
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# jankgpt

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: 2.8181

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1021        | 0.08  | 100  | 3.0845          |
| 3.0967        | 0.16  | 200  | 3.0801          |
| 3.0836        | 0.24  | 300  | 3.0788          |
| 3.0587        | 0.32  | 400  | 3.0834          |
| 3.0541        | 0.4   | 500  | 3.0850          |
| 3.0305        | 0.48  | 600  | 3.0981          |
| 3.0016        | 0.55  | 700  | 3.1095          |
| 2.9926        | 0.63  | 800  | 3.1197          |
| 2.9714        | 0.71  | 900  | 3.1251          |
| 2.9671        | 0.79  | 1000 | 3.1399          |
| 2.9813        | 0.87  | 1100 | 3.1221          |
| 3.0625        | 0.95  | 1200 | 3.0943          |
| 3.035         | 1.03  | 1300 | 3.0655          |
| 2.9603        | 1.11  | 1400 | 3.0517          |
| 2.9562        | 1.19  | 1500 | 3.0356          |
| 2.927         | 1.27  | 1600 | 3.0243          |
| 2.936         | 1.35  | 1700 | 3.0045          |
| 2.9404        | 1.43  | 1800 | 2.9877          |
| 2.9226        | 1.51  | 1900 | 2.9740          |
| 2.9015        | 1.59  | 2000 | 2.9549          |
| 2.8955        | 1.66  | 2100 | 2.9432          |
| 2.8738        | 1.74  | 2200 | 2.9272          |
| 2.8663        | 1.82  | 2300 | 2.9138          |
| 2.8515        | 1.9   | 2400 | 2.8980          |
| 2.8433        | 1.98  | 2500 | 2.8859          |
| 2.7071        | 2.06  | 2600 | 2.8801          |
| 2.6697        | 2.14  | 2700 | 2.8721          |
| 2.6623        | 2.22  | 2800 | 2.8631          |
| 2.6561        | 2.3   | 2900 | 2.8551          |
| 2.6604        | 2.38  | 3000 | 2.8465          |
| 2.6372        | 2.46  | 3100 | 2.8402          |
| 2.6279        | 2.54  | 3200 | 2.8320          |
| 2.6209        | 2.62  | 3300 | 2.8264          |
| 2.6192        | 2.69  | 3400 | 2.8226          |
| 2.605         | 2.77  | 3500 | 2.8204          |
| 2.6054        | 2.85  | 3600 | 2.8187          |
| 2.6183        | 2.93  | 3700 | 2.8181          |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+rocm5.2
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "jankgpt", "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 

jankgpt
=======


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


* Loss: 2.8181


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+rocm5.2
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+rocm5.2
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+rocm5.2
* Datasets 2.12.0
* Tokenizers 0.13.3