readme: ---
  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"
  - text: "Hvordan håndterer jeg min irriterende nabo?"
  ---
  
  # Danish-Bert-GoÆmotion
  
  Danish Go-Emotions classifier. [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (uncased) finetuned on a translation of the [go_emotions](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 dependent on the translation model.
  
  ## Training
  - Translating the training data with MT: [Notebook](https://colab.research.google.com/github/RJuro/Da-HyggeBERT-finetuning/blob/main/HyggeBERT_translation_en_da.ipynb)
  - Fine-tuning danish-bert-botxo: coming soon...
  
  ## 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
  ![](wb_loss.png)
  
  ### 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/Da-HyggeBERT')
  tokenizer = AutoTokenizer.from_pretrained('RJuro/Da-HyggeBERT')
  
  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/Da-HyggeBERT')
  
  predictions, raw_outputs = model.predict(df['text'])
  ```