# Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/senda) package.

Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("pin/analytical")
model = AutoModelForSequenceClassification.from_pretrained("pin/analytical")

# create 'senda' sentiment analysis pipeline 
analytical_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)

text = "Jeg synes, det er en elendig film"
# in English: 'I think, it is a terrible movie'
analytical_pipeline(text)
```

## Performance 
The `senda` model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that [Alexandra Institute](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#twitter-sentiment) provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the [`senda`](https://github.com/ebanalyse/senda) package to do this.

#### Contact
Feel free to contact author Lars Kjeldgaard on [lars.kjeldgaard@eb.dk](mailto:lars.kjeldgaard@eb.dk).



{"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "analytical"], "widget": [{"text": "Jeg synes, det er en elendig film"}]}
transformers pytorch jax bert text-classification danish sentiment analytical da license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #text-classification #danish #sentiment #analytical #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

# Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.

Here is an example of how to load the model in PyTorch using the Transformers library:



## Performance 
The 'senda' model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that Alexandra Institute provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the 'senda' package to do this.

#### Contact
Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@URL.



# Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.

Here is an example of how to load the model in PyTorch using the Transformers library: ## Performance 
The 'senda' model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that Alexandra Institute provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the 'senda' package to do this. #### Contact
Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@URL.
TAGS
#transformers #pytorch #jax #bert #text-classification #danish #sentiment #analytical #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.

Here is an example of how to load the model in PyTorch using the Transformers library: ## Performance 
The 'senda' model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that Alexandra Institute provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the 'senda' package to do this. #### Contact
Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@URL.
passage: TAGS
#transformers #pytorch #jax #bert #text-classification #danish #sentiment #analytical #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# Danish BERT fine-tuned for Detecting 'Analytical'

This model detects if a Danish text is 'subjective' or 'objective'.

It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.

Here is an example of how to load the model in PyTorch using the Transformers library:## Performance 
The 'senda' model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that Alexandra Institute provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the 'senda' package to do this.#### Contact
Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@URL.