
<!-- 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. -->

# gpt2_finetuned_wolfram

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2595

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.0   | 113   | 6.3789          |
| No log        | 2.0   | 226   | 6.0746          |
| No log        | 3.0   | 339   | 5.7649          |
| No log        | 4.0   | 452   | 5.4453          |
| 5.9875        | 5.0   | 565   | 5.2142          |
| 5.9875        | 6.0   | 678   | 5.0967          |
| 5.9875        | 7.0   | 791   | 5.0143          |
| 5.9875        | 8.0   | 904   | 4.9429          |
| 4.5754        | 9.0   | 1017  | 4.8936          |
| 4.5754        | 10.0  | 1130  | 4.8722          |
| 4.5754        | 11.0  | 1243  | 4.8700          |
| 4.5754        | 12.0  | 1356  | 4.8362          |
| 4.5754        | 13.0  | 1469  | 4.8246          |
| 4.0366        | 14.0  | 1582  | 4.8242          |
| 4.0366        | 15.0  | 1695  | 4.8149          |
| 4.0366        | 16.0  | 1808  | 4.8062          |
| 4.0366        | 17.0  | 1921  | 4.8065          |
| 3.8118        | 18.0  | 2034  | 4.8288          |
| 3.8118        | 19.0  | 2147  | 4.8035          |
| 3.8118        | 20.0  | 2260  | 4.8009          |
| 3.8118        | 21.0  | 2373  | 4.7835          |
| 3.8118        | 22.0  | 2486  | 4.7865          |
| 3.6394        | 23.0  | 2599  | 4.7833          |
| 3.6394        | 24.0  | 2712  | 4.7776          |
| 3.6394        | 25.0  | 2825  | 4.8030          |
| 3.6394        | 26.0  | 2938  | 4.7684          |
| 3.5105        | 27.0  | 3051  | 4.7724          |
| 3.5105        | 28.0  | 3164  | 4.7803          |
| 3.5105        | 29.0  | 3277  | 4.7792          |
| 3.5105        | 30.0  | 3390  | 4.8027          |
| 3.38          | 31.0  | 3503  | 4.8000          |
| 3.38          | 32.0  | 3616  | 4.8046          |
| 3.38          | 33.0  | 3729  | 4.7751          |
| 3.38          | 34.0  | 3842  | 4.7774          |
| 3.38          | 35.0  | 3955  | 4.7733          |
| 3.2382        | 36.0  | 4068  | 4.7886          |
| 3.2382        | 37.0  | 4181  | 4.7892          |
| 3.2382        | 38.0  | 4294  | 4.7876          |
| 3.2382        | 39.0  | 4407  | 4.7965          |
| 3.1022        | 40.0  | 4520  | 4.7879          |
| 3.1022        | 41.0  | 4633  | 4.7829          |
| 3.1022        | 42.0  | 4746  | 4.7884          |
| 3.1022        | 43.0  | 4859  | 4.7845          |
| 3.1022        | 44.0  | 4972  | 4.8193          |
| 2.9571        | 45.0  | 5085  | 4.7947          |
| 2.9571        | 46.0  | 5198  | 4.7968          |
| 2.9571        | 47.0  | 5311  | 4.7894          |
| 2.9571        | 48.0  | 5424  | 4.7892          |
| 2.7555        | 49.0  | 5537  | 4.7914          |
| 2.7555        | 50.0  | 5650  | 4.8099          |
| 2.7555        | 51.0  | 5763  | 4.8029          |
| 2.7555        | 52.0  | 5876  | 4.8000          |
| 2.7555        | 53.0  | 5989  | 4.8092          |
| 2.5656        | 54.0  | 6102  | 4.8111          |
| 2.5656        | 55.0  | 6215  | 4.8257          |
| 2.5656        | 56.0  | 6328  | 4.8109          |
| 2.5656        | 57.0  | 6441  | 4.8457          |
| 2.3501        | 58.0  | 6554  | 4.8428          |
| 2.3501        | 59.0  | 6667  | 4.8519          |
| 2.3501        | 60.0  | 6780  | 4.8652          |
| 2.3501        | 61.0  | 6893  | 4.8788          |
| 2.141         | 62.0  | 7006  | 4.8910          |
| 2.141         | 63.0  | 7119  | 4.8928          |
| 2.141         | 64.0  | 7232  | 4.9112          |
| 2.141         | 65.0  | 7345  | 4.9219          |
| 2.141         | 66.0  | 7458  | 4.9403          |
| 1.9122        | 67.0  | 7571  | 4.9585          |
| 1.9122        | 68.0  | 7684  | 4.9726          |
| 1.9122        | 69.0  | 7797  | 4.9904          |
| 1.9122        | 70.0  | 7910  | 5.0118          |
| 1.7176        | 71.0  | 8023  | 5.0129          |
| 1.7176        | 72.0  | 8136  | 5.0303          |
| 1.7176        | 73.0  | 8249  | 5.0529          |
| 1.7176        | 74.0  | 8362  | 5.0610          |
| 1.7176        | 75.0  | 8475  | 5.0821          |
| 1.5292        | 76.0  | 8588  | 5.0931          |
| 1.5292        | 77.0  | 8701  | 5.1154          |
| 1.5292        | 78.0  | 8814  | 5.1319          |
| 1.5292        | 79.0  | 8927  | 5.1394          |
| 1.3843        | 80.0  | 9040  | 5.1529          |
| 1.3843        | 81.0  | 9153  | 5.1711          |
| 1.3843        | 82.0  | 9266  | 5.1802          |
| 1.3843        | 83.0  | 9379  | 5.1952          |
| 1.3843        | 84.0  | 9492  | 5.2088          |
| 1.2643        | 85.0  | 9605  | 5.2170          |
| 1.2643        | 86.0  | 9718  | 5.2160          |
| 1.2643        | 87.0  | 9831  | 5.2267          |
| 1.2643        | 88.0  | 9944  | 5.2346          |
| 1.1928        | 89.0  | 10057 | 5.2418          |
| 1.1928        | 90.0  | 10170 | 5.2463          |
| 1.1928        | 91.0  | 10283 | 5.2505          |
| 1.1928        | 92.0  | 10396 | 5.2522          |
| 1.1556        | 93.0  | 10509 | 5.2538          |
| 1.1556        | 94.0  | 10622 | 5.2557          |
| 1.1556        | 95.0  | 10735 | 5.2566          |
| 1.1556        | 96.0  | 10848 | 5.2585          |
| 1.1556        | 97.0  | 10961 | 5.2594          |
| 1.1268        | 98.0  | 11074 | 5.2596          |
| 1.1268        | 99.0  | 11187 | 5.2595          |
| 1.1268        | 100.0 | 11300 | 5.2595          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2_finetuned_wolfram", "results": []}]}
transformers pytorch tensorboard safetensors gpt2 text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

gpt2\_finetuned\_wolfram
========================


This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 5.2595


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2