Hugging Face's logo

saattrupdan
/
nbailab-base-ner-scandi

Token Classification
Transformers
PyTorch
Safetensors
7 languages
bert
Inference Endpoints
Model card Files Community
5
nbailab-base-ner-scandi / README.md
saattrupdan's picture
saattrupdan
Update README.md
996a623
preview code
|
raw history blame
No virus
3.2 kB
metadata
language:
  - da
  - 'no'
  - nb
  - nn
  - sv
  - fo
  - is
license: mit
tags:
  - generated_from_trainer
datasets:
  - dane
  - norne
  - wikiann
  - suc3.0
model-index:
  - name: nbailab-base-ner-scandi
    results:
      - task:
          name: Token Classification
          type: token-classification
widget:
  - >-
    Hans er en professor på IT Universitetet i København, og han er en rigtig
    københavner. Hans kat, altså Hans' kat, Lisa, er supersød. Han fik købt en
    Mona Lisa på tilbud i Netto og gav den til hans kat, og nu er Mona Lisa
    Lisa's kæreste eje. Hans er med hans bror Peter, og de besluttede, at
    Peterskirken skulle have fint besøg af Peter og hans ven Hans.

nbailab-base-ner-scandi-unbalanced

This model is a fine-tuned version of NbAiLab/nb-bert-base on the concatenation of DaNE, NorNE, SUC 3.0 and the Icelandic and Faroese parts of the WikiAnn dataset. It achieves the following results on DaNE:

  • Loss: 0.0666
  • Micro F1: 0.8693
  • Micro F1 No Misc: 0.8925

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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