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niksmer/ManiBERT

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Model Card

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.

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). 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.

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