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# finetuned-marktextepoch-n800

This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n600](https://huggingface.co/leokai/finetuned-marktextepoch-n600) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8433

## 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 |
|:-------------:|:-----:|:------:|:---------------:|
| 0.287         | 1.0   | 1606   | 2.8473          |
| 0.2913        | 2.0   | 3212   | 2.8147          |
| 0.2865        | 3.0   | 4818   | 2.8809          |
| 0.2947        | 4.0   | 6424   | 2.8510          |
| 0.2988        | 5.0   | 8030   | 2.8883          |
| 0.3109        | 6.0   | 9636   | 2.9016          |
| 0.309         | 7.0   | 11242  | 2.8869          |
| 0.301         | 8.0   | 12848  | 2.9201          |
| 0.303         | 9.0   | 14454  | 2.8902          |
| 0.3156        | 10.0  | 16060  | 2.8888          |
| 0.3132        | 11.0  | 17666  | 2.8777          |
| 0.3089        | 12.0  | 19272  | 2.9429          |
| 0.3146        | 13.0  | 20878  | 2.9131          |
| 0.3297        | 14.0  | 22484  | 2.8983          |
| 0.3214        | 15.0  | 24090  | 2.9321          |
| 0.3095        | 16.0  | 25696  | 2.9436          |
| 0.3171        | 17.0  | 27302  | 2.9163          |
| 0.308         | 18.0  | 28908  | 2.9545          |
| 0.3174        | 19.0  | 30514  | 2.9161          |
| 0.3163        | 20.0  | 32120  | 2.9081          |
| 0.3191        | 21.0  | 33726  | 2.9465          |
| 0.3254        | 22.0  | 35332  | 2.9404          |
| 0.3168        | 23.0  | 36938  | 2.9054          |
| 0.33          | 24.0  | 38544  | 2.9274          |
| 0.3115        | 25.0  | 40150  | 2.9277          |
| 0.3125        | 26.0  | 41756  | 2.9627          |
| 0.3246        | 27.0  | 43362  | 2.9583          |
| 0.3133        | 28.0  | 44968  | 2.9433          |
| 0.3221        | 29.0  | 46574  | 2.9747          |
| 0.3185        | 30.0  | 48180  | 2.9793          |
| 0.3123        | 31.0  | 49786  | 2.9170          |
| 0.3169        | 32.0  | 51392  | 2.9711          |
| 0.3175        | 33.0  | 52998  | 2.9457          |
| 0.3253        | 34.0  | 54604  | 2.9518          |
| 0.3163        | 35.0  | 56210  | 2.9218          |
| 0.3113        | 36.0  | 57816  | 2.9524          |
| 0.3208        | 37.0  | 59422  | 2.9570          |
| 0.3197        | 38.0  | 61028  | 2.9439          |
| 0.3213        | 39.0  | 62634  | 2.9416          |
| 0.3259        | 40.0  | 64240  | 2.9884          |
| 0.3216        | 41.0  | 65846  | 2.9641          |
| 0.3154        | 42.0  | 67452  | 2.9797          |
| 0.3258        | 43.0  | 69058  | 2.9813          |
| 0.3236        | 44.0  | 70664  | 2.9700          |
| 0.3134        | 45.0  | 72270  | 2.9881          |
| 0.3219        | 46.0  | 73876  | 2.9982          |
| 0.3243        | 47.0  | 75482  | 2.9702          |
| 0.3246        | 48.0  | 77088  | 2.9706          |
| 0.3245        | 49.0  | 78694  | 2.9965          |
| 0.3124        | 50.0  | 80300  | 2.9893          |
| 0.3172        | 51.0  | 81906  | 2.9859          |
| 0.3118        | 52.0  | 83512  | 2.9707          |
| 0.3187        | 53.0  | 85118  | 2.9771          |
| 0.3256        | 54.0  | 86724  | 2.9827          |
| 0.3222        | 55.0  | 88330  | 2.9776          |
| 0.3212        | 56.0  | 89936  | 2.9607          |
| 0.3215        | 57.0  | 91542  | 2.9664          |
| 0.3266        | 58.0  | 93148  | 2.9638          |
| 0.3209        | 59.0  | 94754  | 2.9842          |
| 0.333         | 60.0  | 96360  | 3.0053          |
| 0.3202        | 61.0  | 97966  | 2.9833          |
| 0.3155        | 62.0  | 99572  | 2.9952          |
| 0.32          | 63.0  | 101178 | 2.9737          |
| 0.3291        | 64.0  | 102784 | 2.9804          |
| 0.3259        | 65.0  | 104390 | 2.9767          |
| 0.32          | 66.0  | 105996 | 2.9610          |
| 0.3208        | 67.0  | 107602 | 3.0111          |
| 0.3277        | 68.0  | 109208 | 2.9588          |
| 0.337         | 69.0  | 110814 | 2.9920          |
| 0.3296        | 70.0  | 112420 | 2.9466          |
| 0.3197        | 71.0  | 114026 | 2.9619          |
| 0.323         | 72.0  | 115632 | 2.9733          |
| 0.3247        | 73.0  | 117238 | 2.9787          |
| 0.3246        | 74.0  | 118844 | 2.9383          |
| 0.3203        | 75.0  | 120450 | 3.0123          |
| 0.3272        | 76.0  | 122056 | 3.0284          |
| 0.3407        | 77.0  | 123662 | 3.0047          |
| 0.3312        | 78.0  | 125268 | 2.9465          |
| 0.3262        | 79.0  | 126874 | 2.9805          |
| 0.3221        | 80.0  | 128480 | 2.9713          |
| 0.3246        | 81.0  | 130086 | 2.9869          |
| 0.3208        | 82.0  | 131692 | 2.9970          |
| 0.3196        | 83.0  | 133298 | 2.9864          |
| 0.3311        | 84.0  | 134904 | 3.0080          |
| 0.3235        | 85.0  | 136510 | 2.9739          |
| 0.3251        | 86.0  | 138116 | 2.9749          |
| 0.3248        | 87.0  | 139722 | 2.9588          |
| 0.3342        | 88.0  | 141328 | 2.9509          |
| 0.3456        | 89.0  | 142934 | 2.9713          |
| 0.3337        | 90.0  | 144540 | 2.9968          |
| 0.323         | 91.0  | 146146 | 2.9790          |
| 0.3202        | 92.0  | 147752 | 2.9919          |
| 0.3308        | 93.0  | 149358 | 3.0100          |
| 0.3232        | 94.0  | 150964 | 2.9873          |
| 0.3356        | 95.0  | 152570 | 2.9786          |
| 0.3282        | 96.0  | 154176 | 2.9965          |
| 0.3404        | 97.0  | 155782 | 3.0198          |
| 0.3212        | 98.0  | 157388 | 2.9713          |
| 0.3307        | 99.0  | 158994 | 2.9979          |
| 0.337         | 100.0 | 160600 | 2.9805          |
| 0.3354        | 101.0 | 162206 | 2.9759          |
| 0.3252        | 102.0 | 163812 | 2.9810          |
| 0.3324        | 103.0 | 165418 | 2.9433          |
| 0.3278        | 104.0 | 167024 | 3.0079          |
| 0.3419        | 105.0 | 168630 | 2.9576          |
| 0.343         | 106.0 | 170236 | 2.9610          |
| 0.3294        | 107.0 | 171842 | 2.9147          |
| 0.3271        | 108.0 | 173448 | 2.9740          |
| 0.3315        | 109.0 | 175054 | 2.9736          |
| 0.3413        | 110.0 | 176660 | 2.9819          |
| 0.3344        | 111.0 | 178266 | 2.9783          |
| 0.3399        | 112.0 | 179872 | 2.9836          |
| 0.3314        | 113.0 | 181478 | 2.9605          |
| 0.3344        | 114.0 | 183084 | 2.9629          |
| 0.3346        | 115.0 | 184690 | 2.9535          |
| 0.3324        | 116.0 | 186296 | 2.9139          |
| 0.3493        | 117.0 | 187902 | 2.9383          |
| 0.341         | 118.0 | 189508 | 2.9547          |
| 0.3414        | 119.0 | 191114 | 2.9592          |
| 0.335         | 120.0 | 192720 | 2.9822          |
| 0.3423        | 121.0 | 194326 | 2.9498          |
| 0.3415        | 122.0 | 195932 | 2.9371          |
| 0.3557        | 123.0 | 197538 | 2.9625          |
| 0.3544        | 124.0 | 199144 | 2.9637          |
| 0.3528        | 125.0 | 200750 | 2.9881          |
| 0.3567        | 126.0 | 202356 | 2.9576          |
| 0.3336        | 127.0 | 203962 | 2.9427          |
| 0.3282        | 128.0 | 205568 | 2.9659          |
| 0.3605        | 129.0 | 207174 | 2.9555          |
| 0.3436        | 130.0 | 208780 | 2.9590          |
| 0.3489        | 131.0 | 210386 | 2.9250          |
| 0.3604        | 132.0 | 211992 | 2.9411          |
| 0.347         | 133.0 | 213598 | 2.9093          |
| 0.3623        | 134.0 | 215204 | 2.9324          |
| 0.3449        | 135.0 | 216810 | 2.9564          |
| 0.3459        | 136.0 | 218416 | 2.9254          |
| 0.3519        | 137.0 | 220022 | 2.9512          |
| 0.3499        | 138.0 | 221628 | 2.9411          |
| 0.3588        | 139.0 | 223234 | 2.8994          |
| 0.3657        | 140.0 | 224840 | 2.9372          |
| 0.3564        | 141.0 | 226446 | 2.9237          |
| 0.3445        | 142.0 | 228052 | 2.9380          |
| 0.359         | 143.0 | 229658 | 2.9547          |
| 0.3495        | 144.0 | 231264 | 2.9238          |
| 0.3545        | 145.0 | 232870 | 2.9436          |
| 0.3523        | 146.0 | 234476 | 2.9390          |
| 0.3785        | 147.0 | 236082 | 2.8861          |
| 0.356         | 148.0 | 237688 | 2.9239          |
| 0.3624        | 149.0 | 239294 | 2.8960          |
| 0.3619        | 150.0 | 240900 | 2.9224          |
| 0.3607        | 151.0 | 242506 | 2.9155          |
| 0.3585        | 152.0 | 244112 | 2.9144          |
| 0.3735        | 153.0 | 245718 | 2.8805          |
| 0.3534        | 154.0 | 247324 | 2.9095          |
| 0.3667        | 155.0 | 248930 | 2.8888          |
| 0.3705        | 156.0 | 250536 | 2.9049          |
| 0.3711        | 157.0 | 252142 | 2.8801          |
| 0.3633        | 158.0 | 253748 | 2.8874          |
| 0.36          | 159.0 | 255354 | 2.8984          |
| 0.3752        | 160.0 | 256960 | 2.9004          |
| 0.3717        | 161.0 | 258566 | 2.8577          |
| 0.3742        | 162.0 | 260172 | 2.8772          |
| 0.3815        | 163.0 | 261778 | 2.9183          |
| 0.3695        | 164.0 | 263384 | 2.9144          |
| 0.3809        | 165.0 | 264990 | 2.8968          |
| 0.3813        | 166.0 | 266596 | 2.8690          |
| 0.3803        | 167.0 | 268202 | 2.8748          |
| 0.3813        | 168.0 | 269808 | 2.8676          |
| 0.3782        | 169.0 | 271414 | 2.8473          |
| 0.3848        | 170.0 | 273020 | 2.8816          |
| 0.371         | 171.0 | 274626 | 2.8929          |
| 0.3843        | 172.0 | 276232 | 2.8858          |
| 0.381         | 173.0 | 277838 | 2.8590          |
| 0.3889        | 174.0 | 279444 | 2.8484          |
| 0.3814        | 175.0 | 281050 | 2.8634          |
| 0.3865        | 176.0 | 282656 | 2.8713          |
| 0.3968        | 177.0 | 284262 | 2.8490          |
| 0.4007        | 178.0 | 285868 | 2.8497          |
| 0.3805        | 179.0 | 287474 | 2.8435          |
| 0.3903        | 180.0 | 289080 | 2.8582          |
| 0.392         | 181.0 | 290686 | 2.8473          |
| 0.3926        | 182.0 | 292292 | 2.8584          |
| 0.3921        | 183.0 | 293898 | 2.8850          |
| 0.3958        | 184.0 | 295504 | 2.8532          |
| 0.3858        | 185.0 | 297110 | 2.8568          |
| 0.4002        | 186.0 | 298716 | 2.7939          |
| 0.3999        | 187.0 | 300322 | 2.8548          |
| 0.3932        | 188.0 | 301928 | 2.8598          |
| 0.4005        | 189.0 | 303534 | 2.8390          |
| 0.4048        | 190.0 | 305140 | 2.8336          |
| 0.3983        | 191.0 | 306746 | 2.8286          |
| 0.394         | 192.0 | 308352 | 2.8437          |
| 0.3989        | 193.0 | 309958 | 2.8594          |
| 0.3966        | 194.0 | 311564 | 2.8541          |
| 0.397         | 195.0 | 313170 | 2.8697          |
| 0.4007        | 196.0 | 314776 | 2.8549          |
| 0.3978        | 197.0 | 316382 | 2.8815          |
| 0.4005        | 198.0 | 317988 | 2.8565          |
| 0.4025        | 199.0 | 319594 | 2.8451          |
| 0.4078        | 200.0 | 321200 | 2.8433          |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-marktextepoch-n800", "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-n800
============================


This model is a fine-tuned version of leokai/finetuned-marktextepoch-n600 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.8433


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 4.21.1
* Pytorch 1.12.1+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 4.21.1
* Pytorch 1.12.1+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 4.21.1
* Pytorch 1.12.1+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 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1