ScandiNER - Named Entity Recognition model for Scandinavian Languages
This model is a fine-tuned version of NbAiLab/nb-bert-base for Named Entity Recognition for Danish, Norwegian (both Bokmål and Nynorsk), Swedish, Icelandic and Faroese. It has been fine-tuned on the concatenation of DaNE, NorNE, SUC 3.0 and the Icelandic and Faroese parts of the WikiANN dataset. It also works reasonably well on English sentences, given the fact that the pretrained model is also trained on English data along with Scandinavian languages.
The model will predict the following four entities:
Tag | Name | Description |
---|---|---|
PER |
Person | The name of a person (e.g., Birgitte and Mohammed) |
LOC |
Location | The name of a location (e.g., Tyskland and Djurgården) |
ORG |
Organisation | The name of an organisation (e.g., Bunnpris and Landsbankinn) |
MISC |
Miscellaneous | A named entity of a different kind (e.g., Ūjķnustu pund and Mona Lisa) |
Quick start
You can use this model in your scripts as follows:
>>> from transformers import pipeline
>>> import pandas as pd
>>> ner = pipeline(task='ner',
... model='saattrupdan/nbailab-base-ner-scandi',
... aggregation_strategy='first')
>>> result = ner('Borghild kjøper seg inn i Bunnpris')
>>> pd.DataFrame.from_records(result)
entity_group score word start end
0 PER 0.981257 Borghild 0 8
1 ORG 0.974099 Bunnpris 26 34
Performance
The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here:
Model ID | DaNE | NorNE-NB | NorNE-NN | SUC 3.0 | WikiANN-IS | WikiANN-FO | Average |
---|---|---|---|---|---|---|---|
saattrupdan/nbailab-base-ner-scandi | 87.44 ± 0.81 | 91.06 ± 0.26 | 90.42 ± 0.61 | 88.37 ± 0.17 | 88.61 ± 0.41 | 90.22 ± 0.46 | 89.08 ± 0.46 |
chcaa/da_dacy_large_trf | 83.61 ± 1.18 | 78.90 ± 0.49 | 72.62 ± 0.58 | 53.35 ± 0.17 | 50.57 ± 0.46 | 51.72 ± 0.52 | 63.00 ± 0.57 |
RecordedFuture/Swedish-NER | 64.09 ± 0.97 | 61.74 ± 0.50 | 56.67 ± 0.79 | 66.60 ± 0.27 | 34.54 ± 0.73 | 42.16 ± 0.83 | 53.32 ± 0.69 |
Maltehb/danish-bert-botxo-ner-dane | 69.25 ± 1.17 | 60.57 ± 0.27 | 35.60 ± 1.19 | 38.37 ± 0.26 | 21.00 ± 0.57 | 27.88 ± 0.48 | 40.92 ± 0.64 |
Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane | 70.41 ± 1.19 | 48.76 ± 0.70 | 27.58 ± 0.61 | 35.39 ± 0.38 | 26.22 ± 0.52 | 28.30 ± 0.29 | 39.70 ± 0.61 |
radbrt/nb_nocy_trf | 56.82 ± 1.63 | 68.20 ± 0.75 | 69.22 ± 1.04 | 31.63 ± 0.29 | 20.32 ± 0.45 | 12.91 ± 0.50 | 38.08 ± 0.75 |
Aside from its high accuracy, it's also substantially smaller and faster than the previous state-of-the-art:
Model ID | Samples/second | Model size |
---|---|---|
saattrupdan/nbailab-base-ner-scandi | 4.16 ± 0.18 | 676 MB |
chcaa/da_dacy_large_trf | 0.65 ± 0.01 | 2,090 MB |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90135.90000000001
- num_epochs: 1000
Training results
Training Loss | Epoch | Step | Validation Loss | Micro F1 | Micro F1 No Misc |
---|---|---|---|---|---|
0.6682 | 1.0 | 2816 | 0.0872 | 0.6916 | 0.7306 |
0.0684 | 2.0 | 5632 | 0.0464 | 0.8167 | 0.8538 |
0.0444 | 3.0 | 8448 | 0.0367 | 0.8485 | 0.8783 |
0.0349 | 4.0 | 11264 | 0.0316 | 0.8684 | 0.8920 |
0.0282 | 5.0 | 14080 | 0.0290 | 0.8820 | 0.9033 |
0.0231 | 6.0 | 16896 | 0.0283 | 0.8854 | 0.9060 |
0.0189 | 7.0 | 19712 | 0.0253 | 0.8964 | 0.9156 |
0.0155 | 8.0 | 22528 | 0.0260 | 0.9016 | 0.9201 |
0.0123 | 9.0 | 25344 | 0.0266 | 0.9059 | 0.9233 |
0.0098 | 10.0 | 28160 | 0.0280 | 0.9091 | 0.9279 |
0.008 | 11.0 | 30976 | 0.0309 | 0.9093 | 0.9287 |
0.0065 | 12.0 | 33792 | 0.0313 | 0.9103 | 0.9284 |
0.0053 | 13.0 | 36608 | 0.0322 | 0.9078 | 0.9257 |
0.0046 | 14.0 | 39424 | 0.0343 | 0.9075 | 0.9256 |
Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
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