

# ManiBERT

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/).


## Model description

This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories:


## Intended uses & limitations

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.

```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
    task="text-classification",
    model="niksmer/ManiBERT")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```

## Train Data

ManiBERT 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. 


| Country        | Count manifestos | Count sentences | Time span        |
|----------------|------------------|-----------------|--------------------|
| Australia      | 18               | 14,887          | 2010-2016          |
| Ireland        | 23               | 24,966          | 2007-2016          |
| Canada         | 14               | 12,344          | 2004-2008 & 2015   |
| New Zealand    | 46               | 35,079          | 1993-2017          |
| South Africa   | 29               | 13,334          | 1994-2019          |
| USA            | 9                | 13,188          | 1992   & 2004-2020 |
| United Kingdom | 34               | 30,936          | 1997-2019          |

Canadian manifestos between 2004 and 2008 are used as test data.

The resulting Datasets are higly (!) imbalanced. See Evaluation.

## Evaluation


| Description                                                       | Label | Count Train Data | Count Validation Data | Count Test Data | Validation F1-Score | Test F1-Score |
|-------------------------------------------------------------------|-------|------------------|-----------------------|-----------------|---------------------|---------------|
| Foreign   Special Relationships: Positive                         | 0     | 545              | 96                    | 60              | 0.43                | 0.45          |
| Foreign   Special Relationships: Negative                         | 1     | 66               | 14                    | 22              | 0.22                | 0.09          |
| Anti-Imperialism                                                  | 2     | 93               | 16                    | 1               | 0.16                | 0.00          |
| Military:   Positive                                              | 3     | 1,969            | 356                   | 159             | 0.69                | 0.63          |
| Military:   Negative                                              | 4     | 489              | 89                    | 52              | 0.59                | 0.63          |
| Peace                                                             | 5     | 418              | 80                    | 49              | 0.57                | 0.64          |
| Internationalism:   Positive                                      | 6     | 2,401            | 417                   | 404             | 0.60                | 0.54          |
| European   Community/Union or Latin America Integration: Positive | 7     | 930              | 156                   | 20              | 0.58                | 0.32          |
| Internationalism:   Negative                                      | 8     | 209              | 40                    | 57              | 0.28                | 0.05          |
| European   Community/Union or Latin America Integration: Negative | 9     | 520              | 81                    | 0               | 0.39                | -             |
| Freedom   and Human Rights                                        | 10    | 2,196            | 389                   | 76              | 0.50                | 0.34          |
| Democracy                                                         | 11    | 3,045            | 534                   | 206             | 0.53                | 0.51          |
| Constitutionalism:   Positive                                     | 12    | 259              | 48                    | 12              | 0.34                | 0.22          |
| Constitutionalism:   Negative                                     | 13    | 380              | 72                    | 2               | 0.34                | 0.00          |
| Decentralisation:   Positive                                      | 14    | 2,791            | 481                   | 331             | 0.49                | 0.45          |
| Centralisation:   Positive                                        | 15    | 150              | 33                    | 71              | 0.11                | 0.00          |
| Governmental   and Administrative Efficiency                      | 16    | 3,905            | 711                   | 105             | 0.50                | 0.32          |
| Political   Corruption                                            | 17    | 900              | 186                   | 234             | 0.59                | 0.55          |
| Political   Authority                                             | 18    | 3,488            | 627                   | 300             | 0.51                | 0.39          |
| Free   Market Economy                                             | 19    | 1,768            | 309                   | 53              | 0.40                | 0.16          |
| Incentives:   Positive                                            | 20    | 3,100            | 544                   | 81              | 0.52                | 0.28          |
| Market   Regulation                                               | 21    | 3,562            | 616                   | 210             | 0.50                | 0.36          |
| Economic   Planning                                               | 22    | 533              | 93                    | 67              | 0.31                | 0.12          |
| Corporatism/   Mixed Economy                                      | 23    | 193              | 32                    | 23              | 0.28                | 0.33          |
| Protectionism:   Positive                                         | 24    | 633              | 103                   | 180             | 0.44                | 0.22          |
| Protectionism:   Negative                                         | 25    | 723              | 118                   | 149             | 0.52                | 0.40          |
| Economic   Goals                                                  | 26    | 817              | 139                   | 148             | 0.05                | 0.00          |
| Keynesian   Demand Management                                     | 27    | 160              | 25                    | 9               | 0.00                | 0.00          |
| Economic   Growth: Positive                                       | 28    | 3,142            | 607                   | 374             | 0.53                | 0.30          |
| Technology   and Infrastructure: Positive                         | 29    | 8,643            | 1,529                 | 339             | 0.71                | 0.56          |
| Controlled   Economy                                              | 30    | 567              | 96                    | 94              | 0.47                | 0.16          |
| Nationalisation                                                   | 31    | 832              | 157                   | 27              | 0.56                | 0.16          |
| Economic   Orthodoxy                                              | 32    | 1,721            | 287                   | 184             | 0.55                | 0.48          |
| Marxist   Analysis: Positive                                      | 33    | 148              | 33                    | 0               | 0.20                | -             |
| Anti-Growth   Economy and Sustainability                          | 34    | 2,676            | 452                   | 250             | 0.43                | 0.33          |
| Environmental   Protection                                        | 35    | 6,731            | 1,163                 | 934             | 0.70                | 0.67          |
| Culture:   Positive                                               | 36    | 2,082            | 358                   | 92              | 0.69                | 0.56          |
| Equality:   Positive                                              | 37    | 6,630            | 1,126                 | 361             | 0.57                | 0.43          |
| Welfare   State Expansion                                         | 38    | 13,486           | 2,405                 | 990             | 0.72                | 0.61          |
| Welfare   State Limitation                                        | 39    | 926              | 151                   | 2               | 0.45                | 0.00          |
| Education   Expansion                                             | 40    | 7,191            | 1,324                 | 274             | 0.78                | 0.63          |
| Education   Limitation                                            | 41    | 154              | 27                    | 1               | 0.17                | 0.00          |
| National   Way of Life: Positive                                  | 42    | 2,105            | 385                   | 395             | 0.48                | 0.34          |
| National   Way of Life: Negative                                  | 43    | 743              | 147                   | 2               | 0.27                | 0.00          |
| Traditional   Morality: Positive                                  | 44    | 1,375            | 234                   | 19              | 0.55                | 0.14          |
| Traditional   Morality: Negative                                  | 45    | 291              | 54                    | 38              | 0.30                | 0.23          |
| Law   and Order                                                   | 46    | 5,582            | 949                   | 381             | 0.72                | 0.71          |
| Civic   Mindedness: Positive                                      | 47    | 1,348            | 229                   | 27              | 0.45                | 0.28          |
| Multiculturalism:   Positive                                      | 48    | 2,006            | 355                   | 71              | 0.61                | 0.35          |
| Multiculturalism:   Negative                                      | 49    | 144              | 31                    | 7               | 0.33                | 0.00          |
| Labour   Groups: Positive                                         | 50    | 3,856            | 707                   | 57              | 0.64                | 0.14          |
| Labour   Groups: Negative                                         | 51    | 208              | 35                    | 0               | 0.44                | -             |
| Agriculture   and Farmers                                         | 52    | 2,996            | 490                   | 130             | 0.67                | 0.56          |
| Middle   Class and Professional Groups                            | 53    | 271              | 38                    | 12              | 0.38                | 0.40          |
| Underprivileged   Minority Groups                                 | 54    | 1,417            | 252                   | 82              | 0.34                | 0.33          |
| Non-economic   Demographic Groups                                 | 55    | 2,429            | 435                   | 106             | 0.42                | 0.24          |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
```
training_args = TrainingArguments(
    warmup_ratio=0.05,
    weight_decay=0.1, 
    learning_rate=5e-05,
    fp16 = True,
    evaluation_strategy="epoch",
    num_train_epochs=5,
    per_device_train_batch_size=16,
    overwrite_output_dir=True,
    per_device_eval_batch_size=16,
    save_strategy="no",
    logging_dir='logs',   
    logging_strategy= 'steps',     
    logging_steps=10,
    push_to_hub=True,
    hub_strategy="end")
```

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 1.7638        | 1.0   | 1812 | 1.6471          | 0.5531   | 0.5531   | 0.3354   | 0.5368      | 0.5531    | 0.5531 |
| 1.4501        | 2.0   | 3624 | 1.5167          | 0.5807   | 0.5807   | 0.3921   | 0.5655      | 0.5807    | 0.5807 |
| 1.0638        | 3.0   | 5436 | 1.5017          | 0.5893   | 0.5893   | 0.4240   | 0.5789      | 0.5893    | 0.5893 |
| 0.9263        | 4.0   | 7248 | 1.5173          | 0.5975   | 0.5975   | 0.4499   | 0.5901      | 0.5975    | 0.5975 |
| 0.7859        | 5.0   | 9060 | 1.5574          | 0.5978   | 0.5978   | 0.4564   | 0.5903      | 0.5978    | 0.5978 |

### Overall evaluation

| Type | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| Validation |  0.60           | 0.46           | 0.59             |
| Test |  0.48           | 0.30           | 0.47             |

### Evaluation based on saliency theory

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.
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.

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).

In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).

![image](english_manibert_manifesto.png)

### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3

{"license": "mit", "metrics": ["accuracy", "precision", "recall"], "widget": [{"text": "Russia must end the war."}, {"text": "Democratic institutions must be supported."}, {"text": "The state must fight political corruption."}, {"text": "Our energy economy must be nationalised."}, {"text": "We must increase social spending."}], "model-index": [{"name": "ManiBERT", "results": []}]}
transformers pytorch roberta text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

ManiBERT
========


This model is a fine-tuned version of roberta-base on data from the Manifesto Project.


Model description
-----------------


This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories:


Intended uses & limitations
---------------------------


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.


Train Data
----------


ManiBERT was trained on the English-speaking subset of the Manifesto Project Dataset (MPDS2021a). 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.



Canadian manifestos between 2004 and 2008 are used as test data.


The resulting Datasets are higly (!) imbalanced. See Evaluation.


Evaluation
----------



Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


### Training results



### Overall evaluation



### Evaluation based on saliency theory


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.
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.


The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check this.


In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use RoBERTa-RILE.


!image


### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0+cu102
* Datasets 1.8.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training: ### Training results ### Overall evaluation ### Evaluation based on saliency theory


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.
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.


The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check this.


In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use RoBERTa-RILE.


!image ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0+cu102
* Datasets 1.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training: ### Training results ### Overall evaluation ### Evaluation based on saliency theory


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.
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.


The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check this.


In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use RoBERTa-RILE.


!image ### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0+cu102
* Datasets 1.8.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:### Training results### Overall evaluation### Evaluation based on saliency theory


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.
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.


The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check this.


In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use RoBERTa-RILE.


!image### Framework versions


* Transformers 4.16.2
* Pytorch 1.9.0+cu102
* Datasets 1.8.0
* Tokenizers 0.10.3