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# distilroberta-base-finetuned-marktextepoch_n200

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0531

## 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: 200

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.2313        | 1.0   | 1500   | 2.1592          |
| 2.1731        | 2.0   | 3000   | 2.1277          |
| 2.153         | 3.0   | 4500   | 2.1144          |
| 2.1469        | 4.0   | 6000   | 2.1141          |
| 2.1281        | 5.0   | 7500   | 2.1374          |
| 2.1043        | 6.0   | 9000   | 2.1069          |
| 2.0834        | 7.0   | 10500  | 2.0993          |
| 2.0602        | 8.0   | 12000  | 2.0817          |
| 2.024         | 9.0   | 13500  | 2.0918          |
| 2.0261        | 10.0  | 15000  | 2.0793          |
| 1.9889        | 11.0  | 16500  | 2.0567          |
| 1.9915        | 12.0  | 18000  | 2.0700          |
| 1.9532        | 13.0  | 19500  | 2.0436          |
| 1.9362        | 14.0  | 21000  | 2.0596          |
| 1.9024        | 15.0  | 22500  | 2.0189          |
| 1.9262        | 16.0  | 24000  | 2.0435          |
| 1.8883        | 17.0  | 25500  | 2.0430          |
| 1.8867        | 18.0  | 27000  | 2.0416          |
| 1.8807        | 19.0  | 28500  | 2.0051          |
| 1.8517        | 20.0  | 30000  | 2.0338          |
| 1.8357        | 21.0  | 31500  | 2.0166          |
| 1.8241        | 22.0  | 33000  | 2.0355          |
| 1.7985        | 23.0  | 34500  | 2.0073          |
| 1.8061        | 24.0  | 36000  | 2.0473          |
| 1.7996        | 25.0  | 37500  | 2.0446          |
| 1.7786        | 26.0  | 39000  | 2.0086          |
| 1.771         | 27.0  | 40500  | 2.0294          |
| 1.7549        | 28.0  | 42000  | 2.0127          |
| 1.7726        | 29.0  | 43500  | 2.0191          |
| 1.7275        | 30.0  | 45000  | 2.0182          |
| 1.708         | 31.0  | 46500  | 2.0130          |
| 1.7345        | 32.0  | 48000  | 2.0155          |
| 1.7044        | 33.0  | 49500  | 1.9898          |
| 1.7126        | 34.0  | 51000  | 2.0166          |
| 1.698         | 35.0  | 52500  | 1.9879          |
| 1.6637        | 36.0  | 54000  | 2.0311          |
| 1.6854        | 37.0  | 55500  | 2.0355          |
| 1.6585        | 38.0  | 57000  | 2.0094          |
| 1.6418        | 39.0  | 58500  | 2.0042          |
| 1.667         | 40.0  | 60000  | 2.0116          |
| 1.6507        | 41.0  | 61500  | 2.0095          |
| 1.622         | 42.0  | 63000  | 2.0158          |
| 1.6381        | 43.0  | 64500  | 2.0339          |
| 1.6099        | 44.0  | 66000  | 2.0082          |
| 1.6076        | 45.0  | 67500  | 2.0207          |
| 1.5805        | 46.0  | 69000  | 2.0172          |
| 1.5862        | 47.0  | 70500  | 2.0132          |
| 1.5806        | 48.0  | 72000  | 2.0198          |
| 1.574         | 49.0  | 73500  | 2.0181          |
| 1.5718        | 50.0  | 75000  | 2.0086          |
| 1.5591        | 51.0  | 76500  | 1.9832          |
| 1.5468        | 52.0  | 78000  | 2.0167          |
| 1.5637        | 53.0  | 79500  | 2.0118          |
| 1.5117        | 54.0  | 81000  | 2.0290          |
| 1.5363        | 55.0  | 82500  | 2.0011          |
| 1.4976        | 56.0  | 84000  | 2.0160          |
| 1.5129        | 57.0  | 85500  | 2.0224          |
| 1.4964        | 58.0  | 87000  | 2.0219          |
| 1.4906        | 59.0  | 88500  | 2.0212          |
| 1.4941        | 60.0  | 90000  | 2.0255          |
| 1.4876        | 61.0  | 91500  | 2.0116          |
| 1.4837        | 62.0  | 93000  | 2.0176          |
| 1.4661        | 63.0  | 94500  | 2.0388          |
| 1.4634        | 64.0  | 96000  | 2.0165          |
| 1.4449        | 65.0  | 97500  | 2.0185          |
| 1.468         | 66.0  | 99000  | 2.0246          |
| 1.4567        | 67.0  | 100500 | 2.0244          |
| 1.4367        | 68.0  | 102000 | 2.0093          |
| 1.4471        | 69.0  | 103500 | 2.0101          |
| 1.4255        | 70.0  | 105000 | 2.0248          |
| 1.4203        | 71.0  | 106500 | 2.0224          |
| 1.42          | 72.0  | 108000 | 2.0279          |
| 1.4239        | 73.0  | 109500 | 2.0295          |
| 1.4126        | 74.0  | 111000 | 2.0196          |
| 1.4038        | 75.0  | 112500 | 2.0225          |
| 1.3874        | 76.0  | 114000 | 2.0456          |
| 1.3758        | 77.0  | 115500 | 2.0423          |
| 1.3924        | 78.0  | 117000 | 2.0184          |
| 1.3744        | 79.0  | 118500 | 2.0555          |
| 1.3622        | 80.0  | 120000 | 2.0387          |
| 1.3653        | 81.0  | 121500 | 2.0344          |
| 1.3724        | 82.0  | 123000 | 2.0184          |
| 1.3684        | 83.0  | 124500 | 2.0285          |
| 1.3576        | 84.0  | 126000 | 2.0544          |
| 1.348         | 85.0  | 127500 | 2.0412          |
| 1.3387        | 86.0  | 129000 | 2.0459          |
| 1.3416        | 87.0  | 130500 | 2.0329          |
| 1.3421        | 88.0  | 132000 | 2.0274          |
| 1.3266        | 89.0  | 133500 | 2.0233          |
| 1.3183        | 90.0  | 135000 | 2.0319          |
| 1.322         | 91.0  | 136500 | 2.0080          |
| 1.32          | 92.0  | 138000 | 2.0472          |
| 1.304         | 93.0  | 139500 | 2.0538          |
| 1.3061        | 94.0  | 141000 | 2.0340          |
| 1.3199        | 95.0  | 142500 | 2.0456          |
| 1.2985        | 96.0  | 144000 | 2.0167          |
| 1.3021        | 97.0  | 145500 | 2.0204          |
| 1.2787        | 98.0  | 147000 | 2.0645          |
| 1.2879        | 99.0  | 148500 | 2.0345          |
| 1.2695        | 100.0 | 150000 | 2.0340          |
| 1.2884        | 101.0 | 151500 | 2.0602          |
| 1.2747        | 102.0 | 153000 | 2.0667          |
| 1.2607        | 103.0 | 154500 | 2.0551          |
| 1.2551        | 104.0 | 156000 | 2.0544          |
| 1.2557        | 105.0 | 157500 | 2.0553          |
| 1.2495        | 106.0 | 159000 | 2.0370          |
| 1.26          | 107.0 | 160500 | 2.0568          |
| 1.2499        | 108.0 | 162000 | 2.0427          |
| 1.2438        | 109.0 | 163500 | 2.0184          |
| 1.2496        | 110.0 | 165000 | 2.0227          |
| 1.2332        | 111.0 | 166500 | 2.0621          |
| 1.2231        | 112.0 | 168000 | 2.0661          |
| 1.211         | 113.0 | 169500 | 2.0673          |
| 1.217         | 114.0 | 171000 | 2.0544          |
| 1.2206        | 115.0 | 172500 | 2.0542          |
| 1.2083        | 116.0 | 174000 | 2.0592          |
| 1.2205        | 117.0 | 175500 | 2.0451          |
| 1.2065        | 118.0 | 177000 | 2.0402          |
| 1.1988        | 119.0 | 178500 | 2.0615          |
| 1.218         | 120.0 | 180000 | 2.0374          |
| 1.1917        | 121.0 | 181500 | 2.0349          |
| 1.1854        | 122.0 | 183000 | 2.0790          |
| 1.1819        | 123.0 | 184500 | 2.0766          |
| 1.2029        | 124.0 | 186000 | 2.0364          |
| 1.1851        | 125.0 | 187500 | 2.0568          |
| 1.1734        | 126.0 | 189000 | 2.0445          |
| 1.1701        | 127.0 | 190500 | 2.0770          |
| 1.1824        | 128.0 | 192000 | 2.0566          |
| 1.1604        | 129.0 | 193500 | 2.0542          |
| 1.1733        | 130.0 | 195000 | 2.0525          |
| 1.1743        | 131.0 | 196500 | 2.0577          |
| 1.1692        | 132.0 | 198000 | 2.0723          |
| 1.1519        | 133.0 | 199500 | 2.0567          |
| 1.1401        | 134.0 | 201000 | 2.0795          |
| 1.1692        | 135.0 | 202500 | 2.0625          |
| 1.157         | 136.0 | 204000 | 2.0793          |
| 1.1495        | 137.0 | 205500 | 2.0782          |
| 1.1479        | 138.0 | 207000 | 2.0392          |
| 1.1247        | 139.0 | 208500 | 2.0796          |
| 1.143         | 140.0 | 210000 | 2.0369          |
| 1.1324        | 141.0 | 211500 | 2.0699          |
| 1.1341        | 142.0 | 213000 | 2.0694          |
| 1.1317        | 143.0 | 214500 | 2.0569          |
| 1.1254        | 144.0 | 216000 | 2.0545          |
| 1.1156        | 145.0 | 217500 | 2.0708          |
| 1.1353        | 146.0 | 219000 | 2.0767          |
| 1.1312        | 147.0 | 220500 | 2.0523          |
| 1.1224        | 148.0 | 222000 | 2.0565          |
| 1.106         | 149.0 | 223500 | 2.0696          |
| 1.1069        | 150.0 | 225000 | 2.0478          |
| 1.1011        | 151.0 | 226500 | 2.0475          |
| 1.0985        | 152.0 | 228000 | 2.0888          |
| 1.1107        | 153.0 | 229500 | 2.0756          |
| 1.1058        | 154.0 | 231000 | 2.0812          |
| 1.1027        | 155.0 | 232500 | 2.0597          |
| 1.0996        | 156.0 | 234000 | 2.0684          |
| 1.0987        | 157.0 | 235500 | 2.0629          |
| 1.0881        | 158.0 | 237000 | 2.0701          |
| 1.1143        | 159.0 | 238500 | 2.0740          |
| 1.0823        | 160.0 | 240000 | 2.0869          |
| 1.0925        | 161.0 | 241500 | 2.0567          |
| 1.1034        | 162.0 | 243000 | 2.0833          |
| 1.0759        | 163.0 | 244500 | 2.0585          |
| 1.0998        | 164.0 | 246000 | 2.0293          |
| 1.0891        | 165.0 | 247500 | 2.0608          |
| 1.1036        | 166.0 | 249000 | 2.0831          |
| 1.076         | 167.0 | 250500 | 2.0979          |
| 1.0895        | 168.0 | 252000 | 2.0882          |
| 1.0825        | 169.0 | 253500 | 2.0742          |
| 1.0793        | 170.0 | 255000 | 2.0841          |
| 1.079         | 171.0 | 256500 | 2.0829          |
| 1.0653        | 172.0 | 258000 | 2.0888          |
| 1.0834        | 173.0 | 259500 | 2.0784          |
| 1.0721        | 174.0 | 261000 | 2.0859          |
| 1.0712        | 175.0 | 262500 | 2.0810          |
| 1.0494        | 176.0 | 264000 | 2.0605          |
| 1.0654        | 177.0 | 265500 | 2.0623          |
| 1.077         | 178.0 | 267000 | 2.0756          |
| 1.056         | 179.0 | 268500 | 2.0782          |
| 1.0523        | 180.0 | 270000 | 2.0966          |
| 1.0656        | 181.0 | 271500 | 2.0750          |
| 1.0636        | 182.0 | 273000 | 2.0769          |
| 1.0851        | 183.0 | 274500 | 2.0872          |
| 1.0562        | 184.0 | 276000 | 2.0893          |
| 1.0534        | 185.0 | 277500 | 2.0661          |
| 1.0514        | 186.0 | 279000 | 2.0712          |
| 1.062         | 187.0 | 280500 | 2.0769          |
| 1.0683        | 188.0 | 282000 | 2.0765          |
| 1.0606        | 189.0 | 283500 | 2.0735          |
| 1.0555        | 190.0 | 285000 | 2.0710          |
| 1.0568        | 191.0 | 286500 | 2.0860          |
| 1.0502        | 192.0 | 288000 | 2.0587          |
| 1.0437        | 193.0 | 289500 | 2.0998          |
| 1.0534        | 194.0 | 291000 | 2.0418          |
| 1.062         | 195.0 | 292500 | 2.0724          |
| 1.0457        | 196.0 | 294000 | 2.0612          |
| 1.0501        | 197.0 | 295500 | 2.1012          |
| 1.0728        | 198.0 | 297000 | 2.0721          |
| 1.0413        | 199.0 | 298500 | 2.0535          |
| 1.0461        | 200.0 | 300000 | 2.0531          |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-marktextepoch_n200", "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 

distilroberta-base-finetuned-marktextepoch\_n200
================================================


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


* Loss: 2.0531


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: 200


### Training results



### Framework versions


* Transformers 4.21.0
* 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: 200 ### Training results ### Framework versions


* Transformers 4.21.0
* 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: 200 ### Training results ### Framework versions


* Transformers 4.21.0
* 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: 200### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1