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---
tags:
- generated_from_trainer
language:
- sv
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_swedish-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: sv
split: train
args: sv
metrics:
- name: Precision
type: precision
value: 0.9340386115444618
- name: Recall
type: recall
value: 0.9418907624993855
- name: F1
type: f1
value: 0.9379482534942355
- name: Accuracy
type: accuracy
value: 0.979997105690534
widget:
- "Jag heter Peter Petersson och jag jobbar på Skatteverket."
inference:
parameters:
aggregation_strategy: "first"
---
<!-- 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_swedish-ner
This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1316
- Precision: 0.9340
- Recall: 0.9419
- F1: 0.9379
- Accuracy: 0.9800
## Model description
Finetuned the model from [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) for Swedish NER task.
## Intended uses & limitations
NER, token classification
## Training and evaluation data
wikiann-SV dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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