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 | summary |
|---|---|---|
| sentiment-classification | swerec | mcc = 63.02, mcc_se = 2.16, macro_f1 = 62.2, macro_f1_se = 3.61 |
| sentiment-classification | angry-tweets | mcc = 47.21, mcc_se = 0.53, macro_f1 = 64.21, macro_f1_se = 0.53 |
| sentiment-classification | norec | mcc = 42.23, mcc_se = 8.69, macro_f1 = 57.24, macro_f1_se = 7.67 |
| named-entity-recognition | suc3 | micro_f1 = 50.03, micro_f1_se = 4.16, micro_f1_no_misc = 53.55, micro_f1_no_misc_se = 4.57 |
| named-entity-recognition | dane | micro_f1 = 76.44, micro_f1_se = 1.36, micro_f1_no_misc = 80.61, micro_f1_no_misc_se = 1.11 |
| named-entity-recognition | norne-nb | micro_f1 = 68.38, micro_f1_se = 1.72, micro_f1_no_misc = 73.08, micro_f1_no_misc_se = 1.66 |
| named-entity-recognition | norne-nn | micro_f1 = 60.45, micro_f1_se = 1.71, micro_f1_no_misc = 64.39, micro_f1_no_misc_se = 1.8 |
| linguistic-acceptability | scala-sv | mcc = 5.01, mcc_se = 5.41, macro_f1 = 49.46, macro_f1_se = 3.67 |
| linguistic-acceptability | scala-da | mcc = 54.74, mcc_se = 12.22, macro_f1 = 76.25, macro_f1_se = 6.09 |
| linguistic-acceptability | scala-nb | mcc = 19.18, mcc_se = 14.01, macro_f1 = 55.3, macro_f1_se = 8.85 |
| linguistic-acceptability | scala-nn | mcc = 5.72, mcc_se = 5.91, macro_f1 = 49.56, macro_f1_se = 3.73 |
| question-answering | scandiqa-da | em = 26.36, em_se = 1.17, f1 = 32.41, f1_se = 1.1 |
| question-answering | scandiqa-no | em = 26.14, em_se = 1.59, f1 = 32.02, f1_se = 1.59 |
| question-answering | scandiqa-sv | em = 26.38, em_se = 1.1, f1 = 32.33, f1_se = 1.05 |
| speed | speed | speed = 4.55, speed_se = 0.0 |