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---
license: mit
base_model: facebook/m2m100_418M
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: m2m100_418M-finetuned-hi-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m100_418M-finetuned-hi-to-en
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1973
- Bleu: 0.0
- Gen Len: 5.7184
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
| 2.6398 | 0.1100 | 500 | 2.5624 | 2.434 | 5.8204 |
| 2.6877 | 0.2199 | 1000 | 2.4067 | 6.9764 | 5.6658 |
| 2.6 | 0.3299 | 1500 | 2.3000 | 4.9574 | 5.6818 |
| 2.5495 | 0.4399 | 2000 | 2.2093 | 13.5783 | 5.7773 |
| 2.4986 | 0.5498 | 2500 | 2.1232 | 12.0884 | 5.7156 |
| 2.4475 | 0.6598 | 3000 | 2.0526 | 0.0 | 5.7829 |
| 2.418 | 0.7697 | 3500 | 1.9804 | 0.0 | 5.7902 |
| 2.3652 | 0.8797 | 4000 | 1.9253 | 0.0 | 5.7564 |
| 2.3625 | 0.9897 | 4500 | 1.8681 | 0.0 | 5.7984 |
| 2.024 | 1.0996 | 5000 | 1.8020 | 0.0 | 5.81 |
| 2.0017 | 1.2096 | 5500 | 1.7601 | 0.0 | 5.7493 |
| 2.0036 | 1.3196 | 6000 | 1.7208 | 0.0 | 5.8507 |
| 1.9983 | 1.4295 | 6500 | 1.6662 | 0.0 | 5.742 |
| 1.9838 | 1.5395 | 7000 | 1.6273 | 0.0 | 5.8033 |
| 1.9755 | 1.6494 | 7500 | 1.5914 | 0.0 | 5.8629 |
| 1.9679 | 1.7594 | 8000 | 1.5436 | 0.0 | 5.8751 |
| 1.9386 | 1.8694 | 8500 | 1.5154 | 0.0 | 5.8762 |
| 1.9299 | 1.9793 | 9000 | 1.4725 | 0.0 | 5.82 |
| 1.6886 | 2.0893 | 9500 | 1.4242 | 0.0 | 5.7729 |
| 1.6454 | 2.1993 | 10000 | 1.3867 | 0.0 | 5.7042 |
| 1.6361 | 2.3092 | 10500 | 1.3544 | 0.0 | 5.6789 |
| 1.6482 | 2.4192 | 11000 | 1.3346 | 0.0 | 5.7051 |
| 1.6528 | 2.5291 | 11500 | 1.3043 | 0.0 | 5.7147 |
| 1.6687 | 2.6391 | 12000 | 1.2718 | 0.0 | 5.7633 |
| 1.6428 | 2.7491 | 12500 | 1.2417 | 0.0 | 5.7318 |
| 1.6547 | 2.8590 | 13000 | 1.2086 | 0.0 | 5.7536 |
| 1.6467 | 2.9690 | 13500 | 1.1895 | 0.0 | 5.7458 |
| 1.4526 | 3.0790 | 14000 | 1.1425 | 0.0 | 5.7869 |
| 1.3555 | 3.1889 | 14500 | 1.1204 | 0.0 | 5.7491 |
| 1.4007 | 3.2989 | 15000 | 1.1010 | 0.0 | 5.8267 |
| 1.3799 | 3.4088 | 15500 | 1.0754 | 0.0 | 5.7482 |
| 1.401 | 3.5188 | 16000 | 1.0460 | 0.0 | 5.7571 |
| 1.4093 | 3.6288 | 16500 | 1.0239 | 0.0 | 5.7262 |
| 1.3997 | 3.7387 | 17000 | 1.0024 | 0.0 | 5.692 |
| 1.4162 | 3.8487 | 17500 | 0.9869 | 0.0 | 5.7273 |
| 1.4102 | 3.9587 | 18000 | 0.9558 | 0.0 | 5.7613 |
| 1.2476 | 4.0686 | 18500 | 0.9296 | 0.0 | 5.7113 |
| 1.1591 | 4.1786 | 19000 | 0.9163 | 0.0 | 5.7651 |
| 1.1861 | 4.2885 | 19500 | 0.9017 | 0.0 | 5.7498 |
| 1.1799 | 4.3985 | 20000 | 0.8841 | 0.0 | 5.7884 |
| 1.1902 | 4.5085 | 20500 | 0.8635 | 0.0 | 5.7613 |
| 1.193 | 4.6184 | 21000 | 0.8448 | 0.0 | 5.7507 |
| 1.1955 | 4.7284 | 21500 | 0.8266 | 0.0 | 5.7602 |
| 1.2062 | 4.8384 | 22000 | 0.8069 | 0.0 | 5.7562 |
| 1.2058 | 4.9483 | 22500 | 0.7805 | 0.0 | 5.7087 |
| 1.0832 | 5.0583 | 23000 | 0.7583 | 0.0 | 5.7631 |
| 0.9869 | 5.1682 | 23500 | 0.7497 | 0.0 | 5.7284 |
| 0.9956 | 5.2782 | 24000 | 0.7356 | 0.0 | 5.7438 |
| 1.0164 | 5.3882 | 24500 | 0.7253 | 0.0 | 5.7789 |
| 1.017 | 5.4981 | 25000 | 0.7075 | 0.0 | 5.7462 |
| 1.0365 | 5.6081 | 25500 | 0.6890 | 0.0 | 5.7487 |
| 1.0421 | 5.7181 | 26000 | 0.6770 | 0.0 | 5.7547 |
| 1.0344 | 5.8280 | 26500 | 0.6560 | 0.0 | 5.7624 |
| 1.0286 | 5.9380 | 27000 | 0.6429 | 0.0 | 5.7816 |
| 0.9637 | 6.0479 | 27500 | 0.6257 | 0.0 | 5.7547 |
| 0.8297 | 6.1579 | 28000 | 0.6144 | 0.0 | 5.7649 |
| 0.8625 | 6.2679 | 28500 | 0.6038 | 0.0 | 5.7442 |
| 0.8587 | 6.3778 | 29000 | 0.5889 | 0.0 | 5.7633 |
| 0.8732 | 6.4878 | 29500 | 0.5788 | 0.0 | 5.7676 |
| 0.8738 | 6.5978 | 30000 | 0.5673 | 0.0 | 5.7698 |
| 0.8938 | 6.7077 | 30500 | 0.5521 | 0.0 | 5.7929 |
| 0.8797 | 6.8177 | 31000 | 0.5410 | 0.0 | 5.7542 |
| 0.9055 | 6.9276 | 31500 | 0.5284 | 0.0 | 5.7551 |
| 0.8408 | 7.0376 | 32000 | 0.5154 | 0.0 | 5.754 |
| 0.7278 | 7.1476 | 32500 | 0.5106 | 0.0 | 5.7602 |
| 0.7357 | 7.2575 | 33000 | 0.4958 | 0.0 | 5.7422 |
| 0.7498 | 7.3675 | 33500 | 0.4906 | 0.0 | 5.734 |
| 0.7524 | 7.4775 | 34000 | 0.4804 | 0.0 | 5.7136 |
| 0.7609 | 7.5874 | 34500 | 0.4716 | 0.0 | 5.7504 |
| 0.7555 | 7.6974 | 35000 | 0.4621 | 38.6861 | 5.7544 |
| 0.7752 | 7.8073 | 35500 | 0.4493 | 0.0 | 5.7429 |
| 0.7656 | 7.9173 | 36000 | 0.4387 | 0.0 | 5.7484 |
| 0.7329 | 8.0273 | 36500 | 0.4281 | 0.0 | 5.7364 |
| 0.6314 | 8.1372 | 37000 | 0.4251 | 0.0 | 5.7453 |
| 0.6595 | 8.2472 | 37500 | 0.4161 | 0.0 | 5.7393 |
| 0.6566 | 8.3572 | 38000 | 0.4125 | 0.0 | 5.7502 |
| 0.6582 | 8.4671 | 38500 | 0.4043 | 0.0 | 5.7364 |
| 0.6579 | 8.5771 | 39000 | 0.3962 | 0.0 | 5.7422 |
| 0.6622 | 8.6870 | 39500 | 0.3878 | 0.0 | 5.76 |
| 0.6547 | 8.7970 | 40000 | 0.3790 | 0.0 | 5.7642 |
| 0.6682 | 8.9070 | 40500 | 0.3701 | 0.0 | 5.7549 |
| 0.6499 | 9.0169 | 41000 | 0.3584 | 0.0 | 5.7333 |
| 0.541 | 9.1269 | 41500 | 0.3547 | 0.0 | 5.7398 |
| 0.5621 | 9.2369 | 42000 | 0.3519 | 0.0 | 5.7322 |
| 0.5673 | 9.3468 | 42500 | 0.3458 | 0.0 | 5.7467 |
| 0.5618 | 9.4568 | 43000 | 0.3407 | 0.0 | 5.7382 |
| 0.5704 | 9.5667 | 43500 | 0.3326 | 0.0 | 5.7536 |
| 0.5816 | 9.6767 | 44000 | 0.3292 | 0.0 | 5.7349 |
| 0.5892 | 9.7867 | 44500 | 0.3194 | 0.0 | 5.7358 |
| 0.5796 | 9.8966 | 45000 | 0.3129 | 0.0 | 5.7369 |
| 0.5807 | 10.0066 | 45500 | 0.3079 | 0.0 | 5.7404 |
| 0.4786 | 10.1166 | 46000 | 0.3033 | 0.0 | 5.7491 |
| 0.4863 | 10.2265 | 46500 | 0.2989 | 0.0 | 5.7331 |
| 0.4979 | 10.3365 | 47000 | 0.2968 | 0.0 | 5.732 |
| 0.5015 | 10.4464 | 47500 | 0.2917 | 0.0 | 5.7229 |
| 0.5105 | 10.5564 | 48000 | 0.2886 | 0.0 | 5.7398 |
| 0.5039 | 10.6664 | 48500 | 0.2830 | 0.0 | 5.7173 |
| 0.5202 | 10.7763 | 49000 | 0.2789 | 0.0 | 5.7218 |
| 0.5123 | 10.8863 | 49500 | 0.2742 | 0.0 | 5.7276 |
| 0.5043 | 10.9963 | 50000 | 0.2670 | 0.0 | 5.7191 |
| 0.4314 | 11.1062 | 50500 | 0.2661 | 0.0 | 5.7364 |
| 0.4345 | 11.2162 | 51000 | 0.2612 | 0.0 | 5.7262 |
| 0.4411 | 11.3261 | 51500 | 0.2592 | 0.0 | 5.7233 |
| 0.447 | 11.4361 | 52000 | 0.2568 | 0.0 | 5.7344 |
| 0.453 | 11.5461 | 52500 | 0.2528 | 0.0 | 5.7231 |
| 0.4485 | 11.6560 | 53000 | 0.2496 | 0.0 | 5.7311 |
| 0.4472 | 11.7660 | 53500 | 0.2460 | 0.0 | 5.7167 |
| 0.4567 | 11.8760 | 54000 | 0.2412 | 0.0 | 5.7256 |
| 0.4528 | 11.9859 | 54500 | 0.2381 | 0.0 | 5.7264 |
| 0.404 | 12.0959 | 55000 | 0.2342 | 0.0 | 5.7187 |
| 0.3995 | 12.2059 | 55500 | 0.2333 | 0.0 | 5.7293 |
| 0.3989 | 12.3158 | 56000 | 0.2317 | 0.0 | 5.7104 |
| 0.3988 | 12.4258 | 56500 | 0.2284 | 0.0 | 5.7242 |
| 0.3991 | 12.5357 | 57000 | 0.2261 | 0.0 | 5.7276 |
| 0.4075 | 12.6457 | 57500 | 0.2234 | 0.0 | 5.7198 |
| 0.4074 | 12.7557 | 58000 | 0.2207 | 0.0 | 5.7262 |
| 0.398 | 12.8656 | 58500 | 0.2178 | 0.0 | 5.7282 |
| 0.4003 | 12.9756 | 59000 | 0.2162 | 0.0 | 5.7291 |
| 0.374 | 13.0856 | 59500 | 0.2145 | 0.0 | 5.7271 |
| 0.3749 | 13.1955 | 60000 | 0.2126 | 0.0 | 5.7287 |
| 0.3589 | 13.3055 | 60500 | 0.2109 | 0.0 | 5.7356 |
| 0.3734 | 13.4154 | 61000 | 0.2095 | 0.0 | 5.7329 |
| 0.3706 | 13.5254 | 61500 | 0.2087 | 0.0 | 5.7327 |
| 0.3781 | 13.6354 | 62000 | 0.2071 | 0.0 | 5.7296 |
| 0.3735 | 13.7453 | 62500 | 0.2060 | 0.0 | 5.7287 |
| 0.372 | 13.8553 | 63000 | 0.2039 | 0.0 | 5.718 |
| 0.3751 | 13.9653 | 63500 | 0.2024 | 0.0 | 5.728 |
| 0.3573 | 14.0752 | 64000 | 0.2014 | 0.0 | 5.7189 |
| 0.3322 | 14.1852 | 64500 | 0.2010 | 0.0 | 5.7204 |
| 0.3359 | 14.2951 | 65000 | 0.2003 | 0.0 | 5.7227 |
| 0.3533 | 14.4051 | 65500 | 0.1994 | 0.0 | 5.7222 |
| 0.3489 | 14.5151 | 66000 | 0.1986 | 0.0 | 5.7198 |
| 0.3358 | 14.6250 | 66500 | 0.1981 | 0.0 | 5.7231 |
| 0.3424 | 14.7350 | 67000 | 0.1977 | 0.0 | 5.72 |
| 0.3341 | 14.8450 | 67500 | 0.1976 | 0.0 | 5.7209 |
| 0.3513 | 14.9549 | 68000 | 0.1973 | 0.0 | 5.7184 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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