
# 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'])
```
{"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "text-classification", "Maltehb/danish-bert-botxo", "Helsinki-NLP/opus-mt-en-da", "go-emotion", "Certainly"], "datasets": ["go_emotions"], "metrics": ["Accuracy"], "widget": [{"text": "Det er s\u00e5 s\u00f8dt af dig at t\u00e6nke p\u00e5 andre p\u00e5 den m\u00e5de ved du det?"}, {"text": "Jeg vil gerne have en playstation."}, {"text": "Jeg elsker dig"}, {"text": "Hvordan h\u00e5ndterer jeg min irriterende nabo?"}]}
transformers pytorch bert text-classification danish sentiment Maltehb/danish-bert-botxo Helsinki-NLP/opus-mt-en-da go-emotion Certainly da dataset:go_emotions license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #danish #sentiment #Maltehb/danish-bert-botxo #Helsinki-NLP/opus-mt-en-da #go-emotion #Certainly #da #dataset-go_emotions #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# Danish-Bert-GoÆmotion

Danish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model.

## Training
- Translating the training data with MT: Notebook
- Fine-tuning danish-bert-botxo: coming soon...

## Training Parameters:



## Loss
### Training loss
![](wb_loss.png)

### Eval. loss



## Using the model with 'transformers'
Easiest use with 'transformers' and 'pipeline':



'[{'label': 'kærlighed', 'score': 0.9634820818901062}]'

## Using the model with 'simpletransformers'


# Danish-Bert-GoÆmotion

Danish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model. ## Training
- Translating the training data with MT: Notebook
- Fine-tuning danish-bert-botxo: coming soon... ## Training Parameters: ## Loss ### Training loss
![](wb_loss.png) ### Eval. loss ## Using the model with 'transformers'
Easiest use with 'transformers' and 'pipeline':



'[{'label': 'kærlighed', 'score': 0.9634820818901062}]' ## Using the model with 'simpletransformers'
TAGS
#transformers #pytorch #bert #text-classification #danish #sentiment #Maltehb/danish-bert-botxo #Helsinki-NLP/opus-mt-en-da #go-emotion #Certainly #da #dataset-go_emotions #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Danish-Bert-GoÆmotion

Danish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model. ## Training
- Translating the training data with MT: Notebook
- Fine-tuning danish-bert-botxo: coming soon... ## Training Parameters: ## Loss ### Training loss
![](wb_loss.png) ### Eval. loss ## Using the model with 'transformers'
Easiest use with 'transformers' and 'pipeline':



'[{'label': 'kærlighed', 'score': 0.9634820818901062}]' ## Using the model with 'simpletransformers'
passage: TAGS
#transformers #pytorch #bert #text-classification #danish #sentiment #Maltehb/danish-bert-botxo #Helsinki-NLP/opus-mt-en-da #go-emotion #Certainly #da #dataset-go_emotions #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# Danish-Bert-GoÆmotion

Danish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model.## Training
- Translating the training data with MT: Notebook
- Fine-tuning danish-bert-botxo: coming soon...## Training Parameters:## Loss### Training loss
![](wb_loss.png)### Eval. loss## Using the model with 'transformers'
Easiest use with 'transformers' and 'pipeline':



'[{'label': 'kærlighed', 'score': 0.9634820818901062}]'## Using the model with 'simpletransformers'