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---
license: mit
tags:
- generated_from_trainer
datasets:
- ju-bezdek/conll2003-SK-NER
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: outputs
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ju-bezdek/conll2003-SK-NER
type: ju-bezdek/conll2003-SK-NER
args: conll2003-SK-NER
metrics:
- name: Precision
type: precision
value: 0.8189727994593682
- name: Recall
type: recall
value: 0.8389581169955002
- name: F1
type: f1
value: 0.8288450029922203
- name: Accuracy
type: accuracy
value: 0.9526157920337243
---
<!-- 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. -->
# outputs
This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the ju-bezdek/conll2003-SK-NER dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- Precision: 0.8190
- Recall: 0.8390
- F1: 0.8288
- Accuracy: 0.9526
## 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: 0.0001
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 |
| 0.1663 | 2.0 | 1756 | 0.2370 | 0.7775 | 0.8090 | 0.7929 | 0.9394 |
| 0.1251 | 3.0 | 2634 | 0.2289 | 0.7732 | 0.8029 | 0.7878 | 0.9385 |
| 0.0984 | 4.0 | 3512 | 0.2818 | 0.7294 | 0.8189 | 0.7715 | 0.9294 |
| 0.0808 | 5.0 | 4390 | 0.3138 | 0.7615 | 0.7900 | 0.7755 | 0.9326 |
| 0.0578 | 6.0 | 5268 | 0.3072 | 0.7548 | 0.8222 | 0.7871 | 0.9370 |
| 0.0481 | 7.0 | 6146 | 0.2778 | 0.7897 | 0.8156 | 0.8025 | 0.9408 |
| 0.0414 | 8.0 | 7024 | 0.3336 | 0.7695 | 0.8201 | 0.7940 | 0.9389 |
| 0.0268 | 9.0 | 7902 | 0.3294 | 0.7868 | 0.8140 | 0.8002 | 0.9409 |
| 0.0204 | 10.0 | 8780 | 0.3693 | 0.7657 | 0.8239 | 0.7938 | 0.9376 |
| 0.016 | 11.0 | 9658 | 0.3816 | 0.7932 | 0.8242 | 0.8084 | 0.9425 |
| 0.0108 | 12.0 | 10536 | 0.3607 | 0.7929 | 0.8256 | 0.8089 | 0.9431 |
| 0.0078 | 13.0 | 11414 | 0.3980 | 0.7915 | 0.8240 | 0.8074 | 0.9423 |
| 0.0062 | 14.0 | 12292 | 0.4096 | 0.7995 | 0.8247 | 0.8119 | 0.9436 |
| 0.0035 | 15.0 | 13170 | 0.4177 | 0.8006 | 0.8251 | 0.8127 | 0.9438 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
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