
# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
**KlimaBERT**, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0.97, and Recall of 0.97. The negative class, 0, is defined as "non-climate-related".

KlimaBERT is fine-tuned using the pre-trained DaBERT-uncased model, on a training set of 1.000 manually labelled data-points. The training set contains both political quotes and summaries of bills from the [Danish Parliament](https://www.ft.dk/).

The model is created to identify political quotes related to climate change, and performs best on official texts from the Danish Parliament.

### Fine-tuning
To fine-tune a model similar to KlimaBERT, follow the [fine-tuning notebooks](https://github.com/jonahank/Vote-Prediction-Model/tree/main/climate_classifier)

### References
BERT:
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding.
https://arxiv.org/abs/1810.04805

DaBERT:
Certainly (2021). Certainly has trained the most advanced danish bert model to date.
https://www.certainly.io/blog/danish-bert-model/.

### Acknowledgements
The resources are created through the work of my Master's thesis, so I would like to thank my supervisors [Leon Derczynski](https://www.derczynski.com/itu/) and [Vedran Sekara](https://vedransekara.github.io/) for the great support throughout the project! And a HUGE thanks to [Gustav Gyrst](https://github.com/Gyrst) for great sparring and co-development of the tools you find in this repo. 


### Contact
For any further help, questions, comments etc. feel free to contact the author Jonathan Kristensen on [LinedIn](https://www.linkedin.com/in/jonathan-kristensen-444a96104) or by creating a "discussion" on this model's page.

{"language": ["da"], "tags": ["climate change", "climate-classifier", "political quotes", "klimabert"]}
transformers pytorch bert text-classification climate change climate-classifier political quotes klimabert da arxiv:1810.04805 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #climate change #climate-classifier #political quotes #klimabert #da #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 


# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
KlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0.97, and Recall of 0.97. The negative class, 0, is defined as "non-climate-related".

KlimaBERT is fine-tuned using the pre-trained DaBERT-uncased model, on a training set of 1.000 manually labelled data-points. The training set contains both political quotes and summaries of bills from the Danish Parliament.

The model is created to identify political quotes related to climate change, and performs best on official texts from the Danish Parliament.

### Fine-tuning
To fine-tune a model similar to KlimaBERT, follow the fine-tuning notebooks

### References
BERT:
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding.
URL

DaBERT:
Certainly (2021). Certainly has trained the most advanced danish bert model to date.
URL

### Acknowledgements
The resources are created through the work of my Master's thesis, so I would like to thank my supervisors Leon Derczynski and Vedran Sekara for the great support throughout the project! And a HUGE thanks to Gustav Gyrst for great sparring and co-development of the tools you find in this repo. 


### Contact
For any further help, questions, comments etc. feel free to contact the author Jonathan Kristensen on LinedIn or by creating a "discussion" on this model's page.

# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
KlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0.97, and Recall of 0.97. The negative class, 0, is defined as "non-climate-related".

KlimaBERT is fine-tuned using the pre-trained DaBERT-uncased model, on a training set of 1.000 manually labelled data-points. The training set contains both political quotes and summaries of bills from the Danish Parliament.

The model is created to identify political quotes related to climate change, and performs best on official texts from the Danish Parliament. ### Fine-tuning
To fine-tune a model similar to KlimaBERT, follow the fine-tuning notebooks ### References
BERT:
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding.
URL

DaBERT:
Certainly (2021). Certainly has trained the most advanced danish bert model to date.
URL ### Acknowledgements
The resources are created through the work of my Master's thesis, so I would like to thank my supervisors Leon Derczynski and Vedran Sekara for the great support throughout the project! And a HUGE thanks to Gustav Gyrst for great sparring and co-development of the tools you find in this repo. ### Contact
For any further help, questions, comments etc. feel free to contact the author Jonathan Kristensen on LinedIn or by creating a "discussion" on this model's page.
TAGS
#transformers #pytorch #bert #text-classification #climate change #climate-classifier #political quotes #klimabert #da #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
 # Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
KlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0.97, and Recall of 0.97. The negative class, 0, is defined as "non-climate-related".

KlimaBERT is fine-tuned using the pre-trained DaBERT-uncased model, on a training set of 1.000 manually labelled data-points. The training set contains both political quotes and summaries of bills from the Danish Parliament.

The model is created to identify political quotes related to climate change, and performs best on official texts from the Danish Parliament. ### Fine-tuning
To fine-tune a model similar to KlimaBERT, follow the fine-tuning notebooks ### References
BERT:
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding.
URL

DaBERT:
Certainly (2021). Certainly has trained the most advanced danish bert model to date.
URL ### Acknowledgements
The resources are created through the work of my Master's thesis, so I would like to thank my supervisors Leon Derczynski and Vedran Sekara for the great support throughout the project! And a HUGE thanks to Gustav Gyrst for great sparring and co-development of the tools you find in this repo. ### Contact
For any further help, questions, comments etc. feel free to contact the author Jonathan Kristensen on LinedIn or by creating a "discussion" on this model's page.
passage: TAGS
#transformers #pytorch #bert #text-classification #climate change #climate-classifier #political quotes #klimabert #da #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
# Identifying and Analysing political quotes from the Danish Parliament related to climate change using NLP
KlimaBERT, a sequence-classifier fine-tuned to predict whether political quotes are climate-related. When predicting the positive class 1, "climate-related", the model achieves a F1-score of 0.97, Precision of 0.97, and Recall of 0.97. The negative class, 0, is defined as "non-climate-related".

KlimaBERT is fine-tuned using the pre-trained DaBERT-uncased model, on a training set of 1.000 manually labelled data-points. The training set contains both political quotes and summaries of bills from the Danish Parliament.

The model is created to identify political quotes related to climate change, and performs best on official texts from the Danish Parliament.### Fine-tuning
To fine-tune a model similar to KlimaBERT, follow the fine-tuning notebooks### References
BERT:
Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding.
URL

DaBERT:
Certainly (2021). Certainly has trained the most advanced danish bert model to date.
URL### Acknowledgements
The resources are created through the work of my Master's thesis, so I would like to thank my supervisors Leon Derczynski and Vedran Sekara for the great support throughout the project! And a HUGE thanks to Gustav Gyrst for great sparring and co-development of the tools you find in this repo.### Contact
For any further help, questions, comments etc. feel free to contact the author Jonathan Kristensen on LinedIn or by creating a "discussion" on this model's page.