transformers safetensors mbart text2text-generation generated_from_trainer base_model:facebook/mbart-large-50-many-to-many-mmt autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-large-50-many-to-many-mmt-finetuned-en-to-hi
  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. -->

# mbart-large-50-many-to-many-mmt-finetuned-en-to-hi

This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7540
- Bleu: 9.5451
- Gen Len: 6.3699

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 3.2113        | 0.0   | 500   | 2.8535          | 4.975   | 6.0562  |
| 2.8197        | 0.01  | 1000  | 2.6902          | 3.2399  | 6.2449  |
| 2.6595        | 0.01  | 1500  | 2.6256          | 1.2488  | 6.5596  |
| 2.679         | 0.01  | 2000  | 2.5394          | 4.8497  | 6.1644  |
| 2.5408        | 0.02  | 2500  | 2.4751          | 2.294   | 6.3051  |
| 2.4689        | 0.02  | 3000  | 2.4052          | 3.6526  | 6.3714  |
| 2.4299        | 0.02  | 3500  | 2.3642          | 3.4783  | 6.4584  |
| 2.4303        | 0.02  | 4000  | 2.3545          | 4.9837  | 6.343   |
| 2.3715        | 0.03  | 4500  | 2.3138          | 5.3665  | 6.1981  |
| 2.3354        | 0.03  | 5000  | 2.3002          | 5.8659  | 6.109   |
| 2.3734        | 0.03  | 5500  | 2.2877          | 3.8468  | 6.443   |
| 2.2968        | 0.04  | 6000  | 2.2385          | 3.1561  | 6.4381  |
| 2.272         | 0.04  | 6500  | 2.2269          | 5.7378  | 6.1155  |
| 2.2691        | 0.04  | 7000  | 2.2244          | 7.8318  | 6.2325  |
| 2.2333        | 0.05  | 7500  | 2.1973          | 5.4549  | 6.1463  |
| 2.2518        | 0.05  | 8000  | 2.1996          | 2.3346  | 6.4954  |
| 2.251         | 0.05  | 8500  | 2.1682          | 4.7228  | 6.4063  |
| 2.252         | 0.06  | 9000  | 2.2211          | 5.7043  | 6.3745  |
| 2.288         | 0.06  | 9500  | 2.2017          | 3.7285  | 6.4893  |
| 2.2443        | 0.06  | 10000 | 2.1300          | 7.0869  | 6.4448  |
| 2.1723        | 0.06  | 10500 | 2.1076          | 4.3789  | 6.3482  |
| 2.1371        | 0.07  | 11000 | 2.1024          | 4.2024  | 6.547   |
| 2.1206        | 0.07  | 11500 | 2.1045          | 7.2662  | 6.1786  |
| 2.1325        | 0.07  | 12000 | 2.0903          | 3.8746  | 6.344   |
| 2.1315        | 0.08  | 12500 | 2.0817          | 5.9713  | 6.4415  |
| 2.1285        | 0.08  | 13000 | 2.0637          | 7.7832  | 6.1534  |
| 2.1223        | 0.08  | 13500 | 2.0522          | 2.9569  | 6.313   |
| 2.1036        | 0.09  | 14000 | 2.0505          | 5.0732  | 6.3308  |
| 2.1053        | 0.09  | 14500 | 2.0288          | 6.2772  | 6.1638  |
| 2.1122        | 0.09  | 15000 | 2.0232          | 6.8311  | 6.2005  |
| 2.0566        | 0.1   | 15500 | 2.0175          | 5.8541  | 6.1907  |
| 2.0783        | 0.1   | 16000 | 2.0147          | 10.0926 | 6.1531  |
| 2.0775        | 0.1   | 16500 | 2.0128          | 7.6705  | 6.2624  |
| 2.0952        | 0.1   | 17000 | 1.9951          | 5.5904  | 6.2104  |
| 2.115         | 0.11  | 17500 | 1.9806          | 8.0092  | 6.2081  |
| 2.0515        | 0.11  | 18000 | 1.9769          | 5.9444  | 6.2055  |
| 2.0698        | 0.11  | 18500 | 1.9611          | 8.6585  | 6.2591  |
| 2.0521        | 0.12  | 19000 | 1.9715          | 9.1678  | 6.2758  |
| 2.0581        | 0.12  | 19500 | 1.9538          | 7.0038  | 6.2019  |
| 2.0073        | 0.12  | 20000 | 1.9502          | 7.6102  | 6.3093  |
| 2.0104        | 0.13  | 20500 | 1.9414          | 7.7584  | 6.1554  |
| 2.0163        | 0.13  | 21000 | 1.9404          | 5.8758  | 6.3561  |
| 2.03          | 0.13  | 21500 | 1.9294          | 6.9283  | 6.1343  |
| 1.9915        | 0.14  | 22000 | 1.9159          | 5.6757  | 6.2349  |
| 2.0158        | 0.14  | 22500 | 1.9234          | 7.5094  | 6.1197  |
| 1.9616        | 0.14  | 23000 | 1.9170          | 9.4006  | 6.2105  |
| 1.9954        | 0.14  | 23500 | 1.9008          | 2.9622  | 6.2355  |
| 2.0116        | 0.15  | 24000 | 1.9026          | 11.0333 | 6.0291  |
| 1.9742        | 0.15  | 24500 | 1.8973          | 4.504   | 6.3386  |
| 1.9805        | 0.15  | 25000 | 1.8955          | 3.8655  | 6.2335  |
| 1.9413        | 0.16  | 25500 | 1.8821          | 8.9818  | 6.1769  |
| 1.9311        | 0.16  | 26000 | 1.8851          | 6.7291  | 6.0846  |
| 1.9696        | 0.16  | 26500 | 1.8789          | 12.1041 | 6.1274  |
| 1.9419        | 0.17  | 27000 | 1.8687          | 7.2389  | 6.2407  |
| 1.959         | 0.17  | 27500 | 1.8688          | 7.984   | 6.3319  |
| 1.9449        | 0.17  | 28000 | 1.8655          | 6.7646  | 6.2376  |
| 1.961         | 0.18  | 28500 | 1.8541          | 9.8683  | 6.2369  |
| 1.9293        | 0.18  | 29000 | 1.8676          | 8.2689  | 6.0779  |
| 1.978         | 0.18  | 29500 | 1.8515          | 5.7599  | 6.1964  |
| 1.9121        | 0.18  | 30000 | 1.8508          | 7.8691  | 6.19    |
| 1.9566        | 0.19  | 30500 | 1.8350          | 7.7093  | 6.1696  |
| 1.9279        | 0.19  | 31000 | 1.8455          | 7.2261  | 6.2585  |
| 1.9717        | 0.19  | 31500 | 1.8374          | 8.7243  | 6.2427  |
| 1.9215        | 0.2   | 32000 | 1.8239          | 3.5888  | 6.242   |
| 1.937         | 0.2   | 32500 | 1.8352          | 3.352   | 6.2681  |
| 1.9103        | 0.2   | 33000 | 1.8260          | 7.5665  | 6.2443  |
| 1.9363        | 0.21  | 33500 | 1.8122          | 8.6132  | 6.1723  |
| 1.8938        | 0.21  | 34000 | 1.8175          | 8.49    | 6.3157  |
| 1.8869        | 0.21  | 34500 | 1.8156          | 7.8958  | 6.3069  |
| 1.9113        | 0.22  | 35000 | 1.8083          | 5.658   | 6.2682  |
| 1.9175        | 0.22  | 35500 | 1.8012          | 9.5439  | 6.3022  |
| 1.9283        | 0.22  | 36000 | 1.8032          | 9.1064  | 6.3756  |
| 1.9227        | 0.22  | 36500 | 1.7899          | 8.7293  | 6.2953  |
| 1.9129        | 0.23  | 37000 | 1.7822          | 4.2586  | 6.2276  |
| 1.8733        | 0.23  | 37500 | 1.7789          | 9.1095  | 6.2261  |
| 1.8986        | 0.23  | 38000 | 1.7831          | 4.853   | 6.2544  |
| 1.8655        | 0.24  | 38500 | 1.7762          | 7.4264  | 6.3151  |
| 1.8996        | 0.24  | 39000 | 1.7648          | 7.4422  | 6.3657  |
| 1.8771        | 0.24  | 39500 | 1.7690          | 12.3696 | 6.1305  |
| 1.8719        | 0.25  | 40000 | 1.7565          | 10.0389 | 6.2719  |
| 1.8993        | 0.25  | 40500 | 1.7481          | 14.4891 | 6.2271  |
| 1.8756        | 0.25  | 41000 | 1.7516          | 7.2393  | 6.2737  |
| 1.8195        | 0.26  | 41500 | 1.7534          | 9.0375  | 6.2557  |
| 1.8384        | 0.26  | 42000 | 1.7540          | 9.5451  | 6.3699  |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
