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README.md
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language:
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- text: Det er super godt
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#
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# Model Details
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## Model Description
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The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
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- **Developed by:** DaNLP
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- **Shared by [Optional]:** Hugging Face
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- **Model type:** Text Classification
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- **Language(s) (NLP):** Danish (da)
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- **License:** cc-by-sa-4.0
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- **Related Models:** More information needed
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- **Parent Model:** BERT
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- **Resources for more information:**
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- [GitHub Repo](https://github.com/certainlyio/nordic_bert)
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- [Associated Documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone)
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# Uses
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## Direct Use
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This model can be used for text classification
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## Downstream Use [Optional]
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More information needed.
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## Out-of-Scope Use
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The model should not be used to intentionally create hostile or alienating environments for people.
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# Bias, Risks, and Limitations
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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## Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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# Training Details
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## Training Data
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The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
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## Training Procedure
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### Preprocessing
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It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO.
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## Testing Data, Factors & Metrics
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### Testing Data
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More information needed.
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### Factors
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### Metrics
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F1
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## Results
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More information needed.
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# Model Examination
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More information needed.
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# Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** More information needed.
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- **Hours used:** More information needed.
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- **Cloud Provider:** More information needed.
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- **Compute Region:** More information needed.
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- **Carbon Emitted:** More information needed.
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# Technical Specifications [optional]
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## Model Architecture and Objective
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More information needed.
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## Compute Infrastructure
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More information needed.
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### Hardware
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More information needed.
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### Software
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More information needed.
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# Citation
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**BibTeX:**
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More information needed.
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**APA:**
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More information needed.
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# Glossary [optional]
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More information needed.
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# More Information [optional]
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More information needed.
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# Model Card Authors [optional]
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DaNLP in collaboration with Ezi Ozoani and the Hugging Face team
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# Model Card Contact
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More information needed.
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# How to Get Started with the Model
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Use the code below to get started with the model.
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<details>
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<summary> Click to expand </summary>
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```python
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from transformers import BertTokenizer, BertForSequenceClassification
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model = BertForSequenceClassification.from_pretrained("
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tokenizer = BertTokenizer.from_pretrained("
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```
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---
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language:
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- da
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tags:
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- bert
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- pytorch
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- sentiment
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- polarity
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license: cc-by-sa-4.0
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datasets:
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- Twitter Sentiment
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- Europarl Sentiment
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metrics:
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- f1
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widget:
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- text: Det er super godt
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---
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# Danish BERT Tone for sentiment polarity detection
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The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts.
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It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO.
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See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details.
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Here is how to use the model:
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```python
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from transformers import BertTokenizer, BertForSequenceClassification
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model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
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tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
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```
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## Training data
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The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
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model.safetensors
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