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---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-multi-base-uncased-finetuned-pos-ky
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. -->
# bert-multi-base-uncased-finetuned-pos-ky
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0007
- Precision: 0.8230
- Recall: 0.8280
- F1: 0.8255
- Accuracy: 0.8850
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 40 | 0.6248 | 0.6903 | 0.6708 | 0.6804 | 0.8350 |
| No log | 2.0 | 80 | 0.4938 | 0.7555 | 0.7555 | 0.7555 | 0.8626 |
| No log | 3.0 | 120 | 0.4931 | 0.7939 | 0.7948 | 0.7944 | 0.8764 |
| No log | 4.0 | 160 | 0.4948 | 0.7776 | 0.8034 | 0.7903 | 0.8735 |
| No log | 5.0 | 200 | 0.4744 | 0.8102 | 0.8231 | 0.8166 | 0.8850 |
| No log | 6.0 | 240 | 0.5698 | 0.8042 | 0.8071 | 0.8056 | 0.8787 |
| No log | 7.0 | 280 | 0.5787 | 0.7878 | 0.8120 | 0.7998 | 0.8758 |
| No log | 8.0 | 320 | 0.6357 | 0.7841 | 0.8120 | 0.7978 | 0.8718 |
| No log | 9.0 | 360 | 0.6359 | 0.8265 | 0.8366 | 0.8315 | 0.8879 |
| No log | 10.0 | 400 | 0.6735 | 0.8048 | 0.8305 | 0.8174 | 0.8827 |
| No log | 11.0 | 440 | 0.7243 | 0.8087 | 0.8206 | 0.8146 | 0.8804 |
| No log | 12.0 | 480 | 0.7430 | 0.8133 | 0.8292 | 0.8212 | 0.8827 |
| 0.244 | 13.0 | 520 | 0.7097 | 0.8058 | 0.8206 | 0.8131 | 0.8810 |
| 0.244 | 14.0 | 560 | 0.7885 | 0.8152 | 0.8182 | 0.8167 | 0.8787 |
| 0.244 | 15.0 | 600 | 0.7925 | 0.8082 | 0.8231 | 0.8156 | 0.8827 |
| 0.244 | 16.0 | 640 | 0.7850 | 0.8270 | 0.8280 | 0.8275 | 0.8879 |
| 0.244 | 17.0 | 680 | 0.7881 | 0.8162 | 0.8292 | 0.8227 | 0.8850 |
| 0.244 | 18.0 | 720 | 0.8490 | 0.8168 | 0.8219 | 0.8194 | 0.8810 |
| 0.244 | 19.0 | 760 | 0.8470 | 0.8163 | 0.8243 | 0.8203 | 0.8815 |
| 0.244 | 20.0 | 800 | 0.8792 | 0.8007 | 0.8194 | 0.8100 | 0.8752 |
| 0.244 | 21.0 | 840 | 0.9056 | 0.8084 | 0.8243 | 0.8163 | 0.8769 |
| 0.244 | 22.0 | 880 | 0.9099 | 0.8152 | 0.8292 | 0.8222 | 0.8827 |
| 0.244 | 23.0 | 920 | 0.8455 | 0.8166 | 0.8317 | 0.8241 | 0.8844 |
| 0.244 | 24.0 | 960 | 0.9336 | 0.8140 | 0.8170 | 0.8155 | 0.8775 |
| 0.0193 | 25.0 | 1000 | 0.9462 | 0.8145 | 0.8145 | 0.8145 | 0.8787 |
| 0.0193 | 26.0 | 1040 | 0.9457 | 0.8200 | 0.8170 | 0.8185 | 0.8792 |
| 0.0193 | 27.0 | 1080 | 0.9312 | 0.8177 | 0.8268 | 0.8222 | 0.8798 |
| 0.0193 | 28.0 | 1120 | 0.9553 | 0.8235 | 0.8194 | 0.8214 | 0.8833 |
| 0.0193 | 29.0 | 1160 | 0.9450 | 0.8207 | 0.8268 | 0.8237 | 0.8821 |
| 0.0193 | 30.0 | 1200 | 0.9337 | 0.8335 | 0.8366 | 0.8351 | 0.8896 |
| 0.0193 | 31.0 | 1240 | 0.9476 | 0.8203 | 0.8354 | 0.8278 | 0.8861 |
| 0.0193 | 32.0 | 1280 | 0.9443 | 0.8182 | 0.8292 | 0.8237 | 0.8838 |
| 0.0193 | 33.0 | 1320 | 0.9713 | 0.8197 | 0.8268 | 0.8232 | 0.8844 |
| 0.0193 | 34.0 | 1360 | 0.9751 | 0.8210 | 0.8280 | 0.8245 | 0.8821 |
| 0.0193 | 35.0 | 1400 | 0.9850 | 0.8129 | 0.8219 | 0.8173 | 0.8815 |
| 0.0193 | 36.0 | 1440 | 0.9546 | 0.8182 | 0.8292 | 0.8237 | 0.8821 |
| 0.0193 | 37.0 | 1480 | 0.9713 | 0.8216 | 0.8317 | 0.8266 | 0.8844 |
| 0.0049 | 38.0 | 1520 | 0.9696 | 0.8234 | 0.8305 | 0.8269 | 0.8850 |
| 0.0049 | 39.0 | 1560 | 0.9722 | 0.8222 | 0.8354 | 0.8288 | 0.8861 |
| 0.0049 | 40.0 | 1600 | 0.9705 | 0.8273 | 0.8354 | 0.8313 | 0.8879 |
| 0.0049 | 41.0 | 1640 | 0.9777 | 0.8190 | 0.8280 | 0.8235 | 0.8838 |
| 0.0049 | 42.0 | 1680 | 0.9841 | 0.8167 | 0.8268 | 0.8217 | 0.8850 |
| 0.0049 | 43.0 | 1720 | 0.9799 | 0.8234 | 0.8305 | 0.8269 | 0.8873 |
| 0.0049 | 44.0 | 1760 | 0.9785 | 0.8248 | 0.8329 | 0.8289 | 0.8873 |
| 0.0049 | 45.0 | 1800 | 0.9863 | 0.8205 | 0.8256 | 0.8230 | 0.8856 |
| 0.0049 | 46.0 | 1840 | 0.9860 | 0.8278 | 0.8329 | 0.8304 | 0.8879 |
| 0.0049 | 47.0 | 1880 | 0.9870 | 0.8278 | 0.8329 | 0.8304 | 0.8879 |
| 0.0049 | 48.0 | 1920 | 0.9896 | 0.8278 | 0.8329 | 0.8304 | 0.8879 |
| 0.0049 | 49.0 | 1960 | 0.9997 | 0.8230 | 0.8280 | 0.8255 | 0.8850 |
| 0.0022 | 50.0 | 2000 | 1.0007 | 0.8230 | 0.8280 | 0.8255 | 0.8850 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.2.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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