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# pretrained-m-bert-400

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 399

## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2737    | 10.9284         | 0     |
| 7.7909     | 10.9299         | 1     |
| 6.9555     | 11.4698         | 2     |
| 6.4835     | 11.5760         | 3     |
| 6.3613     | 11.4740         | 4     |
| 6.3206     | 12.1134         | 5     |
| 6.5777     | 11.8247         | 6     |
| 6.0680     | 12.1764         | 7     |
| 5.9353     | 12.1038         | 8     |
| 6.0701     | 12.0687         | 9     |
| 6.0047     | 12.4985         | 10    |
| 5.9272     | 12.8107         | 11    |
| 5.9446     | 12.5006         | 12    |
| 5.9716     | 12.8384         | 13    |
| 6.0548     | 12.3585         | 14    |
| 5.8666     | 12.5273         | 15    |
| 5.8951     | 12.8032         | 16    |
| 5.9528     | 13.0296         | 17    |
| 5.8313     | 12.9472         | 18    |
| 5.8324     | 13.0546         | 19    |
| 5.8763     | 13.1180         | 20    |
| 5.7623     | 13.2588         | 21    |
| 5.8636     | 13.2705         | 22    |
| 5.8864     | 13.1756         | 23    |
| 5.7413     | 13.0413         | 24    |
| 5.6825     | 13.2796         | 25    |
| 5.8705     | 13.5748         | 26    |
| 5.7772     | 14.0853         | 27    |
| 5.8029     | 13.5957         | 28    |
| 5.9590     | 13.0809         | 29    |
| 5.8240     | 13.0912         | 30    |
| 5.7687     | 13.5828         | 31    |
| 5.9836     | 13.5432         | 32    |
| 5.6915     | 13.7082         | 33    |
| 5.7853     | 13.6080         | 34    |
| 5.7760     | 13.3992         | 35    |
| 5.7134     | 13.6552         | 36    |
| 5.8415     | 13.5479         | 37    |
| 5.8087     | 13.6847         | 38    |
| 5.8611     | 13.6231         | 39    |
| 5.6492     | 13.8722         | 40    |
| 5.7846     | 13.7056         | 41    |
| 5.7455     | 14.1208         | 42    |
| 5.7129     | 13.7774         | 43    |
| 5.7185     | 14.0725         | 44    |
| 5.6655     | 14.0980         | 45    |
| 5.6734     | 14.0126         | 46    |
| 5.6658     | 14.1982         | 47    |
| 5.6720     | 14.0881         | 48    |
| 5.8220     | 14.0841         | 49    |
| 5.8567     | 14.0512         | 50    |
| 5.6811     | 14.4002         | 51    |
| 5.7271     | 14.1651         | 52    |
| 5.7329     | 13.8948         | 53    |
| 5.9306     | 14.1744         | 54    |
| 5.6757     | 13.6761         | 55    |
| 5.8785     | 14.1815         | 56    |
| 5.7267     | 14.4592         | 57    |
| 5.7313     | 14.1220         | 58    |
| 5.7537     | 14.3197         | 59    |
| 5.7907     | 13.9449         | 60    |
| 5.6611     | 14.0992         | 61    |
| 5.7009     | 14.4630         | 62    |
| 5.6760     | 14.3946         | 63    |
| 5.7096     | 14.2776         | 64    |
| 5.7778     | 14.3569         | 65    |
| 5.7223     | 14.5246         | 66    |
| 5.7954     | 14.2003         | 67    |
| 5.9369     | 14.3692         | 68    |
| 5.8792     | 14.5358         | 69    |
| 5.7172     | 14.3076         | 70    |
| 5.6608     | 14.1833         | 71    |
| 5.8710     | 14.1070         | 72    |
| 5.6896     | 13.9720         | 73    |
| 5.7404     | 14.8816         | 74    |
| 5.8649     | 14.1338         | 75    |
| 5.8223     | 14.4951         | 76    |
| 5.7701     | 14.4468         | 77    |
| 5.7852     | 14.3300         | 78    |
| 5.7038     | 14.0439         | 79    |
| 5.7386     | 14.3995         | 80    |
| 5.7594     | 14.1541         | 81    |
| 5.7688     | 14.3452         | 82    |
| 5.6329     | 14.9077         | 83    |
| 5.8224     | 14.5802         | 84    |
| 5.7969     | 14.8311         | 85    |
| 5.8297     | 14.5400         | 86    |
| 5.6488     | 15.0369         | 87    |
| 5.8020     | 14.7833         | 88    |
| 5.7345     | 14.3675         | 89    |
| 5.7530     | 14.1602         | 90    |
| 5.7599     | 15.2734         | 91    |
| 5.7256     | 14.9461         | 92    |
| 5.6803     | 14.8461         | 93    |
| 5.8602     | 14.5303         | 94    |
| 5.6319     | 14.8087         | 95    |
| 5.7581     | 14.7901         | 96    |
| 5.6319     | 14.8252         | 97    |
| 5.6869     | 14.8271         | 98    |
| 5.7664     | 15.2408         | 99    |
| 5.7731     | 15.0161         | 100   |
| 5.7304     | 15.4610         | 101   |
| 5.8412     | 14.8069         | 102   |
| 5.7723     | 14.1676         | 103   |
| 5.6118     | 15.4048         | 104   |
| 5.7734     | 15.1165         | 105   |
| 5.8253     | 14.9264         | 106   |
| 5.8027     | 14.7596         | 107   |
| 5.7457     | 15.1671         | 108   |
| 5.7381     | 15.2209         | 109   |
| 5.8087     | 14.7408         | 110   |
| 5.8030     | 15.2581         | 111   |
| 5.8320     | 14.9718         | 112   |
| 5.6863     | 15.2960         | 113   |
| 5.8710     | 14.7119         | 114   |
| 5.7232     | 15.0268         | 115   |
| 5.8191     | 14.8410         | 116   |
| 5.6752     | 15.1243         | 117   |
| 5.8462     | 15.0258         | 118   |
| 5.6872     | 15.1223         | 119   |
| 5.7252     | 14.9306         | 120   |
| 5.8288     | 15.0880         | 121   |
| 5.7154     | 15.1128         | 122   |
| 5.7731     | 15.4266         | 123   |
| 5.7265     | 14.9659         | 124   |
| 5.6335     | 15.1716         | 125   |
| 5.7175     | 15.6793         | 126   |
| 5.7104     | 15.4801         | 127   |
| 5.6567     | 15.0955         | 128   |
| 5.9180     | 15.0188         | 129   |
| 5.5288     | 15.3623         | 130   |
| 5.7597     | 15.4404         | 131   |
| 5.8730     | 15.1977         | 132   |
| 5.7699     | 15.5542         | 133   |
| 5.6693     | 15.1629         | 134   |
| 5.7615     | 15.1227         | 135   |
| 5.7240     | 15.4303         | 136   |
| 5.7303     | 15.1579         | 137   |
| 5.7373     | 15.2233         | 138   |
| 5.8327     | 14.7246         | 139   |
| 5.6575     | 15.5657         | 140   |
| 5.6499     | 15.1918         | 141   |
| 5.7592     | 14.9380         | 142   |
| 5.7694     | 15.3017         | 143   |
| 5.7230     | 15.3452         | 144   |
| 5.8646     | 14.7439         | 145   |
| 5.7644     | 15.3110         | 146   |
| 5.7203     | 15.8015         | 147   |
| 5.6472     | 15.6682         | 148   |
| 5.8608     | 15.1659         | 149   |
| 5.7988     | 15.8322         | 150   |
| 5.8471     | 15.3636         | 151   |
| 5.7727     | 15.7957         | 152   |
| 5.6508     | 15.1050         | 153   |
| 5.7816     | 15.3157         | 154   |
| 5.8210     | 15.6138         | 155   |
| 5.7539     | 15.1308         | 156   |
| 5.7941     | 15.3775         | 157   |
| 5.6000     | 15.4838         | 158   |
| 5.7631     | 15.5708         | 159   |
| 5.7086     | 15.5326         | 160   |
| 5.7873     | 15.4520         | 161   |
| 5.7033     | 15.1639         | 162   |
| 5.8096     | 14.9752         | 163   |
| 5.7741     | 15.1489         | 164   |
| 5.6837     | 15.8508         | 165   |
| 5.6194     | 15.6911         | 166   |
| 5.8802     | 15.8204         | 167   |
| 5.7230     | 15.3510         | 168   |
| 5.7498     | 15.5641         | 169   |
| 5.6593     | 16.0866         | 170   |
| 5.7106     | 15.6393         | 171   |
| 5.7150     | 15.7347         | 172   |
| 5.8900     | 15.1096         | 173   |
| 5.6876     | 16.0257         | 174   |
| 5.7460     | 15.7940         | 175   |
| 5.6921     | 15.4479         | 176   |
| 5.7380     | 15.8687         | 177   |
| 5.6652     | 15.7234         | 178   |
| 5.7337     | 15.9722         | 179   |
| 5.7107     | 15.6004         | 180   |
| 5.6688     | 15.9564         | 181   |
| 5.7698     | 15.4953         | 182   |
| 5.6752     | 15.4695         | 183   |
| 5.7951     | 15.3870         | 184   |
| 5.6820     | 15.3892         | 185   |
| 5.7012     | 15.3069         | 186   |
| 5.8445     | 15.4246         | 187   |
| 5.7400     | 15.7290         | 188   |
| 5.7490     | 15.6652         | 189   |
| 5.7101     | 15.9525         | 190   |
| 5.8126     | 15.2409         | 191   |
| 5.7394     | 15.6082         | 192   |
| 5.6442     | 15.7932         | 193   |
| 5.7402     | 15.1385         | 194   |
| 5.7884     | 15.4226         | 195   |
| 5.5444     | 16.1383         | 196   |
| 5.7094     | 15.8144         | 197   |
| 5.6645     | 16.0524         | 198   |
| 5.6880     | 15.7864         | 199   |
| 5.7136     | 15.5176         | 200   |
| 5.6637     | 15.2914         | 201   |
| 5.6428     | 16.0657         | 202   |
| 5.7652     | 16.0983         | 203   |
| 5.7069     | 16.2021         | 204   |
| 5.7251     | 15.6593         | 205   |
| 5.7546     | 15.5634         | 206   |
| 5.6854     | 16.0332         | 207   |
| 5.7590     | 15.9248         | 208   |
| 5.7916     | 15.4682         | 209   |
| 5.7794     | 16.1797         | 210   |
| 5.6640     | 15.8133         | 211   |
| 5.5957     | 15.9253         | 212   |
| 5.8607     | 16.3813         | 213   |
| 5.7572     | 16.1253         | 214   |
| 5.7549     | 16.0874         | 215   |
| 5.6915     | 16.3464         | 216   |
| 5.6458     | 16.7042         | 217   |
| 5.6706     | 15.7191         | 218   |
| 5.7297     | 16.0799         | 219   |
| 5.6525     | 15.7766         | 220   |
| 5.6576     | 16.2920         | 221   |
| 5.6630     | 15.7460         | 222   |
| 5.7052     | 16.1921         | 223   |
| 5.7096     | 15.9475         | 224   |
| 5.8168     | 15.6872         | 225   |
| 5.7823     | 16.1230         | 226   |
| 5.7059     | 15.8028         | 227   |
| 5.7665     | 16.0063         | 228   |
| 5.7908     | 16.2456         | 229   |
| 5.7071     | 16.0749         | 230   |
| 5.7506     | 16.0597         | 231   |
| 5.6364     | 16.1518         | 232   |
| 5.7050     | 16.2710         | 233   |
| 5.7266     | 16.2172         | 234   |
| 5.7804     | 15.8192         | 235   |
| 5.7076     | 16.1186         | 236   |
| 5.6965     | 16.1123         | 237   |
| 5.7385     | 15.7495         | 238   |
| 5.7877     | 16.0528         | 239   |
| 5.5933     | 16.1774         | 240   |
| 5.6745     | 16.5711         | 241   |
| 5.6913     | 16.3114         | 242   |
| 5.7292     | 16.3525         | 243   |
| 5.7804     | 15.9284         | 244   |
| 5.6428     | 16.1581         | 245   |
| 5.6294     | 16.0005         | 246   |
| 5.7076     | 16.2986         | 247   |
| 5.7254     | 16.1134         | 248   |
| 5.7657     | 16.3133         | 249   |
| 5.7027     | 15.9364         | 250   |
| 5.6698     | 16.6521         | 251   |
| 5.6628     | 15.7597         | 252   |
| 5.6293     | 16.2164         | 253   |
| 5.6199     | 15.8001         | 254   |
| 5.6581     | 16.0590         | 255   |
| 5.6714     | 16.0894         | 256   |
| 5.7542     | 16.1121         | 257   |
| 5.7222     | 16.1358         | 258   |
| 5.8194     | 15.9213         | 259   |
| 5.6567     | 16.0372         | 260   |
| 5.6751     | 16.3755         | 261   |
| 5.6469     | 16.0893         | 262   |
| 5.6717     | 16.1122         | 263   |
| 5.7958     | 15.8767         | 264   |
| 5.8171     | 16.2429         | 265   |
| 5.7119     | 16.2250         | 266   |
| 5.7855     | 16.4065         | 267   |
| 5.7949     | 15.4456         | 268   |
| 5.7782     | 16.4003         | 269   |
| 5.7497     | 15.8927         | 270   |
| 5.6609     | 16.3852         | 271   |
| 5.8071     | 16.3739         | 272   |
| 5.7726     | 15.9497         | 273   |
| 5.6874     | 16.0825         | 274   |
| 5.7265     | 15.9471         | 275   |
| 5.8261     | 15.9685         | 276   |
| 5.6508     | 16.3370         | 277   |
| 5.6734     | 16.3040         | 278   |
| 5.6986     | 16.2803         | 279   |
| 5.7025     | 16.6162         | 280   |
| 5.7346     | 16.1483         | 281   |
| 5.6689     | 16.1718         | 282   |
| 5.6913     | 16.0822         | 283   |
| 5.7541     | 15.5025         | 284   |
| 5.7325     | 16.2702         | 285   |
| 5.8124     | 15.7343         | 286   |
| 5.6972     | 16.3263         | 287   |
| 5.7388     | 16.2631         | 288   |
| 5.7337     | 16.1185         | 289   |
| 5.5873     | 16.2938         | 290   |
| 5.6859     | 16.2026         | 291   |
| 5.8711     | 15.2906         | 292   |
| 5.6716     | 15.5945         | 293   |
| 5.7098     | 16.1930         | 294   |
| 5.6214     | 16.2915         | 295   |
| 5.7025     | 16.1317         | 296   |
| 5.6574     | 16.6203         | 297   |
| 5.7909     | 16.1098         | 298   |
| 5.8298     | 15.5068         | 299   |
| 5.7457     | 16.4390         | 300   |
| 5.6674     | 16.1994         | 301   |
| 5.6473     | 16.4981         | 302   |
| 5.7486     | 16.0311         | 303   |
| 5.5914     | 16.8805         | 304   |
| 5.7065     | 15.6899         | 305   |
| 5.7049     | 16.4121         | 306   |
| 5.7791     | 16.5573         | 307   |
| 5.7422     | 16.4121         | 308   |
| 5.6493     | 15.8135         | 309   |
| 5.7504     | 16.1579         | 310   |
| 5.7983     | 15.7995         | 311   |
| 5.6670     | 15.7541         | 312   |
| 5.6519     | 15.8321         | 313   |
| 5.7987     | 16.1384         | 314   |
| 5.8223     | 16.2025         | 315   |
| 5.7494     | 16.2737         | 316   |
| 5.7390     | 16.2242         | 317   |
| 5.7607     | 16.4318         | 318   |
| 5.6872     | 16.4799         | 319   |
| 5.7474     | 16.3387         | 320   |
| 5.8631     | 16.0204         | 321   |
| 5.7391     | 15.9252         | 322   |
| 5.6785     | 17.0023         | 323   |
| 5.6791     | 16.0897         | 324   |
| 5.7644     | 16.0529         | 325   |
| 5.7013     | 16.6206         | 326   |
| 5.8280     | 16.4234         | 327   |
| 5.6553     | 16.4436         | 328   |
| 5.6920     | nan             | 329   |
| 5.7356     | 16.2001         | 330   |
| 5.7204     | 16.1120         | 331   |
| 5.5542     | 16.2315         | 332   |
| 5.7303     | 16.2902         | 333   |
| 5.6750     | 16.3221         | 334   |
| 5.6405     | 16.5904         | 335   |
| 5.8191     | 16.0871         | 336   |
| 5.7464     | 15.8915         | 337   |
| 5.7566     | 16.6865         | 338   |
| 5.7168     | 16.0537         | 339   |
| 5.6113     | 16.5037         | 340   |
| 5.6066     | 15.8096         | 341   |
| 5.6343     | 16.5955         | 342   |
| nan        | nan             | 343   |
| nan        | nan             | 344   |
| nan        | nan             | 345   |
| nan        | nan             | 346   |
| nan        | nan             | 347   |
| nan        | nan             | 348   |
| nan        | nan             | 349   |
| nan        | nan             | 350   |
| nan        | nan             | 351   |
| nan        | nan             | 352   |
| nan        | nan             | 353   |
| nan        | nan             | 354   |
| nan        | nan             | 355   |
| nan        | nan             | 356   |
| nan        | nan             | 357   |
| nan        | nan             | 358   |
| nan        | nan             | 359   |
| nan        | nan             | 360   |
| nan        | nan             | 361   |
| nan        | nan             | 362   |
| nan        | nan             | 363   |
| nan        | nan             | 364   |
| nan        | nan             | 365   |
| nan        | nan             | 366   |
| nan        | nan             | 367   |
| nan        | nan             | 368   |
| nan        | nan             | 369   |
| nan        | nan             | 370   |
| nan        | nan             | 371   |
| nan        | nan             | 372   |
| nan        | nan             | 373   |
| nan        | nan             | 374   |
| nan        | nan             | 375   |
| nan        | nan             | 376   |
| nan        | nan             | 377   |
| nan        | nan             | 378   |
| nan        | nan             | 379   |
| nan        | nan             | 380   |
| nan        | nan             | 381   |
| nan        | nan             | 382   |
| nan        | nan             | 383   |
| nan        | nan             | 384   |
| nan        | nan             | 385   |
| nan        | nan             | 386   |
| nan        | nan             | 387   |
| nan        | nan             | 388   |
| nan        | nan             | 389   |
| nan        | nan             | 390   |
| nan        | nan             | 391   |
| nan        | nan             | 392   |
| nan        | nan             | 393   |
| nan        | nan             | 394   |
| nan        | nan             | 395   |
| nan        | nan             | 396   |
| nan        | nan             | 397   |
| nan        | nan             | 398   |
| nan        | nan             | 399   |


### Framework versions

- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "pretrained-m-bert-400", "results": []}]}
transformers tf bert pretraining generated_from_keras_callback endpoints_compatible region:us
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 

pretrained-m-bert-400
=====================


This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: nan
* Validation Loss: nan
* Epoch: 399


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:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2