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# MIX2_ja-en_helsinki

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4929
- Otaku Benchmark VN BLEU: 20.21
- Otaku Benchmark LN BLEU: 13.29
- Otaku Benchmark MANGA BLEU: 19.07

## 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: 0.0003
- train_batch_size: 96
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.8467        | 0.01  | 2000   | 2.3237          |
| 2.6439        | 0.02  | 4000   | 2.2542          |
| 2.547         | 0.03  | 6000   | 2.1956          |
| 2.4852        | 0.04  | 8000   | 2.1088          |
| 2.4408        | 0.05  | 10000  | 2.0909          |
| 2.404         | 0.06  | 12000  | 2.1029          |
| 2.3634        | 0.07  | 14000  | 2.0636          |
| 2.3491        | 0.08  | 16000  | 2.0312          |
| 2.3203        | 0.09  | 18000  | 2.0187          |
| 2.3002        | 0.1   | 20000  | 1.9999          |
| 2.2791        | 0.11  | 22000  | 1.9823          |
| 2.2607        | 0.11  | 24000  | 1.9588          |
| 2.2475        | 0.12  | 26000  | 1.9728          |
| 2.2308        | 0.13  | 28000  | 1.9330          |
| 2.2237        | 0.14  | 30000  | 1.9657          |
| 2.208         | 0.15  | 32000  | 1.9560          |
| 2.2019        | 0.16  | 34000  | 1.9704          |
| 2.1864        | 0.17  | 36000  | 1.9513          |
| 2.1764        | 0.18  | 38000  | 1.9534          |
| 2.163         | 0.19  | 40000  | 1.9140          |
| 2.1534        | 0.2   | 42000  | 1.9241          |
| 2.146         | 0.21  | 44000  | 1.9162          |
| 2.1403        | 0.22  | 46000  | 1.9030          |
| 2.1309        | 0.23  | 48000  | 1.8741          |
| 2.1174        | 0.24  | 50000  | 1.8834          |
| 2.1157        | 0.25  | 52000  | 1.8666          |
| 2.1116        | 0.26  | 54000  | 1.8870          |
| 2.1062        | 0.27  | 56000  | 1.8837          |
| 2.0994        | 0.28  | 58000  | 1.8638          |
| 2.0924        | 0.29  | 60000  | 1.8766          |
| 2.0874        | 0.3   | 62000  | 1.8712          |
| 2.0805        | 0.31  | 64000  | 1.8792          |
| 2.0746        | 0.32  | 66000  | 1.8586          |
| 2.0684        | 0.32  | 68000  | 1.8819          |
| 2.0678        | 0.33  | 70000  | 1.8529          |
| 2.061         | 0.34  | 72000  | 1.8219          |
| 2.0532        | 0.35  | 74000  | 1.8383          |
| 2.0536        | 0.36  | 76000  | 1.8273          |
| 2.0432        | 0.37  | 78000  | 1.8304          |
| 2.0386        | 0.38  | 80000  | 1.8208          |
| 2.0361        | 0.39  | 82000  | 1.8103          |
| 2.0353        | 0.4   | 84000  | 1.8193          |
| 2.0266        | 0.41  | 86000  | 1.8369          |
| 2.0277        | 0.42  | 88000  | 1.8266          |
| 2.0221        | 0.43  | 90000  | 1.8372          |
| 2.0181        | 0.44  | 92000  | 1.8436          |
| 2.0182        | 0.45  | 94000  | 1.8505          |
| 2.0088        | 0.46  | 96000  | 1.8127          |
| 2.005         | 0.47  | 98000  | 1.8325          |
| 2.0003        | 0.48  | 100000 | 1.8407          |
| 2.0031        | 0.49  | 102000 | 1.8140          |
| 1.9954        | 0.5   | 104000 | 1.8177          |
| 1.9894        | 0.51  | 106000 | 1.8072          |
| 1.9901        | 0.52  | 108000 | 1.7971          |
| 1.9864        | 0.53  | 110000 | 1.8007          |
| 1.9848        | 0.53  | 112000 | 1.7961          |
| 1.9774        | 0.54  | 114000 | 1.7933          |
| 1.9802        | 0.55  | 116000 | 1.8031          |
| 1.9698        | 0.56  | 118000 | 1.8137          |
| 1.973         | 0.57  | 120000 | 1.7930          |
| 1.9696        | 0.58  | 122000 | 1.7838          |
| 1.9641        | 0.59  | 124000 | 1.7730          |
| 1.9609        | 0.6   | 126000 | 1.7800          |
| 1.9605        | 0.61  | 128000 | 1.7680          |
| 1.9516        | 0.62  | 130000 | 1.7895          |
| 1.9529        | 0.63  | 132000 | 1.7825          |
| 1.9503        | 0.64  | 134000 | 1.7792          |
| 1.9528        | 0.65  | 136000 | 1.8031          |
| 1.9439        | 0.66  | 138000 | 1.7652          |
| 1.9453        | 0.67  | 140000 | 1.7713          |
| 1.9404        | 0.68  | 142000 | 1.7585          |
| 1.9399        | 0.69  | 144000 | 1.7454          |
| 1.9325        | 0.7   | 146000 | 1.7605          |
| 1.9327        | 0.71  | 148000 | 1.7608          |
| 1.9301        | 0.72  | 150000 | 1.7743          |
| 1.928         | 0.73  | 152000 | 1.7532          |
| 1.9286        | 0.74  | 154000 | 1.7682          |
| 1.9194        | 0.74  | 156000 | 1.7582          |
| 1.9247        | 0.75  | 158000 | 1.7601          |
| 1.9183        | 0.76  | 160000 | 1.7600          |
| 1.9138        | 0.77  | 162000 | 1.7555          |
| 1.9148        | 0.78  | 164000 | 1.7447          |
| 1.913         | 0.79  | 166000 | 1.7512          |
| 1.9084        | 0.8   | 168000 | 1.7408          |
| 1.9109        | 0.81  | 170000 | 1.7463          |
| 1.905         | 0.82  | 172000 | 1.7543          |
| 1.9067        | 0.83  | 174000 | 1.7662          |
| 1.9005        | 0.84  | 176000 | 1.7428          |
| 1.8997        | 0.85  | 178000 | 1.7500          |
| 1.8963        | 0.86  | 180000 | 1.7297          |
| 1.8938        | 0.87  | 182000 | 1.7356          |
| 1.8923        | 0.88  | 184000 | 1.7602          |
| 1.8896        | 0.89  | 186000 | 1.7426          |
| 1.8866        | 0.9   | 188000 | 1.7323          |
| 1.887         | 0.91  | 190000 | 1.7587          |
| 1.8855        | 0.92  | 192000 | 1.7591          |
| 1.8842        | 0.93  | 194000 | 1.7570          |
| 1.8808        | 0.94  | 196000 | 1.7311          |
| 1.8836        | 0.95  | 198000 | 1.7449          |
| 1.8761        | 0.96  | 200000 | 1.7534          |
| 1.8721        | 0.96  | 202000 | 1.7623          |
| 1.8765        | 0.97  | 204000 | 1.7462          |
| 1.8747        | 0.98  | 206000 | 1.7452          |
| 1.8667        | 0.99  | 208000 | 1.7303          |
| 1.8618        | 1.0   | 210000 | 1.7468          |
| 1.8475        | 1.01  | 212000 | 1.7443          |
| 1.8435        | 1.02  | 214000 | 1.7622          |
| 1.8452        | 1.03  | 216000 | 1.7153          |
| 1.84          | 1.04  | 218000 | 1.6976          |
| 1.8432        | 1.05  | 220000 | 1.7013          |
| 1.842         | 1.06  | 222000 | 1.7073          |
| 1.8428        | 1.07  | 224000 | 1.6991          |
| 1.841         | 1.08  | 226000 | 1.7477          |
| 1.8321        | 1.09  | 228000 | 1.7438          |
| 1.838         | 1.1   | 230000 | 1.7352          |
| 1.8339        | 1.11  | 232000 | 1.7242          |
| 1.836         | 1.12  | 234000 | 1.7221          |
| 1.8329        | 1.13  | 236000 | 1.7402          |
| 1.8337        | 1.14  | 238000 | 1.7083          |
| 1.8267        | 1.15  | 240000 | 1.7200          |
| 1.8335        | 1.16  | 242000 | 1.7092          |
| 1.8306        | 1.17  | 244000 | 1.7340          |
| 1.8279        | 1.17  | 246000 | 1.6983          |
| 1.8261        | 1.18  | 248000 | 1.6928          |
| 1.8295        | 1.19  | 250000 | 1.7135          |
| 1.8227        | 1.2   | 252000 | 1.7156          |
| 1.822         | 1.21  | 254000 | 1.7018          |
| 1.8216        | 1.22  | 256000 | 1.7157          |
| 1.8205        | 1.23  | 258000 | 1.7047          |
| 1.8163        | 1.24  | 260000 | 1.6988          |
| 1.8187        | 1.25  | 262000 | 1.7077          |
| 1.8188        | 1.26  | 264000 | 1.6859          |
| 1.8138        | 1.27  | 266000 | 1.6831          |
| 1.8173        | 1.28  | 268000 | 1.6887          |
| 1.813         | 1.29  | 270000 | 1.6967          |
| 1.8114        | 1.3   | 272000 | 1.7085          |
| 1.8057        | 1.31  | 274000 | 1.6885          |
| 1.8094        | 1.32  | 276000 | 1.7198          |
| 1.8079        | 1.33  | 278000 | 1.7036          |
| 1.8056        | 1.34  | 280000 | 1.7106          |
| 1.8044        | 1.35  | 282000 | 1.6704          |
| 1.8047        | 1.36  | 284000 | 1.6811          |
| 1.7978        | 1.37  | 286000 | 1.6848          |
| 1.7997        | 1.38  | 288000 | 1.6698          |
| 1.7997        | 1.38  | 290000 | 1.6820          |
| 1.7945        | 1.39  | 292000 | 1.6963          |
| 1.7958        | 1.4   | 294000 | 1.6922          |
| 1.7923        | 1.41  | 296000 | 1.6577          |
| 1.7975        | 1.42  | 298000 | 1.6621          |
| 1.7914        | 1.43  | 300000 | 1.6804          |
| 1.7944        | 1.44  | 302000 | 1.6953          |
| 1.7927        | 1.45  | 304000 | 1.6846          |
| 1.789         | 1.46  | 306000 | 1.6889          |
| 1.7851        | 1.47  | 308000 | 1.6652          |
| 1.7902        | 1.48  | 310000 | 1.6823          |
| 1.7873        | 1.49  | 312000 | 1.6603          |
| 1.7868        | 1.5   | 314000 | 1.6766          |
| 1.7856        | 1.51  | 316000 | 1.6717          |
| 1.7807        | 1.52  | 318000 | 1.6466          |
| 1.7767        | 1.53  | 320000 | 1.6639          |
| 1.7782        | 1.54  | 322000 | 1.6678          |
| 1.7762        | 1.55  | 324000 | 1.6853          |
| 1.7746        | 1.56  | 326000 | 1.6785          |
| 1.7746        | 1.57  | 328000 | 1.6777          |
| 1.7716        | 1.58  | 330000 | 1.6784          |
| 1.7699        | 1.59  | 332000 | 1.6648          |
| 1.7739        | 1.59  | 334000 | 1.6725          |
| 1.7703        | 1.6   | 336000 | 1.6915          |
| 1.7707        | 1.61  | 338000 | 1.6858          |
| 1.7619        | 1.62  | 340000 | 1.6624          |
| 1.7652        | 1.63  | 342000 | 1.6797          |
| 1.7626        | 1.64  | 344000 | 1.6728          |
| 1.7647        | 1.65  | 346000 | 1.6580          |
| 1.7616        | 1.66  | 348000 | 1.6679          |
| 1.7616        | 1.67  | 350000 | 1.6470          |
| 1.7611        | 1.68  | 352000 | 1.6489          |
| 1.759         | 1.69  | 354000 | 1.6603          |
| 1.7604        | 1.7   | 356000 | 1.6532          |
| 1.7599        | 1.71  | 358000 | 1.6477          |
| 1.7529        | 1.72  | 360000 | 1.6322          |
| 1.7596        | 1.73  | 362000 | 1.6447          |
| 1.7508        | 1.74  | 364000 | 1.6509          |
| 1.7533        | 1.75  | 366000 | 1.6465          |
| 1.755         | 1.76  | 368000 | 1.6485          |
| 1.7473        | 1.77  | 370000 | 1.6493          |
| 1.7435        | 1.78  | 372000 | 1.6542          |
| 1.7483        | 1.79  | 374000 | 1.6573          |
| 1.7475        | 1.8   | 376000 | 1.6626          |
| 1.7439        | 1.8   | 378000 | 1.6366          |
| 1.7417        | 1.81  | 380000 | 1.6312          |
| 1.7387        | 1.82  | 382000 | 1.6424          |
| 1.7415        | 1.83  | 384000 | 1.6468          |
| 1.7409        | 1.84  | 386000 | 1.6528          |
| 1.7362        | 1.85  | 388000 | 1.6394          |
| 1.7372        | 1.86  | 390000 | 1.6581          |
| 1.7347        | 1.87  | 392000 | 1.6546          |
| 1.7368        | 1.88  | 394000 | 1.6468          |
| 1.7302        | 1.89  | 396000 | 1.6450          |
| 1.7317        | 1.9   | 398000 | 1.6368          |
| 1.7306        | 1.91  | 400000 | 1.6399          |
| 1.7304        | 1.92  | 402000 | 1.6180          |
| 1.726         | 1.93  | 404000 | 1.6212          |
| 1.7271        | 1.94  | 406000 | 1.6302          |
| 1.7312        | 1.95  | 408000 | 1.6264          |
| 1.7249        | 1.96  | 410000 | 1.6584          |
| 1.7226        | 1.97  | 412000 | 1.6514          |
| 1.7214        | 1.98  | 414000 | 1.6516          |
| 1.7228        | 1.99  | 416000 | 1.6346          |
| 1.7205        | 2.0   | 418000 | 1.6370          |
| 1.7041        | 2.01  | 420000 | 1.6021          |
| 1.691         | 2.02  | 422000 | 1.6385          |
| 1.6896        | 2.02  | 424000 | 1.6280          |
| 1.6882        | 2.03  | 426000 | 1.6295          |
| 1.6889        | 2.04  | 428000 | 1.6445          |
| 1.6904        | 2.05  | 430000 | 1.6558          |
| 1.6933        | 2.06  | 432000 | 1.6164          |
| 1.6916        | 2.07  | 434000 | 1.6011          |
| 1.6873        | 2.08  | 436000 | 1.6199          |
| 1.6903        | 2.09  | 438000 | 1.6300          |
| 1.6859        | 2.1   | 440000 | 1.6104          |
| 1.6901        | 2.11  | 442000 | 1.6248          |
| 1.6884        | 2.12  | 444000 | 1.6251          |
| 1.6859        | 2.13  | 446000 | 1.6145          |
| 1.6906        | 2.14  | 448000 | 1.6181          |
| 1.6859        | 2.15  | 450000 | 1.6264          |
| 1.6814        | 2.16  | 452000 | 1.6069          |
| 1.6853        | 2.17  | 454000 | 1.6089          |
| 1.6881        | 2.18  | 456000 | 1.6102          |
| 1.6869        | 2.19  | 458000 | 1.6327          |
| 1.6827        | 2.2   | 460000 | 1.6069          |
| 1.6813        | 2.21  | 462000 | 1.6278          |
| 1.6806        | 2.22  | 464000 | 1.6176          |
| 1.6763        | 2.23  | 466000 | 1.6180          |
| 1.68          | 2.23  | 468000 | 1.6226          |
| 1.6816        | 2.24  | 470000 | 1.6071          |
| 1.6845        | 2.25  | 472000 | 1.6178          |
| 1.6764        | 2.26  | 474000 | 1.6073          |
| 1.682         | 2.27  | 476000 | 1.5966          |
| 1.6727        | 2.28  | 478000 | 1.5979          |
| 1.6718        | 2.29  | 480000 | 1.6109          |
| 1.6764        | 2.3   | 482000 | 1.6034          |
| 1.671         | 2.31  | 484000 | 1.6001          |
| 1.6691        | 2.32  | 486000 | 1.6148          |
| 1.6706        | 2.33  | 488000 | 1.6003          |
| 1.6705        | 2.34  | 490000 | 1.6021          |
| 1.6699        | 2.35  | 492000 | 1.5940          |
| 1.6708        | 2.36  | 494000 | 1.6077          |
| 1.6715        | 2.37  | 496000 | 1.6188          |
| 1.6672        | 2.38  | 498000 | 1.5903          |
| 1.6638        | 2.39  | 500000 | 1.6042          |
| 1.6634        | 2.4   | 502000 | 1.5967          |
| 1.6669        | 2.41  | 504000 | 1.5904          |
| 1.6643        | 2.42  | 506000 | 1.6071          |
| 1.6606        | 2.43  | 508000 | 1.6065          |
| 1.6573        | 2.44  | 510000 | 1.6010          |
| 1.6603        | 2.44  | 512000 | 1.5801          |
| 1.6568        | 2.45  | 514000 | 1.5961          |
| 1.6564        | 2.46  | 516000 | 1.6020          |
| 1.6596        | 2.47  | 518000 | 1.5952          |
| 1.6567        | 2.48  | 520000 | 1.5760          |
| 1.6536        | 2.49  | 522000 | 1.5697          |
| 1.6564        | 2.5   | 524000 | 1.5664          |
| 1.652         | 2.51  | 526000 | 1.5616          |
| 1.653         | 2.52  | 528000 | 1.5738          |
| 1.6525        | 2.53  | 530000 | 1.5754          |
| 1.65          | 2.54  | 532000 | 1.5749          |
| 1.6519        | 2.55  | 534000 | 1.5788          |
| 1.6515        | 2.56  | 536000 | 1.5953          |
| 1.6492        | 2.57  | 538000 | 1.5836          |
| 1.6473        | 2.58  | 540000 | 1.5896          |
| 1.6452        | 2.59  | 542000 | 1.5858          |
| 1.6464        | 2.6   | 544000 | 1.5760          |
| 1.6445        | 2.61  | 546000 | 1.5683          |
| 1.6457        | 2.62  | 548000 | 1.5823          |
| 1.6417        | 2.63  | 550000 | 1.5780          |
| 1.6407        | 2.64  | 552000 | 1.5715          |
| 1.6368        | 2.65  | 554000 | 1.5618          |
| 1.6357        | 2.65  | 556000 | 1.5725          |
| 1.6446        | 2.66  | 558000 | 1.5744          |
| 1.634         | 2.67  | 560000 | 1.5360          |
| 1.6351        | 2.68  | 562000 | 1.5599          |
| 1.6362        | 2.69  | 564000 | 1.5607          |
| 1.637         | 2.7   | 566000 | 1.5561          |
| 1.6324        | 2.71  | 568000 | 1.5591          |
| 1.6325        | 2.72  | 570000 | 1.5527          |
| 1.6323        | 2.73  | 572000 | 1.5537          |
| 1.629         | 2.74  | 574000 | 1.5673          |
| 1.627         | 2.75  | 576000 | 1.5509          |
| 1.6279        | 2.76  | 578000 | 1.5507          |
| 1.6291        | 2.77  | 580000 | 1.5304          |
| 1.625         | 2.78  | 582000 | 1.5540          |
| 1.6246        | 2.79  | 584000 | 1.5530          |
| 1.6228        | 2.8   | 586000 | 1.5570          |
| 1.6241        | 2.81  | 588000 | 1.5586          |
| 1.6224        | 2.82  | 590000 | 1.5480          |
| 1.6264        | 2.83  | 592000 | 1.5624          |
| 1.6214        | 2.84  | 594000 | 1.5565          |
| 1.6187        | 2.85  | 596000 | 1.5397          |
| 1.6191        | 2.86  | 598000 | 1.5520          |
| 1.6192        | 2.87  | 600000 | 1.5494          |
| 1.6182        | 2.87  | 602000 | 1.5608          |
| 1.6164        | 2.88  | 604000 | 1.5428          |
| 1.6107        | 2.89  | 606000 | 1.5525          |
| 1.614         | 2.9   | 608000 | 1.5277          |
| 1.6158        | 2.91  | 610000 | 1.5502          |
| 1.6082        | 2.92  | 612000 | 1.5452          |
| 1.6089        | 2.93  | 614000 | 1.5400          |
| 1.6112        | 2.94  | 616000 | 1.5322          |
| 1.6069        | 2.95  | 618000 | 1.5394          |
| 1.6111        | 2.96  | 620000 | 1.5537          |
| 1.6038        | 2.97  | 622000 | 1.5486          |
| 1.6073        | 2.98  | 624000 | 1.5551          |
| 1.6046        | 2.99  | 626000 | 1.5386          |
| 1.6051        | 3.0   | 628000 | 1.5369          |
| 1.5672        | 3.01  | 630000 | 1.5361          |
| 1.5694        | 3.02  | 632000 | 1.5390          |
| 1.5692        | 3.03  | 634000 | 1.5386          |
| 1.5651        | 3.04  | 636000 | 1.5456          |
| 1.5724        | 3.05  | 638000 | 1.5419          |
| 1.5708        | 3.06  | 640000 | 1.5363          |
| 1.5665        | 3.07  | 642000 | 1.5446          |
| 1.5706        | 3.08  | 644000 | 1.5331          |
| 1.5679        | 3.08  | 646000 | 1.5449          |
| 1.5678        | 3.09  | 648000 | 1.5436          |
| 1.5676        | 3.1   | 650000 | 1.5309          |
| 1.5657        | 3.11  | 652000 | 1.5334          |
| 1.5697        | 3.12  | 654000 | 1.5303          |
| 1.5617        | 3.13  | 656000 | 1.5380          |
| 1.5675        | 3.14  | 658000 | 1.5404          |
| 1.5612        | 3.15  | 660000 | 1.5258          |
| 1.5639        | 3.16  | 662000 | 1.5329          |
| 1.567         | 3.17  | 664000 | 1.5418          |
| 1.5619        | 3.18  | 666000 | 1.5314          |
| 1.5637        | 3.19  | 668000 | 1.5201          |
| 1.5608        | 3.2   | 670000 | 1.5181          |
| 1.5641        | 3.21  | 672000 | 1.5290          |
| 1.5626        | 3.22  | 674000 | 1.5180          |
| 1.5605        | 3.23  | 676000 | 1.5156          |
| 1.5566        | 3.24  | 678000 | 1.5266          |
| 1.5587        | 3.25  | 680000 | 1.5286          |
| 1.5602        | 3.26  | 682000 | 1.5265          |
| 1.5535        | 3.27  | 684000 | 1.5354          |
| 1.5589        | 3.28  | 686000 | 1.5265          |
| 1.5569        | 3.29  | 688000 | 1.5346          |
| 1.559         | 3.29  | 690000 | 1.5306          |
| 1.5507        | 3.3   | 692000 | 1.5359          |
| 1.5547        | 3.31  | 694000 | 1.5264          |
| 1.5498        | 3.32  | 696000 | 1.5264          |
| 1.5559        | 3.33  | 698000 | 1.5273          |
| 1.553         | 3.34  | 700000 | 1.5137          |
| 1.5503        | 3.35  | 702000 | 1.5143          |
| 1.5498        | 3.36  | 704000 | 1.5263          |
| 1.5516        | 3.37  | 706000 | 1.5096          |
| 1.5461        | 3.38  | 708000 | 1.5112          |
| 1.5489        | 3.39  | 710000 | 1.5094          |
| 1.5451        | 3.4   | 712000 | 1.5079          |
| 1.544         | 3.41  | 714000 | 1.5058          |
| 1.5446        | 3.42  | 716000 | 1.5005          |
| 1.5417        | 3.43  | 718000 | 1.4972          |
| 1.5469        | 3.44  | 720000 | 1.5043          |
| 1.5407        | 3.45  | 722000 | 1.5041          |
| 1.5484        | 3.46  | 724000 | 1.5104          |
| 1.5409        | 3.47  | 726000 | 1.5087          |
| 1.5431        | 3.48  | 728000 | 1.5114          |
| 1.5393        | 3.49  | 730000 | 1.5102          |
| 1.5364        | 3.5   | 732000 | 1.5143          |
| 1.5403        | 3.5   | 734000 | 1.5202          |
| 1.5386        | 3.51  | 736000 | 1.5143          |
| 1.5381        | 3.52  | 738000 | 1.5198          |
| 1.5341        | 3.53  | 740000 | 1.5136          |
| 1.5344        | 3.54  | 742000 | 1.5172          |
| 1.5347        | 3.55  | 744000 | 1.5149          |
| 1.5292        | 3.56  | 746000 | 1.5141          |
| 1.5344        | 3.57  | 748000 | 1.5066          |
| 1.5307        | 3.58  | 750000 | 1.5087          |
| 1.5324        | 3.59  | 752000 | 1.5113          |
| 1.5273        | 3.6   | 754000 | 1.5101          |
| 1.5273        | 3.61  | 756000 | 1.4975          |
| 1.5282        | 3.62  | 758000 | 1.5053          |
| 1.5252        | 3.63  | 760000 | 1.4998          |
| 1.525         | 3.64  | 762000 | 1.5020          |
| 1.5297        | 3.65  | 764000 | 1.5075          |
| 1.5215        | 3.66  | 766000 | 1.4980          |
| 1.5237        | 3.67  | 768000 | 1.5066          |
| 1.5248        | 3.68  | 770000 | 1.5093          |
| 1.5231        | 3.69  | 772000 | 1.5090          |
| 1.5224        | 3.7   | 774000 | 1.5093          |
| 1.526         | 3.71  | 776000 | 1.5015          |
| 1.5215        | 3.71  | 778000 | 1.5045          |
| 1.5231        | 3.72  | 780000 | 1.4971          |
| 1.5205        | 3.73  | 782000 | 1.4987          |
| 1.5171        | 3.74  | 784000 | 1.5001          |
| 1.5134        | 3.75  | 786000 | 1.4951          |
| 1.5155        | 3.76  | 788000 | 1.4975          |
| 1.5154        | 3.77  | 790000 | 1.4928          |
| 1.5167        | 3.78  | 792000 | 1.4983          |
| 1.5146        | 3.79  | 794000 | 1.4938          |
| 1.5138        | 3.8   | 796000 | 1.4985          |
| 1.5137        | 3.81  | 798000 | 1.5021          |
| 1.5111        | 3.82  | 800000 | 1.5020          |
| 1.5134        | 3.83  | 802000 | 1.4998          |
| 1.5086        | 3.84  | 804000 | 1.5001          |
| 1.5081        | 3.85  | 806000 | 1.5031          |
| 1.5097        | 3.86  | 808000 | 1.5008          |
| 1.5128        | 3.87  | 810000 | 1.4990          |
| 1.5093        | 3.88  | 812000 | 1.4994          |
| 1.5109        | 3.89  | 814000 | 1.5021          |
| 1.5049        | 3.9   | 816000 | 1.5012          |
| 1.5042        | 3.91  | 818000 | 1.5013          |
| 1.5053        | 3.92  | 820000 | 1.4946          |
| 1.5066        | 3.93  | 822000 | 1.4984          |
| 1.5074        | 3.93  | 824000 | 1.4963          |
| 1.5046        | 3.94  | 826000 | 1.4972          |
| 1.5043        | 3.95  | 828000 | 1.4970          |
| 1.5064        | 3.96  | 830000 | 1.4940          |
| 1.4999        | 3.97  | 832000 | 1.4940          |
| 1.5022        | 3.98  | 834000 | 1.4934          |
| 1.5054        | 3.99  | 836000 | 1.4929          |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MIX2_ja-en_helsinki", "results": []}]}
transformers pytorch marian text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

MIX2\_ja-en\_helsinki
=====================


This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4929
* Otaku Benchmark VN BLEU: 20.21
* Otaku Benchmark LN BLEU: 13.29
* Otaku Benchmark MANGA BLEU: 19.07


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: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1