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# ner-swedish-wikiann

This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-roberta-wiki) trained for NER on the wikiann dataset.

eval F1-Score: **83,78** 

test F1-Score: **83,76** 

## Model Usage

```python

from transformers import AutoTokenizer, AutoModelForTokenClassification

from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")

model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

example = "Jag heter Per och jag jobbar på KTH"

nlp(example)

```
<!-- 
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 4.9086903597787154e-05

- train_batch_size: 32

- eval_batch_size: 16

- seed: 42

- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

- lr_scheduler_type: linear

- num_epochs: 5.0

- mixed_precision_training: Native AMP

### Training results

It achieves the following results on the evaluation set:

- Loss: 0.3156

- Precision: 0.8332
from transformers import AutoTokenizer, AutoModelForTokenClassification

from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")

model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

example = "Jag heter Per och jag jobbar på KTH"

nlp(example)

- F1: 0.8378

- Accuracy: 0.9193

It achieves the following results on the test set:

- Loss: 0.3023

- Precision: 0.8301

- Recall: 0.8452

- F1: 0.8376

- Accuracy: 0.92

### Framework versions

- Transformers 4.6.1

- Pytorch 1.8.1+cu101

- Datasets 1.6.2

- Tokenizers 0.10.2
 -->

{"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "ner-swedish-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann"}, "metrics": [{"type": "precision", "value": 0.8331921416757433, "name": "Precision"}, {"type": "recall", "value": 0.84243586083126, "name": "Recall"}, {"type": "f1", "value": 0.8377885044416501, "name": "F1"}, {"type": "accuracy", "value": 0.91930707459758, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification dataset:wikiann license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# ner-swedish-wikiann

This model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.

eval F1-Score: 83,78 

test F1-Score: 83,76 

## Model Usage




# ner-swedish-wikiann

This model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.

eval F1-Score: 83,78 

test F1-Score: 83,76 ## Model Usage
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # ner-swedish-wikiann

This model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.

eval F1-Score: 83,78 

test F1-Score: 83,76 ## Model Usage
passage: TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# ner-swedish-wikiann

This model is a fine-tuned version of nordic-roberta-wiki trained for NER on the wikiann dataset.

eval F1-Score: 83,78 

test F1-Score: 83,76## Model Usage