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# codeparrot-ds2

GPT-2 style trained on a filtered set of The Stack, specific to data science related code.  Things like pandas, numpy, matplotlib, etc.
- Loss: 1.0584

## 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: 200
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2038        | 0.01  | 500   | 2.1062          |
| 2.0551        | 0.02  | 1000  | 2.0109          |
| 1.9622        | 0.02  | 1500  | 1.9219          |
| 1.9512        | 0.03  | 2000  | 1.8461          |
| 1.8817        | 0.04  | 2500  | 1.7903          |
| 1.8341        | 0.05  | 3000  | 1.7401          |
| 1.7877        | 0.05  | 3500  | 1.7022          |
| 1.7586        | 0.06  | 4000  | 1.6694          |
| 1.7271        | 0.07  | 4500  | 1.6457          |
| 1.7034        | 0.08  | 5000  | 1.6193          |
| 1.6756        | 0.08  | 5500  | 1.5978          |
| 1.6576        | 0.09  | 6000  | 1.5772          |
| 1.6377        | 0.1   | 6500  | 1.5611          |
| 1.6211        | 0.11  | 7000  | 1.5453          |
| 1.6033        | 0.11  | 7500  | 1.5317          |
| 1.591         | 0.12  | 8000  | 1.5193          |
| 1.5765        | 0.13  | 8500  | 1.5053          |
| 1.5661        | 0.14  | 9000  | 1.4966          |
| 1.5548        | 0.15  | 9500  | 1.4846          |
| 1.5429        | 0.15  | 10000 | 1.4729          |
| 1.5347        | 0.16  | 10500 | 1.4641          |
| 1.5215        | 0.17  | 11000 | 1.4557          |
| 1.5151        | 0.18  | 11500 | 1.4454          |
| 1.5059        | 0.18  | 12000 | 1.4381          |
| 1.499         | 0.19  | 12500 | 1.4288          |
| 1.4906        | 0.2   | 13000 | 1.4210          |
| 1.4849        | 0.21  | 13500 | 1.4143          |
| 1.4765        | 0.21  | 14000 | 1.4085          |
| 1.4708        | 0.22  | 14500 | 1.4026          |
| 1.4602        | 0.23  | 15000 | 1.3936          |
| 1.4533        | 0.24  | 15500 | 1.3896          |
| 1.4523        | 0.25  | 16000 | 1.3818          |
| 1.4415        | 0.25  | 16500 | 1.3748          |
| 1.4417        | 0.26  | 17000 | 1.3701          |
| 1.4311        | 0.27  | 17500 | 1.3645          |
| 1.4282        | 0.28  | 18000 | 1.3585          |
| 1.4223        | 0.28  | 18500 | 1.3531          |
| 1.4165        | 0.29  | 19000 | 1.3473          |
| 1.4105        | 0.3   | 19500 | 1.3419          |
| 1.3993        | 0.31  | 20000 | 1.3374          |
| 1.4034        | 0.31  | 20500 | 1.3322          |
| 1.3982        | 0.32  | 21000 | 1.3278          |
| 1.3951        | 0.33  | 21500 | 1.3225          |
| 1.3806        | 0.34  | 22000 | 1.3180          |
| 1.3781        | 0.34  | 22500 | 1.3121          |
| 1.3761        | 0.35  | 23000 | 1.3082          |
| 1.3662        | 0.36  | 23500 | 1.3038          |
| 1.3631        | 0.37  | 24000 | 1.2995          |
| 1.3549        | 0.38  | 24500 | 1.2955          |
| 1.3577        | 0.38  | 25000 | 1.2912          |
| 1.3498        | 0.39  | 25500 | 1.2851          |
| 1.3428        | 0.4   | 26000 | 1.2807          |
| 1.342         | 0.41  | 26500 | 1.2768          |
| 1.3365        | 0.41  | 27000 | 1.2720          |
| 1.3313        | 0.42  | 27500 | 1.2678          |
| 1.3309        | 0.43  | 28000 | 1.2629          |
| 1.3221        | 0.44  | 28500 | 1.2594          |
| 1.3214        | 0.44  | 29000 | 1.2558          |
| 1.3099        | 0.45  | 29500 | 1.2510          |
| 1.31          | 0.46  | 30000 | 1.2449          |
| 1.31          | 0.47  | 30500 | 1.2414          |
| 1.305         | 0.48  | 31000 | 1.2390          |
| 1.2975        | 0.48  | 31500 | 1.2358          |
| 1.2882        | 0.49  | 32000 | 1.2311          |
| 1.2831        | 0.5   | 32500 | 1.2251          |
| 1.2836        | 0.51  | 33000 | 1.2212          |
| 1.2817        | 0.51  | 33500 | 1.2178          |
| 1.2772        | 0.52  | 34000 | 1.2130          |
| 1.2651        | 0.53  | 34500 | 1.2080          |
| 1.2683        | 0.54  | 35000 | 1.2048          |
| 1.2581        | 0.54  | 35500 | 1.1999          |
| 1.263         | 0.55  | 36000 | 1.1972          |
| 1.255         | 0.56  | 36500 | 1.1924          |
| 1.2466        | 0.57  | 37000 | 1.1884          |
| 1.2448        | 0.57  | 37500 | 1.1860          |
| 1.2413        | 0.58  | 38000 | 1.1804          |
| 1.2362        | 0.59  | 38500 | 1.1782          |
| 1.2309        | 0.6   | 39000 | 1.1732          |
| 1.2289        | 0.61  | 39500 | 1.1687          |
| 1.2208        | 0.61  | 40000 | 1.1649          |
| 1.2225        | 0.62  | 40500 | 1.1605          |
| 1.2178        | 0.63  | 41000 | 1.1555          |
| 1.208         | 0.64  | 41500 | 1.1533          |
| 1.2069        | 0.64  | 42000 | 1.1490          |
| 1.206         | 0.65  | 42500 | 1.1453          |
| 1.2013        | 0.66  | 43000 | 1.1414          |
| 1.2003        | 0.67  | 43500 | 1.1374          |
| 1.1867        | 0.67  | 44000 | 1.1337          |
| 1.187         | 0.68  | 44500 | 1.1302          |
| 1.188         | 0.69  | 45000 | 1.1270          |
| 1.179         | 0.7   | 45500 | 1.1237          |
| 1.1866        | 0.71  | 46000 | 1.1204          |
| 1.173         | 0.71  | 46500 | 1.1173          |
| 1.1706        | 0.72  | 47000 | 1.1134          |
| 1.1645        | 0.73  | 47500 | 1.1099          |
| 1.1641        | 0.74  | 48000 | 1.1063          |
| 1.1623        | 0.74  | 48500 | 1.1032          |
| 1.1561        | 0.75  | 49000 | 1.1006          |
| 1.1531        | 0.76  | 49500 | 1.0977          |
| 1.1569        | 0.77  | 50000 | 1.0950          |
| 1.1505        | 0.77  | 50500 | 1.0927          |
| 1.1473        | 0.78  | 51000 | 1.0902          |
| 1.1428        | 0.79  | 51500 | 1.0870          |
| 1.1412        | 0.8   | 52000 | 1.0844          |
| 1.1452        | 0.8   | 52500 | 1.0823          |
| 1.1391        | 0.81  | 53000 | 1.0805          |
| 1.1329        | 0.82  | 53500 | 1.0783          |
| 1.1295        | 0.83  | 54000 | 1.0764          |
| 1.125         | 0.84  | 54500 | 1.0746          |
| 1.1295        | 0.84  | 55000 | 1.0730          |
| 1.1247        | 0.85  | 55500 | 1.0711          |
| 1.1225        | 0.86  | 56000 | 1.0696          |
| 1.1235        | 0.87  | 56500 | 1.0680          |
| 1.1192        | 0.87  | 57000 | 1.0670          |
| 1.1189        | 0.88  | 57500 | 1.0654          |
| 1.1196        | 0.89  | 58000 | 1.0646          |
| 1.1152        | 0.9   | 58500 | 1.0635          |
| 1.1133        | 0.9   | 59000 | 1.0628          |
| 1.1126        | 0.91  | 59500 | 1.0619          |
| 1.1142        | 0.92  | 60000 | 1.0610          |
| 1.1112        | 0.93  | 60500 | 1.0605          |
| 1.1137        | 0.93  | 61000 | 1.0599          |
| 1.1127        | 0.94  | 61500 | 1.0595          |
| 1.1111        | 0.95  | 62000 | 1.0592          |
| 1.1121        | 0.96  | 62500 | 1.0588          |
| 1.1114        | 0.97  | 63000 | 1.0587          |
| 1.1121        | 0.97  | 63500 | 1.0585          |
| 1.1078        | 0.98  | 64000 | 1.0584          |
| 1.1104        | 0.99  | 64500 | 1.0584          |
| 1.1057        | 1.0   | 65000 | 1.0584          |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds2", "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 

codeparrot-ds2
==============


GPT-2 style trained on a filtered set of The Stack, specific to data science related code. Things like pandas, numpy, matplotlib, etc.


* Loss: 1.0584


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: 200
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1
* Datasets 2.13.1
* 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: 200
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1
* Datasets 2.13.1
* 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: 200
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 1.13.1
* Datasets 2.13.1
* Tokenizers 0.13.3