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# gpt-neo-125m-neurallinguisticpioneers

This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6584

## 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: 3e-05
- 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: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.8245        | 0.01  | 1    | 5.0320          |
| 5.1946        | 0.01  | 2    | 4.2171          |
| 4.1809        | 0.02  | 3    | 3.4349          |
| 3.3553        | 0.02  | 4    | 2.7171          |
| 2.185         | 0.03  | 5    | 2.0634          |
| 1.9955        | 0.03  | 6    | 1.5786          |
| 1.9371        | 0.04  | 7    | 1.2490          |
| 1.4402        | 0.04  | 8    | 1.0349          |
| 0.8763        | 0.05  | 9    | 0.9157          |
| 0.8813        | 0.05  | 10   | 0.8550          |
| 0.7723        | 0.06  | 11   | 0.8259          |
| 0.7909        | 0.06  | 12   | 0.8052          |
| 0.4889        | 0.07  | 13   | 0.7959          |
| 0.7361        | 0.07  | 14   | 0.7891          |
| 0.4922        | 0.08  | 15   | 0.7793          |
| 0.5533        | 0.09  | 16   | 0.7675          |
| 1.1071        | 0.09  | 17   | 0.7563          |
| 0.7885        | 0.1   | 18   | 0.7480          |
| 0.7701        | 0.1   | 19   | 0.7445          |
| 0.6235        | 0.11  | 20   | 0.7447          |
| 0.8623        | 0.11  | 21   | 0.7484          |
| 0.665         | 0.12  | 22   | 0.7558          |
| 0.6907        | 0.12  | 23   | 0.7573          |
| 0.7143        | 0.13  | 24   | 0.7583          |
| 0.7554        | 0.13  | 25   | 0.7599          |
| 0.6228        | 0.14  | 26   | 0.7621          |
| 0.8079        | 0.14  | 27   | 0.7612          |
| 0.6974        | 0.15  | 28   | 0.7586          |
| 0.8349        | 0.16  | 29   | 0.7541          |
| 0.8251        | 0.16  | 30   | 0.7484          |
| 0.687         | 0.17  | 31   | 0.7400          |
| 0.8156        | 0.17  | 32   | 0.7280          |
| 0.7693        | 0.18  | 33   | 0.7183          |
| 0.5224        | 0.18  | 34   | 0.7096          |
| 0.6345        | 0.19  | 35   | 0.7033          |
| 0.6443        | 0.19  | 36   | 0.6979          |
| 1.1552        | 0.2   | 37   | 0.6930          |
| 0.7819        | 0.2   | 38   | 0.6897          |
| 0.6277        | 0.21  | 39   | 0.6875          |
| 0.5751        | 0.21  | 40   | 0.6862          |
| 0.7169        | 0.22  | 41   | 0.6854          |
| 0.7077        | 0.22  | 42   | 0.6842          |
| 0.5667        | 0.23  | 43   | 0.6831          |
| 0.9234        | 0.24  | 44   | 0.6822          |
| 0.6332        | 0.24  | 45   | 0.6815          |
| 0.865         | 0.25  | 46   | 0.6806          |
| 0.5918        | 0.25  | 47   | 0.6797          |
| 0.6196        | 0.26  | 48   | 0.6788          |
| 0.7697        | 0.26  | 49   | 0.6778          |
| 0.4448        | 0.27  | 50   | 0.6769          |
| 0.7951        | 0.27  | 51   | 0.6760          |
| 0.9171        | 0.28  | 52   | 0.6751          |
| 0.7169        | 0.28  | 53   | 0.6745          |
| 0.7001        | 0.29  | 54   | 0.6742          |
| 0.7755        | 0.29  | 55   | 0.6742          |
| 0.7426        | 0.3   | 56   | 0.6743          |
| 0.6208        | 0.3   | 57   | 0.6742          |
| 0.6962        | 0.31  | 58   | 0.6740          |
| 0.3848        | 0.32  | 59   | 0.6739          |
| 0.6986        | 0.32  | 60   | 0.6736          |
| 0.6316        | 0.33  | 61   | 0.6734          |
| 0.5988        | 0.33  | 62   | 0.6732          |
| 0.6551        | 0.34  | 63   | 0.6729          |
| 0.6102        | 0.34  | 64   | 0.6724          |
| 0.7752        | 0.35  | 65   | 0.6718          |
| 0.6145        | 0.35  | 66   | 0.6713          |
| 0.6829        | 0.36  | 67   | 0.6709          |
| 0.7952        | 0.36  | 68   | 0.6705          |
| 0.5888        | 0.37  | 69   | 0.6702          |
| 0.7763        | 0.37  | 70   | 0.6698          |
| 0.6723        | 0.38  | 71   | 0.6694          |
| 0.6429        | 0.39  | 72   | 0.6691          |
| 1.0005        | 0.39  | 73   | 0.6688          |
| 0.6184        | 0.4   | 74   | 0.6684          |
| 0.7118        | 0.4   | 75   | 0.6682          |
| 0.5414        | 0.41  | 76   | 0.6679          |
| 0.6491        | 0.41  | 77   | 0.6676          |
| 0.9418        | 0.42  | 78   | 0.6673          |
| 0.7183        | 0.42  | 79   | 0.6670          |
| 0.682         | 0.43  | 80   | 0.6668          |
| 0.5946        | 0.43  | 81   | 0.6665          |
| 0.6681        | 0.44  | 82   | 0.6662          |
| 0.9125        | 0.44  | 83   | 0.6659          |
| 0.6752        | 0.45  | 84   | 0.6657          |
| 0.6908        | 0.45  | 85   | 0.6655          |
| 0.5878        | 0.46  | 86   | 0.6653          |
| 0.805         | 0.47  | 87   | 0.6651          |
| 0.7584        | 0.47  | 88   | 0.6650          |
| 0.6652        | 0.48  | 89   | 0.6649          |
| 0.9363        | 0.48  | 90   | 0.6647          |
| 0.6201        | 0.49  | 91   | 0.6646          |
| 0.6827        | 0.49  | 92   | 0.6644          |
| 0.8921        | 0.5   | 93   | 0.6643          |
| 0.5194        | 0.5   | 94   | 0.6641          |
| 0.9393        | 0.51  | 95   | 0.6639          |
| 0.8484        | 0.51  | 96   | 0.6637          |
| 0.5412        | 0.52  | 97   | 0.6635          |
| 1.0085        | 0.52  | 98   | 0.6633          |
| 0.5217        | 0.53  | 99   | 0.6632          |
| 0.6137        | 0.53  | 100  | 0.6630          |
| 0.5484        | 0.54  | 101  | 0.6629          |
| 0.5827        | 0.55  | 102  | 0.6627          |
| 0.3374        | 0.55  | 103  | 0.6629          |
| 0.8269        | 0.56  | 104  | 0.6630          |
| 0.8126        | 0.56  | 105  | 0.6630          |
| 0.8088        | 0.57  | 106  | 0.6631          |
| 0.5498        | 0.57  | 107  | 0.6632          |
| 0.6787        | 0.58  | 108  | 0.6633          |
| 0.8786        | 0.58  | 109  | 0.6633          |
| 0.6237        | 0.59  | 110  | 0.6634          |
| 0.6369        | 0.59  | 111  | 0.6634          |
| 0.5629        | 0.6   | 112  | 0.6634          |
| 0.4571        | 0.6   | 113  | 0.6635          |
| 0.902         | 0.61  | 114  | 0.6634          |
| 0.5153        | 0.61  | 115  | 0.6632          |
| 0.9284        | 0.62  | 116  | 0.6629          |
| 0.7149        | 0.63  | 117  | 0.6626          |
| 0.5224        | 0.63  | 118  | 0.6623          |
| 0.5969        | 0.64  | 119  | 0.6621          |
| 0.655         | 0.64  | 120  | 0.6619          |
| 0.6182        | 0.65  | 121  | 0.6619          |
| 0.6564        | 0.65  | 122  | 0.6618          |
| 0.6919        | 0.66  | 123  | 0.6618          |
| 0.5894        | 0.66  | 124  | 0.6617          |
| 0.4312        | 0.67  | 125  | 0.6617          |
| 0.7523        | 0.67  | 126  | 0.6617          |
| 0.7962        | 0.68  | 127  | 0.6617          |
| 0.3758        | 0.68  | 128  | 0.6617          |
| 0.7343        | 0.69  | 129  | 0.6617          |
| 0.7569        | 0.7   | 130  | 0.6616          |
| 0.4816        | 0.7   | 131  | 0.6616          |
| 0.7127        | 0.71  | 132  | 0.6616          |
| 0.4597        | 0.71  | 133  | 0.6616          |
| 0.6429        | 0.72  | 134  | 0.6616          |
| 0.6452        | 0.72  | 135  | 0.6616          |
| 0.5815        | 0.73  | 136  | 0.6615          |
| 0.743         | 0.73  | 137  | 0.6614          |
| 0.5613        | 0.74  | 138  | 0.6612          |
| 0.5038        | 0.74  | 139  | 0.6610          |
| 0.797         | 0.75  | 140  | 0.6609          |
| 0.6244        | 0.75  | 141  | 0.6608          |
| 0.4257        | 0.76  | 142  | 0.6607          |
| 0.6096        | 0.76  | 143  | 0.6606          |
| 0.6566        | 0.77  | 144  | 0.6605          |
| 0.4325        | 0.78  | 145  | 0.6604          |
| 0.7307        | 0.78  | 146  | 0.6604          |
| 0.7955        | 0.79  | 147  | 0.6603          |
| 0.6972        | 0.79  | 148  | 0.6602          |
| 0.7527        | 0.8   | 149  | 0.6602          |
| 0.5718        | 0.8   | 150  | 0.6602          |
| 0.8002        | 0.81  | 151  | 0.6602          |
| 0.6643        | 0.81  | 152  | 0.6602          |
| 0.7817        | 0.82  | 153  | 0.6602          |
| 0.6829        | 0.82  | 154  | 0.6602          |
| 0.8392        | 0.83  | 155  | 0.6601          |
| 0.5246        | 0.83  | 156  | 0.6601          |
| 0.6613        | 0.84  | 157  | 0.6601          |
| 0.4456        | 0.84  | 158  | 0.6600          |
| 0.4505        | 0.85  | 159  | 0.6600          |
| 0.6184        | 0.86  | 160  | 0.6600          |
| 0.6419        | 0.86  | 161  | 0.6599          |
| 0.3138        | 0.87  | 162  | 0.6599          |
| 0.5554        | 0.87  | 163  | 0.6598          |
| 0.702         | 0.88  | 164  | 0.6597          |
| 0.801         | 0.88  | 165  | 0.6595          |
| 0.6689        | 0.89  | 166  | 0.6594          |
| 0.5907        | 0.89  | 167  | 0.6593          |
| 0.9349        | 0.9   | 168  | 0.6592          |
| 0.7987        | 0.9   | 169  | 0.6591          |
| 0.6379        | 0.91  | 170  | 0.6590          |
| 0.5561        | 0.91  | 171  | 0.6589          |
| 0.6637        | 0.92  | 172  | 0.6589          |
| 0.5391        | 0.93  | 173  | 0.6588          |
| 0.6578        | 0.93  | 174  | 0.6588          |
| 0.7013        | 0.94  | 175  | 0.6587          |
| 0.6868        | 0.94  | 176  | 0.6587          |
| 0.6297        | 0.95  | 177  | 0.6586          |
| 0.7349        | 0.95  | 178  | 0.6586          |
| 0.8577        | 0.96  | 179  | 0.6585          |
| 0.8536        | 0.96  | 180  | 0.6585          |
| 0.4971        | 0.97  | 181  | 0.6585          |
| 0.5129        | 0.97  | 182  | 0.6585          |
| 0.7636        | 0.98  | 183  | 0.6585          |
| 0.5111        | 0.98  | 184  | 0.6585          |
| 0.7281        | 0.99  | 185  | 0.6585          |
| 0.5653        | 0.99  | 186  | 0.6585          |
| 0.7766        | 1.0   | 187  | 0.6584          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-125m-neurallinguisticpioneers", "results": []}]}
transformers pytorch tensorboard gpt_neo text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

gpt-neo-125m-neurallinguisticpioneers
=====================================


This model is a fine-tuned version of EleutherAI/gpt-neo-125m on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6584


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: 3e-05
* 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: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* 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: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* 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: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3