---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-greek-uncased-v1-finetuned-ner
  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-base-greek-uncased-v1-finetuned-ner

This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1052
- Precision: 0.8440
- Recall: 0.8566
- F1: 0.8503
- Accuracy: 0.9768

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.64  | 250  | 0.0913          | 0.7814    | 0.8208 | 0.8073 | 0.9728   |
| 0.1136        | 1.29  | 500  | 0.0823          | 0.7940    | 0.8448 | 0.8342 | 0.9755   |
| 0.1136        | 1.93  | 750  | 0.0812          | 0.8057    | 0.8212 | 0.8328 | 0.9751   |
| 0.0521        | 2.58  | 1000 | 0.0855          | 0.8244    | 0.8514 | 0.8292 | 0.9744   |
| 0.0521        | 3.22  | 1250 | 0.0926          | 0.8329    | 0.8441 | 0.8397 | 0.9760   |
| 0.0352        | 3.87  | 1500 | 0.0869          | 0.8256  	| 0.8633 | 0.8440 | 0.9774   |
| 0.0352        | 4.51  | 1750 | 0.1049          | 0.8290    | 0.8636 | 0.8459 | 0.9766   |
| 0.023         | 5.15  | 2000 | 0.1093          | 0.8440    | 0.8566 | 0.8503 | 0.9768   |
| 0.023         | 5.8   | 2250 | 0.1172          | 0.8301    | 0.8514 | 0.8406 | 0.9760   |
| 0.0158        | 6.44  | 2500 | 0.1273          | 0.8238    | 0.8688 | 0.8457 | 0.9766   |
| 0.0158        | 7.09  | 2750 | 0.1246          | 0.8350    | 0.8539 | 0.8443 | 0.9764   |
| 0.0126        | 7.73  | 3000 | 0.1262          | 0.8333    | 0.8608 | 0.8468 | 0.9764   |
| 0.0126        | 8.38  | 3250 | 0.1347          | 0.8319    | 0.8591 | 0.8453 | 0.9762   |
| 0.0089        | 9.02  | 3500 | 0.1325          | 0.8376    | 0.8504 | 0.8439 | 0.9766   |
| 0.0089        | 9.66  | 3750 | 0.1362          | 0.8371    | 0.8563 | 0.8466 | 0.9765   |


### Framework versions

- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1