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# finetuned-marktextepoch-n600

This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n500](https://huggingface.co/leokai/finetuned-marktextepoch-n500) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6814

## 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: 2e-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: 182

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.5332        | 1.0   | 1606   | 2.5256          |
| 0.5315        | 2.0   | 3212   | 2.4835          |
| 0.5181        | 3.0   | 4818   | 2.5471          |
| 0.5318        | 4.0   | 6424   | 2.5213          |
| 0.5398        | 5.0   | 8030   | 2.5408          |
| 0.5474        | 6.0   | 9636   | 2.5557          |
| 0.541         | 7.0   | 11242  | 2.5415          |
| 0.529         | 8.0   | 12848  | 2.5729          |
| 0.5294        | 9.0   | 14454  | 2.5533          |
| 0.5404        | 10.0  | 16060  | 2.5414          |
| 0.5359        | 11.0  | 17666  | 2.5316          |
| 0.5295        | 12.0  | 19272  | 2.5985          |
| 0.5319        | 13.0  | 20878  | 2.5644          |
| 0.5492        | 14.0  | 22484  | 2.5594          |
| 0.5403        | 15.0  | 24090  | 2.5830          |
| 0.526         | 16.0  | 25696  | 2.5999          |
| 0.5308        | 17.0  | 27302  | 2.5629          |
| 0.516         | 18.0  | 28908  | 2.6015          |
| 0.525         | 19.0  | 30514  | 2.5726          |
| 0.5238        | 20.0  | 32120  | 2.5656          |
| 0.5244        | 21.0  | 33726  | 2.5961          |
| 0.531         | 22.0  | 35332  | 2.5809          |
| 0.517         | 23.0  | 36938  | 2.5688          |
| 0.5334        | 24.0  | 38544  | 2.5828          |
| 0.505         | 25.0  | 40150  | 2.5861          |
| 0.5136        | 26.0  | 41756  | 2.6044          |
| 0.5228        | 27.0  | 43362  | 2.6000          |
| 0.5066        | 28.0  | 44968  | 2.5971          |
| 0.5183        | 29.0  | 46574  | 2.6240          |
| 0.5076        | 30.0  | 48180  | 2.6201          |
| 0.5059        | 31.0  | 49786  | 2.5746          |
| 0.5033        | 32.0  | 51392  | 2.6229          |
| 0.5041        | 33.0  | 52998  | 2.6086          |
| 0.5132        | 34.0  | 54604  | 2.6115          |
| 0.5007        | 35.0  | 56210  | 2.5865          |
| 0.4947        | 36.0  | 57816  | 2.6159          |
| 0.4997        | 37.0  | 59422  | 2.6072          |
| 0.4988        | 38.0  | 61028  | 2.5931          |
| 0.5001        | 39.0  | 62634  | 2.5956          |
| 0.5027        | 40.0  | 64240  | 2.6444          |
| 0.4969        | 41.0  | 65846  | 2.6194          |
| 0.4916        | 42.0  | 67452  | 2.6394          |
| 0.4986        | 43.0  | 69058  | 2.6401          |
| 0.5           | 44.0  | 70664  | 2.6164          |
| 0.4848        | 45.0  | 72270  | 2.6479          |
| 0.4926        | 46.0  | 73876  | 2.6519          |
| 0.492         | 47.0  | 75482  | 2.6359          |
| 0.4939        | 48.0  | 77088  | 2.6211          |
| 0.4914        | 49.0  | 78694  | 2.6536          |
| 0.4743        | 50.0  | 80300  | 2.6434          |
| 0.4787        | 51.0  | 81906  | 2.6396          |
| 0.4686        | 52.0  | 83512  | 2.6337          |
| 0.4775        | 53.0  | 85118  | 2.6352          |
| 0.4844        | 54.0  | 86724  | 2.6382          |
| 0.4802        | 55.0  | 88330  | 2.6473          |
| 0.4799        | 56.0  | 89936  | 2.6284          |
| 0.4749        | 57.0  | 91542  | 2.6371          |
| 0.4779        | 58.0  | 93148  | 2.6264          |
| 0.4727        | 59.0  | 94754  | 2.6506          |
| 0.4875        | 60.0  | 96360  | 2.6677          |
| 0.4695        | 61.0  | 97966  | 2.6507          |
| 0.4612        | 62.0  | 99572  | 2.6600          |
| 0.4658        | 63.0  | 101178 | 2.6442          |
| 0.4737        | 64.0  | 102784 | 2.6593          |
| 0.47          | 65.0  | 104390 | 2.6451          |
| 0.4658        | 66.0  | 105996 | 2.6493          |
| 0.4634        | 67.0  | 107602 | 2.6795          |
| 0.4713        | 68.0  | 109208 | 2.6392          |
| 0.4771        | 69.0  | 110814 | 2.6633          |
| 0.4704        | 70.0  | 112420 | 2.6273          |
| 0.458         | 71.0  | 114026 | 2.6426          |
| 0.4577        | 72.0  | 115632 | 2.6652          |
| 0.4585        | 73.0  | 117238 | 2.6609          |
| 0.4567        | 74.0  | 118844 | 2.6285          |
| 0.4524        | 75.0  | 120450 | 2.6860          |
| 0.4615        | 76.0  | 122056 | 2.7033          |
| 0.4725        | 77.0  | 123662 | 2.6877          |
| 0.4621        | 78.0  | 125268 | 2.6343          |
| 0.4555        | 79.0  | 126874 | 2.6664          |
| 0.4485        | 80.0  | 128480 | 2.6650          |
| 0.4508        | 81.0  | 130086 | 2.6777          |
| 0.4475        | 82.0  | 131692 | 2.6759          |
| 0.4432        | 83.0  | 133298 | 2.6711          |
| 0.4541        | 84.0  | 134904 | 2.6905          |
| 0.444         | 85.0  | 136510 | 2.6699          |
| 0.4428        | 86.0  | 138116 | 2.6737          |
| 0.4436        | 87.0  | 139722 | 2.6536          |
| 0.4522        | 88.0  | 141328 | 2.6504          |
| 0.4632        | 89.0  | 142934 | 2.6697          |
| 0.4514        | 90.0  | 144540 | 2.6854          |
| 0.4369        | 91.0  | 146146 | 2.6804          |
| 0.4324        | 92.0  | 147752 | 2.7011          |
| 0.4436        | 93.0  | 149358 | 2.7145          |
| 0.4317        | 94.0  | 150964 | 2.6880          |
| 0.4468        | 95.0  | 152570 | 2.6784          |
| 0.4364        | 96.0  | 154176 | 2.7050          |
| 0.4505        | 97.0  | 155782 | 2.7214          |
| 0.4273        | 98.0  | 157388 | 2.6843          |
| 0.4374        | 99.0  | 158994 | 2.7047          |
| 0.4436        | 100.0 | 160600 | 2.6934          |
| 0.4399        | 101.0 | 162206 | 2.6913          |
| 0.4273        | 102.0 | 163812 | 2.6949          |
| 0.4334        | 103.0 | 165418 | 2.6628          |
| 0.4277        | 104.0 | 167024 | 2.7170          |
| 0.439         | 105.0 | 168630 | 2.6752          |
| 0.4418        | 106.0 | 170236 | 2.6832          |
| 0.4278        | 107.0 | 171842 | 2.6386          |
| 0.4226        | 108.0 | 173448 | 2.6946          |
| 0.4255        | 109.0 | 175054 | 2.6911          |
| 0.4349        | 110.0 | 176660 | 2.7073          |
| 0.4259        | 111.0 | 178266 | 2.7048          |
| 0.4328        | 112.0 | 179872 | 2.7105          |
| 0.4242        | 113.0 | 181478 | 2.6897          |
| 0.4228        | 114.0 | 183084 | 2.6921          |
| 0.4227        | 115.0 | 184690 | 2.6833          |
| 0.4192        | 116.0 | 186296 | 2.6483          |
| 0.4381        | 117.0 | 187902 | 2.6690          |
| 0.425         | 118.0 | 189508 | 2.6866          |
| 0.4273        | 119.0 | 191114 | 2.6892          |
| 0.4201        | 120.0 | 192720 | 2.7128          |
| 0.4252        | 121.0 | 194326 | 2.6883          |
| 0.423         | 122.0 | 195932 | 2.6766          |
| 0.4371        | 123.0 | 197538 | 2.7092          |
| 0.4363        | 124.0 | 199144 | 2.7084          |
| 0.4315        | 125.0 | 200750 | 2.7321          |
| 0.4367        | 126.0 | 202356 | 2.7005          |
| 0.4114        | 127.0 | 203962 | 2.6878          |
| 0.4025        | 128.0 | 205568 | 2.7100          |
| 0.4376        | 129.0 | 207174 | 2.7073          |
| 0.4201        | 130.0 | 208780 | 2.7064          |
| 0.4248        | 131.0 | 210386 | 2.6755          |
| 0.4333        | 132.0 | 211992 | 2.6884          |
| 0.4178        | 133.0 | 213598 | 2.6688          |
| 0.433         | 134.0 | 215204 | 2.6911          |
| 0.4145        | 135.0 | 216810 | 2.7116          |
| 0.4163        | 136.0 | 218416 | 2.6867          |
| 0.4203        | 137.0 | 220022 | 2.7109          |
| 0.4164        | 138.0 | 221628 | 2.7031          |
| 0.4252        | 139.0 | 223234 | 2.6656          |
| 0.4302        | 140.0 | 224840 | 2.7018          |
| 0.4205        | 141.0 | 226446 | 2.6912          |
| 0.4055        | 142.0 | 228052 | 2.7107          |
| 0.4204        | 143.0 | 229658 | 2.7236          |
| 0.4104        | 144.0 | 231264 | 2.6931          |
| 0.4146        | 145.0 | 232870 | 2.7160          |
| 0.4113        | 146.0 | 234476 | 2.7116          |
| 0.4375        | 147.0 | 236082 | 2.6680          |
| 0.4135        | 148.0 | 237688 | 2.6984          |
| 0.4198        | 149.0 | 239294 | 2.6823          |
| 0.4154        | 150.0 | 240900 | 2.7031          |
| 0.4159        | 151.0 | 242506 | 2.7000          |
| 0.4104        | 152.0 | 244112 | 2.6974          |
| 0.4283        | 153.0 | 245718 | 2.6649          |
| 0.4046        | 154.0 | 247324 | 2.6989          |
| 0.4174        | 155.0 | 248930 | 2.6774          |
| 0.4199        | 156.0 | 250536 | 2.6943          |
| 0.421         | 157.0 | 252142 | 2.6728          |
| 0.4106        | 158.0 | 253748 | 2.6836          |
| 0.4081        | 159.0 | 255354 | 2.6946          |
| 0.4233        | 160.0 | 256960 | 2.6992          |
| 0.4183        | 161.0 | 258566 | 2.6585          |
| 0.4213        | 162.0 | 260172 | 2.6761          |
| 0.4259        | 163.0 | 261778 | 2.7186          |
| 0.4157        | 164.0 | 263384 | 2.7150          |
| 0.4257        | 165.0 | 264990 | 2.7004          |
| 0.4251        | 166.0 | 266596 | 2.6728          |
| 0.4228        | 167.0 | 268202 | 2.6831          |
| 0.4233        | 168.0 | 269808 | 2.6781          |
| 0.418         | 169.0 | 271414 | 2.6598          |
| 0.4263        | 170.0 | 273020 | 2.6930          |
| 0.4104        | 171.0 | 274626 | 2.7045          |
| 0.4213        | 172.0 | 276232 | 2.6979          |
| 0.419         | 173.0 | 277838 | 2.6726          |
| 0.4273        | 174.0 | 279444 | 2.6631          |
| 0.4189        | 175.0 | 281050 | 2.6802          |
| 0.4228        | 176.0 | 282656 | 2.6872          |
| 0.431         | 177.0 | 284262 | 2.6677          |
| 0.4363        | 178.0 | 285868 | 2.6710          |
| 0.4145        | 179.0 | 287474 | 2.6654          |
| 0.4256        | 180.0 | 289080 | 2.6802          |
| 0.4277        | 181.0 | 290686 | 2.6698          |
| 0.4249        | 182.0 | 292292 | 2.6814          |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-marktextepoch-n600", "results": []}]}
transformers pytorch tensorboard roberta fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

finetuned-marktextepoch-n600
============================


This model is a fine-tuned version of leokai/finetuned-marktextepoch-n500 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6814


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: 2e-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: 182


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 182 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 182 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 182### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1