---
license: mit
tags:
- generated_from_trainer
datasets:
- sucx3_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: histbert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: sucx3_ner
      type: sucx3_ner
      config: simple_cased
      split: validation
      args: simple_cased
    metrics:
    - name: Precision
      type: precision
      value: 0.8784308810627898
    - name: Recall
      type: recall
      value: 0.9261363636363636
    - name: F1
      type: f1
      value: 0.9016530520357625
    - name: Accuracy
      type: accuracy
      value: 0.992218705252845
---

<!-- 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. -->

# histbert-finetuned-ner

This model is a fine-tuned version of [Riksarkivet/bert-base-cased-swe-historical](https://huggingface.co./Riksarkivet/bert-base-cased-swe-historical) on the sucx3_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.8784
- Recall: 0.9261
- F1: 0.9017
- Accuracy: 0.9922

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0403        | 1.0   | 5391  | 0.0316          | 0.8496    | 0.8866 | 0.8677 | 0.9903   |
| 0.0199        | 2.0   | 10782 | 0.0308          | 0.8814    | 0.9034 | 0.8923 | 0.9915   |
| 0.0173        | 3.0   | 16173 | 0.0372          | 0.8698    | 0.9197 | 0.8940 | 0.9913   |
| 0.0066        | 4.0   | 21564 | 0.0397          | 0.8783    | 0.9239 | 0.9005 | 0.9921   |
| 0.0029        | 5.0   | 26955 | 0.0454          | 0.8855    | 0.9181 | 0.9015 | 0.9923   |
| 0.0035        | 6.0   | 32346 | 0.0454          | 0.8834    | 0.9211 | 0.9019 | 0.9922   |
| 0.0009        | 7.0   | 37737 | 0.0495          | 0.8784    | 0.9261 | 0.9017 | 0.9922   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3