 0.0165          |
| 0.0169        | 300.43 | 70000  | 0.0164          |
| 0.0168        | 321.89 | 75000  | 0.0164          |
| 0.0167        | 343.35 | 80000  | 0.0163          |
| 0.0166        | 364.8  | 85000  | 0.0163          |
| 0.0165        | 386.27 | 90000  | 0.0163          |
| 0.0164        | 407.72 | 95000  | 0.0162          |
| 0.0164        | 429.18 | 100000 | 0.0162          |
| 0.0163        | 450.64 | 105000 | 0.0162          |
| 0.0163        | 472.1  | 110000 | 0.0162          |
| 0.0163        | 493.56 | 115000 | 0.0162          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v4", "results": []}]}
transformers pytorch gpt2 text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v4
=============================================


This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0162


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: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 500
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1.post200
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1.post200
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1.post200
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 500
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1.post200
* Datasets 2.9.0
* Tokenizers 0.13.2