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--- |
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license: apache-2.0 |
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tags: |
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- token-classification |
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datasets: |
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- wikiann |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: ner-swedish-wikiann |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wikiann |
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type: wikiann |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8331921416757433 |
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- name: Recall |
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type: recall |
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value: 0.84243586083126 |
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- name: F1 |
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type: f1 |
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value: 0.8377885044416501 |
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- name: Accuracy |
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type: accuracy |
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value: 0.91930707459758 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ner-swedish-wikiann |
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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. |
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eval F1-Score: **83,78** |
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test F1-Score: **83,76** |
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## Model Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann") |
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model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Jag heter Per och jag jobbar på KTH" |
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nlp(example) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4.9086903597787154e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3156 |
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- Precision: 0.8332 |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann") |
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model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Jag heter Per och jag jobbar på KTH" |
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nlp(example) |
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- F1: 0.8378 |
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- Accuracy: 0.9193 |
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It achieves the following results on the test set: |
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- Loss: 0.3023 |
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- Precision: 0.8301 |
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- Recall: 0.8452 |
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- F1: 0.8376 |
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- Accuracy: 0.92 |
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### Framework versions |
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- Transformers 4.6.1 |
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- Pytorch 1.8.1+cu101 |
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- Datasets 1.6.2 |
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- Tokenizers 0.10.2 |
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--> |
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