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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

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)

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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 -->