pytorch marian text2text-generation transformers generated_from_trainer license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: LN_ja-en_helsinki
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# LN_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: 2.5382

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.5108        | 0.02  | 2000   | 2.8405          |
| 2.2729        | 0.04  | 4000   | 2.7758          |
| 2.1673        | 0.06  | 6000   | 2.7098          |
| 2.0981        | 0.08  | 8000   | 2.6487          |
| 2.048         | 0.1   | 10000  | 2.7008          |
| 2.0077        | 0.12  | 12000  | 2.6614          |
| 1.9677        | 0.13  | 14000  | 2.6227          |
| 1.9445        | 0.15  | 16000  | 2.5895          |
| 1.9137        | 0.17  | 18000  | 2.5897          |
| 1.8911        | 0.19  | 20000  | 2.6771          |
| 1.8695        | 0.21  | 22000  | 2.6332          |
| 1.8479        | 0.23  | 24000  | 2.6130          |
| 1.8378        | 0.25  | 26000  | 2.6518          |
| 1.8191        | 0.27  | 28000  | 2.6401          |
| 1.8024        | 0.29  | 30000  | 2.6617          |
| 1.7933        | 0.31  | 32000  | 2.6705          |
| 1.7787        | 0.33  | 34000  | 2.6280          |
| 1.7661        | 0.35  | 36000  | 2.6911          |
| 1.7514        | 0.36  | 38000  | 2.6766          |
| 1.7444        | 0.38  | 40000  | 2.6996          |
| 1.7363        | 0.4   | 42000  | 2.6276          |
| 1.722         | 0.42  | 44000  | 2.6466          |
| 1.7177        | 0.44  | 46000  | 2.5937          |
| 1.7055        | 0.46  | 48000  | 2.6386          |
| 1.6956        | 0.48  | 50000  | 2.6794          |
| 1.6885        | 0.5   | 52000  | 2.7336          |
| 1.679         | 0.52  | 54000  | 2.7266          |
| 1.6715        | 0.54  | 56000  | 2.6945          |
| 1.6666        | 0.56  | 58000  | 2.7111          |
| 1.6599        | 0.58  | 60000  | 2.7205          |
| 1.6566        | 0.59  | 62000  | 2.7194          |
| 1.6481        | 0.61  | 64000  | 2.6582          |
| 1.6434        | 0.63  | 66000  | 2.6859          |
| 1.6315        | 0.65  | 68000  | 2.7058          |
| 1.6258        | 0.67  | 70000  | 2.7428          |
| 1.6189        | 0.69  | 72000  | 2.7411          |
| 1.6169        | 0.71  | 74000  | 2.7039          |
| 1.6087        | 0.73  | 76000  | 2.6844          |
| 1.6021        | 0.75  | 78000  | 2.6454          |
| 1.6034        | 0.77  | 80000  | 2.6596          |
| 1.5941        | 0.79  | 82000  | 2.6903          |
| 1.5862        | 0.81  | 84000  | 2.7099          |
| 1.5836        | 0.83  | 86000  | 2.6929          |
| 1.5827        | 0.84  | 88000  | 2.7181          |
| 1.5747        | 0.86  | 90000  | 2.6888          |
| 1.5678        | 0.88  | 92000  | 2.6662          |
| 1.5643        | 0.9   | 94000  | 2.6663          |
| 1.561         | 0.92  | 96000  | 2.6699          |
| 1.5565        | 0.94  | 98000  | 2.6667          |
| 1.5501        | 0.96  | 100000 | 2.6828          |
| 1.5476        | 0.98  | 102000 | 2.6531          |
| 1.5444        | 1.0   | 104000 | 2.6799          |
| 1.5057        | 1.02  | 106000 | 2.6525          |
| 1.5003        | 1.04  | 108000 | 2.6996          |
| 1.4996        | 1.06  | 110000 | 2.6649          |
| 1.4996        | 1.07  | 112000 | 2.6974          |
| 1.4966        | 1.09  | 114000 | 2.7594          |
| 1.4967        | 1.11  | 116000 | 2.6966          |
| 1.492         | 1.13  | 118000 | 2.6929          |
| 1.4923        | 1.15  | 120000 | 2.6522          |
| 1.4838        | 1.17  | 122000 | 2.6363          |
| 1.4839        | 1.19  | 124000 | 2.6849          |
| 1.4807        | 1.21  | 126000 | 2.6667          |
| 1.4778        | 1.23  | 128000 | 2.6684          |
| 1.4731        | 1.25  | 130000 | 2.6338          |
| 1.4727        | 1.27  | 132000 | 2.6093          |
| 1.4695        | 1.29  | 134000 | 2.6020          |
| 1.4656        | 1.3   | 136000 | 2.6341          |
| 1.4648        | 1.32  | 138000 | 2.6509          |
| 1.4578        | 1.34  | 140000 | 2.6807          |
| 1.4606        | 1.36  | 142000 | 2.6357          |
| 1.4529        | 1.38  | 144000 | 2.6404          |
| 1.4488        | 1.4   | 146000 | 2.6347          |
| 1.4442        | 1.42  | 148000 | 2.6058          |
| 1.4447        | 1.44  | 150000 | 2.6645          |
| 1.4432        | 1.46  | 152000 | 2.6070          |
| 1.437         | 1.48  | 154000 | 2.5987          |
| 1.4345        | 1.5   | 156000 | 2.6309          |
| 1.43          | 1.52  | 158000 | 2.5947          |
| 1.4301        | 1.54  | 160000 | 2.5938          |
| 1.4267        | 1.55  | 162000 | 2.6146          |
| 1.426         | 1.57  | 164000 | 2.6519          |
| 1.4193        | 1.59  | 166000 | 2.6163          |
| 1.416         | 1.61  | 168000 | 2.5793          |
| 1.4146        | 1.63  | 170000 | 2.6031          |
| 1.4091        | 1.65  | 172000 | 2.5826          |
| 1.4067        | 1.67  | 174000 | 2.5891          |
| 1.4081        | 1.69  | 176000 | 2.6006          |
| 1.4023        | 1.71  | 178000 | 2.5697          |
| 1.4003        | 1.73  | 180000 | 2.5633          |
| 1.3986        | 1.75  | 182000 | 2.5494          |
| 1.3924        | 1.77  | 184000 | 2.5577          |
| 1.3931        | 1.78  | 186000 | 2.5888          |
| 1.3851        | 1.8   | 188000 | 2.5716          |
| 1.3869        | 1.82  | 190000 | 2.5570          |
| 1.3825        | 1.84  | 192000 | 2.5702          |
| 1.3787        | 1.86  | 194000 | 2.5754          |
| 1.3738        | 1.88  | 196000 | 2.5901          |
| 1.3734        | 1.9   | 198000 | 2.5374          |
| 1.3693        | 1.92  | 200000 | 2.5897          |
| 1.3703        | 1.94  | 202000 | 2.5422          |
| 1.3685        | 1.96  | 204000 | 2.5825          |
| 1.3664        | 1.98  | 206000 | 2.5201          |
| 1.3607        | 2.0   | 208000 | 2.5733          |
| 1.3217        | 2.02  | 210000 | 2.5879          |
| 1.31          | 2.03  | 212000 | 2.5777          |
| 1.3125        | 2.05  | 214000 | 2.5724          |
| 1.3084        | 2.07  | 216000 | 2.5968          |
| 1.3087        | 2.09  | 218000 | 2.5976          |
| 1.3063        | 2.11  | 220000 | 2.5969          |
| 1.3057        | 2.13  | 222000 | 2.6353          |
| 1.3067        | 2.15  | 224000 | 2.6147          |
| 1.3013        | 2.17  | 226000 | 2.5897          |
| 1.3018        | 2.19  | 228000 | 2.5783          |
| 1.2968        | 2.21  | 230000 | 2.6172          |
| 1.2975        | 2.23  | 232000 | 2.6180          |
| 1.2946        | 2.25  | 234000 | 2.6192          |
| 1.299         | 2.26  | 236000 | 2.5895          |
| 1.2896        | 2.28  | 238000 | 2.5682          |
| 1.287         | 2.3   | 240000 | 2.5653          |
| 1.2902        | 2.32  | 242000 | 2.5501          |
| 1.2862        | 2.34  | 244000 | 2.5747          |
| 1.2841        | 2.36  | 246000 | 2.5654          |
| 1.2838        | 2.38  | 248000 | 2.5703          |
| 1.2813        | 2.4   | 250000 | 2.5919          |
| 1.2778        | 2.42  | 252000 | 2.5552          |
| 1.2821        | 2.44  | 254000 | 2.5603          |
| 1.2729        | 2.46  | 256000 | 2.5455          |
| 1.2718        | 2.48  | 258000 | 2.5688          |
| 1.2729        | 2.49  | 260000 | 2.5574          |
| 1.2699        | 2.51  | 262000 | 2.5468          |
| 1.2677        | 2.53  | 264000 | 2.5704          |
| 1.2647        | 2.55  | 266000 | 2.5665          |
| 1.2628        | 2.57  | 268000 | 2.5594          |
| 1.2636        | 2.59  | 270000 | 2.5426          |
| 1.2573        | 2.61  | 272000 | 2.5666          |
| 1.2576        | 2.63  | 274000 | 2.5580          |
| 1.2511        | 2.65  | 276000 | 2.5742          |
| 1.2513        | 2.67  | 278000 | 2.5646          |
| 1.2495        | 2.69  | 280000 | 2.5669          |
| 1.2472        | 2.71  | 282000 | 2.5700          |
| 1.2478        | 2.73  | 284000 | 2.5496          |
| 1.2471        | 2.74  | 286000 | 2.5335          |
| 1.2436        | 2.76  | 288000 | 2.5315          |
| 1.2411        | 2.78  | 290000 | 2.5302          |
| 1.2391        | 2.8   | 292000 | 2.5290          |
| 1.2352        | 2.82  | 294000 | 2.5303          |
| 1.2332        | 2.84  | 296000 | 2.5412          |
| 1.233         | 2.86  | 298000 | 2.5523          |
| 1.2298        | 2.88  | 300000 | 2.5524          |
| 1.2285        | 2.9   | 302000 | 2.5517          |
| 1.2297        | 2.92  | 304000 | 2.5419          |
| 1.2256        | 2.94  | 306000 | 2.5404          |
| 1.2239        | 2.96  | 308000 | 2.5390          |
| 1.2264        | 2.97  | 310000 | 2.5364          |
| 1.2259        | 2.99  | 312000 | 2.5382          |


### Framework versions

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