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---
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
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