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---
language: da
tags:
- danish
- bert
- sentiment
- text-classification
- Maltehb/danish-bert-botxo
- Helsinki-NLP/opus-mt-en-da
- go-emotion
- Certainly
license: cc-by-4.0
datasets:
- go_emotions
metrics:
- Accuracy
widget:
- text: "Det er så sødt af dig at tænke på andre på den måde ved du det?"
- text: "Jeg vil gerne have en playstation."
- text: "Jeg elsker dig"
---
# Danish-Bert-GoÆmotion
Danish Go-Emotion classifier. [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (uncased) finetuned on a translation of the [go_emotion](https://huggingface.co/datasets/go_emotions) dataset using [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opus-mt-de-en). Thus,performance is obviousely only as good as the translation model.
## Training Parameters:
```
Num examples = 189900
Num Epochs = 3
Train batch = 8
Eval batch = 8
Learning Rate = 3e-5
Warmup steps = 4273
Total optimization steps = 71125
```
## Loss
### Training loss
### Eval. loss
```
0.1178 (21100 examples)
```
## Using the model with `transformers`
Easiest use with `transformers` and `pipeline`:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model = AutoModelForSequenceClassification.from_pretrained('RJuro/danish-bert-go-aemotion')
tokenizer = AutoTokenizer.from_pretrained('RJuro/danish-bert-go-aemotion')
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
classifier('jeg elsker dig')
```
`[{'label': 'kærlighed', 'score': 0.9634820818901062}]`
## Using the model with `simpletransformers`
```python
from simpletransformers.classification import MultiLabelClassificationModel
model = MultiLabelClassificationModel('bert', 'RJuro/danish-bert-go-aemotion')
predictions, raw_outputs = model.predict(df['text'])
``` |