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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: PolicyBERTa-7d |
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results: [] |
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widget: |
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- text: "Russia must end the war." |
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- text: "Democratic institutions must be supported." |
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- text: "The state must fight political corruption." |
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- text: "Our energy economy must be nationalised." |
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- text: "We must increase social spending." |
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--- |
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# PolicyBERTa-7d |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8549 |
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- Accuracy: 0.7059 |
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- F1-micro: 0.7059 |
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- F1-macro: 0.6683 |
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- F1-weighted: 0.7033 |
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- Precision: 0.7059 |
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- Recall: 0.7059 |
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## Model description |
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This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups". |
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## Intended uses & limitations |
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The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance. |
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```python |
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from transformers import pipeline |
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import pandas as pd |
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classifier = pipeline( |
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task="text-classification", |
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model="niksmer/PolicyBERTa-7d") |
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# Load text data you want to classify |
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text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list() |
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# Inference |
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output = classifier(text) |
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# Print output |
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pd.DataFrame(output).head() |
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``` |
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## Training and evaluation data |
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PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020. |
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| Country | Count manifestos | Count sentences | Time span | |
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|----------------|------------------|-----------------|--------------------| |
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| Australia | 18 | 14,887 | 2010-2016 | |
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| Ireland | 23 | 24,966 | 2007-2016 | |
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| Canada | 14 | 12,344 | 2004-2008 & 2015 | |
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| New Zealand | 46 | 35,079 | 1993-2017 | |
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| South Africa | 29 | 13,334 | 1994-2019 | |
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| USA | 9 | 13,188 | 1992 & 2004-2020 | |
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| United Kingdom | 34 | 30,936 | 1997-2019 | |
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Canadian manifestos between 2004 and 2008 are used as test data. |
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The Manifesto Project mannually annotates individual sentences from political party manifestos in 7 main political domains: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups' - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each domain. |
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### Tain data |
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Train data was higly imbalanced. |
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| Label | Description | Count | |
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|------------|--------------|--------| |
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| 0 | external relations | 7,640 | |
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| 1 | freedom and democracy | 5,880 | |
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| 2 | political system | 11,234 | |
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| 3 | economy | 29,218 | |
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| 4 | welfare and quality of life | 37,200 | |
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| 5 | fabric of society | 13,594 | |
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| 6 | social groups | 11,177 | |
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Overall count: 115,943 |
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### Validation data |
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The validation was created by chance. |
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| Label | Description | Count | |
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|------------|--------------|--------| |
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| 0 | external relations | 1,345 | |
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| 1 | freedom and democracy | 1,043 | |
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| 2 | political system | 2,038 | |
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| 3 | economy | 5,140 | |
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| 4 | welfare and quality of life | 6,554 | |
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| 5 | fabric of society | 2,384 | |
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| 6 | social groups | 1,957 | |
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Overall count: 20,461 |
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## Test data |
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The test dataset contains ten canadian manifestos between 2004 and 2008. |
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| Label | Description | Count | |
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|------------|--------------|--------| |
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| 0 | external relations | 824 | |
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| 1 | freedom and democracy | 296 | |
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| 2 | political system | 1,041 | |
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| 3 | economy | 2,188 | |
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| 4 | welfare and quality of life | 2,654 | |
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| 5 | fabric of society | 940 | |
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| 6 | social groups | 387 | |
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Overall count: 8,330 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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``` |
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training_args = TrainingArguments( |
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warmup_steps=0, |
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weight_decay=0.1, |
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learning_rate=1e-05, |
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fp16 = True, |
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evaluation_strategy="epoch", |
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num_train_epochs=5, |
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per_device_train_batch_size=16, |
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overwrite_output_dir=True, |
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per_device_eval_batch_size=16, |
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save_strategy="no", |
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logging_dir='logs', |
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logging_strategy= 'steps', |
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logging_steps=10, |
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push_to_hub=True, |
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hub_strategy="end") |
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``` |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:| |
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| 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0.6785 | 0.6383 | 0.6772 | 0.6785 | 0.6785 | |
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| 0.8374 | 2.0 | 3624 | 0.8569 | 0.6957 | 0.6957 | 0.6529 | 0.6914 | 0.6957 | 0.6957 | |
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| 0.7053 | 3.0 | 5436 | 0.8582 | 0.7019 | 0.7019 | 0.6594 | 0.6967 | 0.7019 | 0.7019 | |
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| 0.7178 | 4.0 | 7248 | 0.8488 | 0.7030 | 0.7030 | 0.6662 | 0.7011 | 0.7030 | 0.7030 | |
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| 0.6688 | 5.0 | 9060 | 0.8549 | 0.7059 | 0.7059 | 0.6683 | 0.7033 | 0.7059 | 0.7059 | |
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### Validation evaluation |
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| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |
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|----------------|----------------|----------------|-------------------| |
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| PolicyBERTa-7d | 0.71 | 0.67 | 0.70 | |
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### Test evaluation |
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| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |
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|----------------|----------------|----------------|-------------------| |
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| PolicyBERTa-7d | 0.65 | 0.60 | 0.65 | |
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### Evaluation per category |
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| Label | Validation F1-Score | Test F1-Score | |
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|-----------------------------|---------------------|---------------| |
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| external relations | 0.76 | 0.70 | |
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| freedom and democracy | 0.61 | 0.55 | |
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| political system | 0.55 | 0.55 | |
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| economy | 0.74 | 0.67 | |
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| welfare and quality of life | 0.77 | 0.72 | |
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| fabric of society | 0.67 | 0.60 | |
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| social groups | 0.58 | 0.41 | |
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### Evaluation based on saliency theory |
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Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent. |
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Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology. |
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The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE). |
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In the following table, the predicted and original share of the individual policy domains are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original shares is 0.965. |
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| Party-ID | Year | Type | Share external relations | Share freedom and democracy | Share political system | Share economy | Share welfare and quality of life | Share fabric of society | Share social groups | |
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|--------------|-------------|---------------|--------------------------|-----------------------------|------------------------|----------------|-----------------------------------|-------------------------|---------------------| |
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| 62320 | 2004 | Predicted | 7.1% | 4.8% | 13.2% | 20.3% | 35.2% | 9.6% | 9.8% | |
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| | | Original | 10.2% | 2.5% | 13.7% | 23.8% | 31.7% | 11.6% | 6.4% | |
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| 62320 | 2006 | Predicted | 2.9% | 4.7% | 16.4% | 18.9% | 38.3% | 11.9% | 6.9% | |
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| | | Original | 5.6% | 5.0% | 15.8% | 20.7% | 38.7% | 9.3% | 4.9% | |
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| 62320 | 2008 | Predicted | 6.8% | 4.7% | 6.2% | 24.7% | 38.3% | 10.3% | 9.0% | |
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| | | Original | 5.6% | 3.7% | 8.2% | 33.1% | 29.5% | 11.7% | 4.3% | |
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| 62420 | 2004 | Predicted | 9.7% | 3.5% | 14.5% | 24.7% | 34.8% | 8.5% | 4.3% | |
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| | | Original | 12.6% | 1.3% | 18.8% | 23.0% | 33.2% | 9.0% | 2.0% | |
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| 62420 | 2006 | Predicted | 9.5% | 2.2% | 7.9% | 27.8% | 34.8% | 9.2% | 8.7% | |
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| | | Original | 10.6% | 2.5% | 9.6% | 29.7% | 33.1% | 8.3% | 6.2% | |
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| 62420 | 2008 | Predicted | 0.7% | 0.5% | 3.5% | 41.7% | 46.4% | 3.7% | 3.5% | |
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| | | Original | 2.0% | 0.2% | 4.4% | 33.3% | 45.9% | 7.7% | 6.4% | |
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| 62623 | 2004 | Predicted | 7.1% | 11.4% | 24.5% | 17.6% | 21.5% | 13.6% | 4.3% | |
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| | | Original | 8.4% | 6.7% | 28.8% | 17.4% | 18.7% | 15.5% | 4.5% | |
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| 62623 | 2006 | Predicted | 5.6% | 8.5% | 23.6% | 15.6% | 14.8% | 24.3% | 7.6% | |
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| | | Original | 5.0% | 8.9% | 22.2% | 17.4% | 17.2% | 25.7% | 3.6% | |
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| 62623 | 2008 | Predicted | 5.0% | 4.4% | 12.2% | 33.1% | 21.9% | 17.5% | 5.9% | |
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| | | Original | 5.6% | 2.2% | 11.6% | 37.8% | 17.8% | 20.9% | 4.1% | |
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| 62110 | 2008 | Predicted | 10.0% | 3.1% | 6.8% | 22.7% | 41.3% | 10.1% | 6.0% | |
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| | | Original | 13.4% | 3.3% | 7.7% | 26.9% | 35.6% | 8.9% | 4.3% | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.8.0 |
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- Tokenizers 0.10.3 |
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