---
license: cc-by-nc-4.0
base_model: NYTK/PULI-BERT-Large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: kmdb_ner_model
  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. -->

# kmdb_ner_model

This model is a fine-tuned version of [NYTK/PULI-BERT-Large](https://huggingface.co/NYTK/PULI-BERT-Large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0405
- Precision: 0.8023
- Recall: 0.8261
- F1: 0.8140
- Accuracy: 0.9842

## 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0613        | 0.11  | 1000  | 0.0575          | 0.7102    | 0.7359 | 0.7228 | 0.9776   |
| 0.0559        | 0.23  | 2000  | 0.0525          | 0.7108    | 0.7611 | 0.7351 | 0.9788   |
| 0.0569        | 0.34  | 3000  | 0.0512          | 0.7375    | 0.7856 | 0.7608 | 0.9798   |
| 0.0481        | 0.45  | 4000  | 0.0496          | 0.7402    | 0.7975 | 0.7678 | 0.9803   |
| 0.041         | 0.57  | 5000  | 0.0474          | 0.7567    | 0.7981 | 0.7769 | 0.9809   |
| 0.0434        | 0.68  | 6000  | 0.0463          | 0.7588    | 0.7853 | 0.7718 | 0.9813   |
| 0.0577        | 0.8   | 7000  | 0.0462          | 0.7592    | 0.7896 | 0.7741 | 0.9812   |
| 0.0422        | 0.91  | 8000  | 0.0428          | 0.7820    | 0.8057 | 0.7937 | 0.9828   |
| 0.0346        | 1.02  | 9000  | 0.0443          | 0.7758    | 0.8153 | 0.7951 | 0.9825   |
| 0.0301        | 1.14  | 10000 | 0.0431          | 0.7831    | 0.8124 | 0.7974 | 0.9830   |
| 0.0305        | 1.25  | 11000 | 0.0427          | 0.7955    | 0.8162 | 0.8057 | 0.9834   |
| 0.0347        | 1.36  | 12000 | 0.0420          | 0.7923    | 0.8171 | 0.8045 | 0.9834   |
| 0.0397        | 1.48  | 13000 | 0.0422          | 0.7923    | 0.8220 | 0.8069 | 0.9835   |
| 0.0267        | 1.59  | 14000 | 0.0411          | 0.7970    | 0.8209 | 0.8087 | 0.9838   |
| 0.03          | 1.71  | 15000 | 0.0415          | 0.7946    | 0.8230 | 0.8085 | 0.9838   |
| 0.0311        | 1.82  | 16000 | 0.0407          | 0.8023    | 0.8253 | 0.8136 | 0.9842   |
| 0.0283        | 1.93  | 17000 | 0.0405          | 0.8023    | 0.8261 | 0.8140 | 0.9842   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0