tensorboard generated_from_trainer base_model:bert-base-multilingual-cased license:apache-2.0 region:us
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mt
  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. -->

# mt

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0345
- Accuracy: 0.7947

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.8769        | 0.39  | 500   | 2.3415          | 0.5941   |
| 2.3422        | 0.78  | 1000  | 2.0567          | 0.6324   |
| 2.1192        | 1.17  | 1500  | 1.8960          | 0.6535   |
| 1.9894        | 1.57  | 2000  | 1.7869          | 0.6695   |
| 1.8961        | 1.96  | 2500  | 1.7181          | 0.6796   |
| 1.8258        | 2.35  | 3000  | 1.6541          | 0.6893   |
| 1.7648        | 2.74  | 3500  | 1.5881          | 0.6996   |
| 1.7159        | 3.13  | 4000  | 1.5544          | 0.7065   |
| 1.6763        | 3.52  | 4500  | 1.5229          | 0.7101   |
| 1.6338        | 3.92  | 5000  | 1.4794          | 0.7166   |
| 1.6           | 4.31  | 5500  | 1.4452          | 0.7222   |
| 1.5832        | 4.7   | 6000  | 1.4302          | 0.7250   |
| 1.5532        | 5.09  | 6500  | 1.4013          | 0.7308   |
| 1.5247        | 5.48  | 7000  | 1.3956          | 0.7325   |
| 1.5103        | 5.87  | 7500  | 1.3598          | 0.7367   |
| 1.4866        | 6.26  | 8000  | 1.3331          | 0.7401   |
| 1.468         | 6.66  | 8500  | 1.3266          | 0.7428   |
| 1.4402        | 7.05  | 9000  | 1.3119          | 0.7457   |
| 1.4255        | 7.44  | 9500  | 1.2967          | 0.7481   |
| 1.4236        | 7.83  | 10000 | 1.2779          | 0.7516   |
| 1.41          | 8.22  | 10500 | 1.2598          | 0.7544   |
| 1.3994        | 8.61  | 11000 | 1.2677          | 0.7539   |
| 1.3809        | 9.01  | 11500 | 1.2334          | 0.7579   |
| 1.3689        | 9.4   | 12000 | 1.2468          | 0.7581   |
| 1.3637        | 9.79  | 12500 | 1.2349          | 0.7588   |
| 1.3587        | 10.18 | 13000 | 1.2157          | 0.7625   |
| 1.3397        | 10.57 | 13500 | 1.2055          | 0.7630   |
| 1.3347        | 10.96 | 14000 | 1.1968          | 0.7654   |
| 1.315         | 11.35 | 14500 | 1.1955          | 0.7652   |
| 1.3246        | 11.75 | 15000 | 1.1886          | 0.7674   |
| 1.3078        | 12.14 | 15500 | 1.1942          | 0.7660   |
| 1.2925        | 12.53 | 16000 | 1.1850          | 0.7678   |
| 1.3004        | 12.92 | 16500 | 1.1747          | 0.7692   |
| 1.2911        | 13.31 | 17000 | 1.1591          | 0.7719   |
| 1.2786        | 13.7  | 17500 | 1.1602          | 0.7734   |
| 1.2771        | 14.1  | 18000 | 1.1597          | 0.7717   |
| 1.2774        | 14.49 | 18500 | 1.1547          | 0.7724   |
| 1.2652        | 14.88 | 19000 | 1.1403          | 0.7751   |
| 1.262         | 15.27 | 19500 | 1.1397          | 0.7754   |
| 1.2595        | 15.66 | 20000 | 1.1325          | 0.7778   |
| 1.2544        | 16.05 | 20500 | 1.1385          | 0.7759   |
| 1.2424        | 16.44 | 21000 | 1.1291          | 0.7774   |
| 1.2361        | 16.84 | 21500 | 1.1338          | 0.7782   |
| 1.2325        | 17.23 | 22000 | 1.1081          | 0.7818   |
| 1.236         | 17.62 | 22500 | 1.1161          | 0.7789   |
| 1.2284        | 18.01 | 23000 | 1.1150          | 0.7809   |
| 1.2267        | 18.4  | 23500 | 1.1001          | 0.7831   |
| 1.2151        | 18.79 | 24000 | 1.1054          | 0.7829   |
| 1.2197        | 19.19 | 24500 | 1.1096          | 0.7814   |
| 1.2226        | 19.58 | 25000 | 1.1098          | 0.7815   |
| 1.2101        | 19.97 | 25500 | 1.0962          | 0.7840   |
| 1.2102        | 20.36 | 26000 | 1.0920          | 0.7847   |
| 1.2003        | 20.75 | 26500 | 1.0828          | 0.7863   |
| 1.1912        | 21.14 | 27000 | 1.0886          | 0.7854   |
| 1.1987        | 21.53 | 27500 | 1.0860          | 0.7860   |
| 1.2072        | 21.93 | 28000 | 1.0812          | 0.7859   |
| 1.1894        | 22.32 | 28500 | 1.0816          | 0.7858   |
| 1.2031        | 22.71 | 29000 | 1.0771          | 0.7874   |
| 1.1819        | 23.1  | 29500 | 1.0674          | 0.7881   |
| 1.185         | 23.49 | 30000 | 1.0761          | 0.7879   |
| 1.1873        | 23.88 | 30500 | 1.0697          | 0.7892   |
| 1.1793        | 24.28 | 31000 | 1.0706          | 0.7884   |
| 1.1793        | 24.67 | 31500 | 1.0622          | 0.7899   |
| 1.1748        | 25.06 | 32000 | 1.0630          | 0.7894   |
| 1.1701        | 25.45 | 32500 | 1.0643          | 0.7889   |
| 1.1678        | 25.84 | 33000 | 1.0567          | 0.7906   |
| 1.177         | 26.23 | 33500 | 1.0660          | 0.7886   |
| 1.1749        | 26.62 | 34000 | 1.0652          | 0.7911   |
| 1.1623        | 27.02 | 34500 | 1.0436          | 0.7924   |
| 1.1647        | 27.41 | 35000 | 1.0769          | 0.7873   |
| 1.1692        | 27.8  | 35500 | 1.0474          | 0.7918   |
| 1.1572        | 28.19 | 36000 | 1.0454          | 0.7922   |
| 1.1612        | 28.58 | 36500 | 1.0554          | 0.7916   |
| 1.1626        | 28.97 | 37000 | 1.0492          | 0.7918   |
| 1.1613        | 29.37 | 37500 | 1.0586          | 0.7909   |
| 1.146         | 29.76 | 38000 | 1.0470          | 0.7918   |
| 1.1558        | 30.15 | 38500 | 1.0530          | 0.7921   |
| 1.1553        | 30.54 | 39000 | 1.0474          | 0.7910   |
| 1.1543        | 30.93 | 39500 | 1.0446          | 0.7920   |
| 1.1523        | 31.32 | 40000 | 1.0521          | 0.7916   |
| 1.1529        | 31.71 | 40500 | 1.0489          | 0.7923   |
| 1.1528        | 32.11 | 41000 | 1.0407          | 0.7930   |
| 1.1532        | 32.5  | 41500 | 1.0386          | 0.7943   |
| 1.1415        | 32.89 | 42000 | 1.0489          | 0.7913   |
| 1.1509        | 33.28 | 42500 | 1.0355          | 0.7940   |
| 1.1484        | 33.67 | 43000 | 1.0375          | 0.7931   |
| 1.1434        | 34.06 | 43500 | 1.0431          | 0.7928   |
| 1.1464        | 34.46 | 44000 | 1.0348          | 0.7949   |
| 1.1394        | 34.85 | 44500 | 1.0514          | 0.7927   |
| 1.1418        | 35.24 | 45000 | 1.0429          | 0.7933   |
| 1.1453        | 35.63 | 45500 | 1.0423          | 0.7942   |
| 1.1411        | 36.02 | 46000 | 1.0358          | 0.7949   |
| 1.1434        | 36.41 | 46500 | 1.0308          | 0.7954   |
| 1.1392        | 36.81 | 47000 | 1.0326          | 0.7950   |
| 1.137         | 37.2  | 47500 | 1.0315          | 0.7948   |
| 1.14          | 37.59 | 48000 | 1.0406          | 0.7937   |
| 1.142         | 37.98 | 48500 | 1.0464          | 0.7933   |
| 1.1404        | 38.37 | 49000 | 1.0423          | 0.7933   |
| 1.1412        | 38.76 | 49500 | 1.0363          | 0.7950   |
| 1.143         | 39.15 | 50000 | 1.0355          | 0.7950   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
