ner-swedish-wikiann

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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)

<!-- ## 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 -->