metadata
license: mit
datasets:
- oscar
- DDSC/dagw_reddit_filtered_v1.0.0
- graelo/wikipedia
language:
- da
widget:
- text: Der var engang en [MASK]
What is this?
A pre-trained BERT model (base version, ~110 M parameters) for Danish NLP. The model was not pre-trained from scratch but adapted from the English version with a tokenizer trained on Danish text.
How to use
Test the model using the pipeline from the 🤗 Transformers library:
from transformers import pipeline
pipe = pipeline("fill-mask", model="KennethTM/bert-base-uncased-danish")
pipe("Der var engang en [MASK]")
Or load it using the Auto* classes:
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("KennethTM/bert-base-uncased-danish")
model = AutoModelForMaskedLM.from_pretrained("KennethTM/bert-base-uncased-danish")
Model training
The model is trained using multiple Danish datasets and a context length of 512 tokens.
The model weights are initialized from the English bert-base-uncased model with new word token embeddings created for Danish using WECHSEL.
Initially, only the word token embeddings are trained using 1.000.000 samples. Finally, the whole model is trained for 8 epochs.
Evaluation
The performance of the pretrained model was evaluated using ScandEval.
| Task | Dataset | Score (±SE) |
|---|---|---|
| sentiment-classification | swerec | mcc = 63.02 (±2.16) |
| macro_f1 = 62.2 (±3.61) | ||
| sentiment-classification | angry-tweets | mcc = 47.21 (±0.53) |
| macro_f1 = 64.21 (±0.53) | ||
| sentiment-classification | norec | mcc = 42.23 (±8.69) |
| macro_f1 = 57.24 (±7.67) | ||
| named-entity-recognition | suc3 | micro_f1 = 50.03 (±4.16) |
| micro_f1_no_misc = 53.55 (±4.57) | ||
| named-entity-recognition | dane | micro_f1 = 76.44 (±1.36) |
| micro_f1_no_misc = 80.61 (±1.11) | ||
| named-entity-recognition | norne-nb | micro_f1 = 68.38 (±1.72) |
| micro_f1_no_misc = 73.08 (±1.66) | ||
| named-entity-recognition | norne-nn | micro_f1 = 60.45 (±1.71) |
| micro_f1_no_misc = 64.39 (±1.8) | ||
| linguistic-acceptability | scala-sv | mcc = 5.01 (±5.41) |
| macro_f1 = 49.46 (±3.67) | ||
| linguistic-acceptability | scala-da | mcc = 54.74 (±12.22) |
| macro_f1 = 76.25 (±6.09) | ||
| linguistic-acceptability | scala-nb | mcc = 19.18 (±14.01) |
| macro_f1 = 55.3 (±8.85) | ||
| linguistic-acceptability | scala-nn | mcc = 5.72 (±5.91) |
| macro_f1 = 49.56 (±3.73) | ||
| question-answering | scandiqa-da | em = 26.36 (±1.17) |
| f1 = 32.41 (±1.1) | ||
| question-answering | scandiqa-no | em = 26.14 (±1.59) |
| f1 = 32.02 (±1.59) | ||
| question-answering | scandiqa-sv | em = 26.38 (±1.1) |
| f1 = 32.33 (±1.05) | ||
| speed | speed | speed = 4.55 (±0.0) |