deberta-v3-large-zeroshot-v1.1-all-33

Last updated 5/30/2024

🚀

Property	Value
Run this model	Run on HuggingFace
API spec	View on HuggingFace
Github link	No Github link provided
Paper link	No paper link provided

Create account to get full access

Model description: deberta-v3-large-zeroshot-v1.1-all-33

The model is designed for zero-shot classification with the Hugging Face pipeline.

The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. not_entailment).
This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into this task.

A detailed description of how the model was trained and how it can be used is available in this paper.

Training data

The model was trained on a mixture of 33 datasets and 387 classes that have been reformatted into this universal format.

Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
28 classification tasks reformatted into the universal NLI format. ~51k cleaned texts were used to avoid overfitting: 'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes', 'emotiondair', 'emocontext', 'empathetic', 'financialphrasebank', 'banking77', 'massive', 'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', 'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent', 'agnews', 'yahootopics', 'trueteacher', 'spam', 'wellformedquery', 'manifesto', 'capsotu'.

See details on each dataset here: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv

Note that compared to other NLI models, this model predicts two classes (entailment vs. not_entailment) as opposed to three classes (entailment/neutral/contradiction)

The model was only trained on English data. For multilingual use-cases, I recommend machine translating texts to English with libraries like EasyNMT. English-only models tend to perform better than multilingual models and validation with English data can be easier if you don't speak all languages in your corpus.

How to use the model

Simple zero-shot classification pipeline

#!pip install transformers[sentencepiece]
from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "This example is about {}"
classes_verbalized = ["politics", "economy", "entertainment", "environment"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
print(output)

Details on data and training

The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main

Hyperparameters and other details are available in this Weights & Biases repo: https://wandb.ai/moritzlaurer/deberta-v3-large-zeroshot-v1-1-all-33/table?workspace=user-

Metrics

Balanced accuracy is reported for all datasets. deberta-v3-large-zeroshot-v1.1-all-33 was trained on all datasets, with only maximum 500 texts per class to avoid overfitting. The metrics on these datasets are therefore not strictly zeroshot, as the model has seen some data for each task during training. deberta-v3-large-zeroshot-v1.1-heldout indicates zeroshot performance on the respective dataset. To calculate these zeroshot metrics, the pipeline was run 28 times, each time with one dataset held out from training to simulate a zeroshot setup.

[object Object]

deberta-v3-large-mnli-fever-anli-ling-wanli-binary

deberta-v3-large-zeroshot-v1.1-heldout

deberta-v3-large-zeroshot-v1.1-all-33

datasets mean (w/o nli)

64.1

73.4

85.2

amazonpolarity (2)

94.7

96.6

96.8

imdb (2)

90.3

95.2

95.5

appreviews (2)

93.6

94.3

94.7

yelpreviews (2)

98.5

98.4

98.9

rottentomatoes (2)

83.9

90.5

90.8

emotiondair (6)

49.2

42.1

72.1

emocontext (4)

69.3

82.4

empathetic (32)

34.4

financialphrasebank (3)

77.4

77.5

91.9

banking77 (72)

29.1

52.8

72.2

massive (59)

47.3

64.7

77.3

wikitoxic_toxicaggreg (2)

81.6

86.6

wikitoxic_obscene (2)

85.9

91.9

93.1

wikitoxic_threat (2)

77.9

93.7

97.6

wikitoxic_insult (2)

77.8

91.1

92.3

wikitoxic_identityhate (2)

86.4

89.8

95.7

hateoffensive (3)

62.8

66.5

88.4

hatexplain (3)

46.9

76.9

biasframes_offensive (2)

62.5

86.6

biasframes_sex (2)

87.6

89.6

92.6

biasframes_intent (2)

54.8

88.6

89.9

agnews (4)

81.9

82.8

90.9

yahootopics (10)

37.7

65.6

74.3

trueteacher (2)

51.2

54.9

86.6

spam (2)

52.6

51.8

97.1

wellformedquery (2)

49.9

40.4

82.7

manifesto (56)

10.6

29.4

44.1

capsotu (21)

23.2

69.4

mnli_m (2)

93.1

nan

93.1

mnli_mm (2)

93.2

nan

93.2

fevernli (2)

89.3

nan

89.5

anli_r1 (2)

87.9

nan

87.3

anli_r2 (2)

76.3

nan

anli_r3 (2)

73.6

nan

74.1

wanli (2)

82.8

nan

82.7

lingnli (2)

90.2

nan

89.6

Limitations and bias

The model can only do text classification tasks.

Please consult the original DeBERTa paper and the papers for the different datasets for potential biases.

License

The base model (DeBERTa-v3) is published under the MIT license. The datasets the model was fine-tuned on are published under a diverse set of licenses. The following table provides an overview of the non-NLI datasets used for fine-tuning, information on licenses, the underlying papers etc.: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv

Citation

If you use this model academically, please cite:

@misc{laurer_building_2023,
    title = {Building {Efficient} {Universal} {Classifiers} with {Natural} {Language} {Inference}},
    url = {http://arxiv.org/abs/2312.17543},
    doi = {10.48550/arXiv.2312.17543},
    abstract = {Generative Large Language Models (LLMs) have become the mainstream choice for fewshot and zeroshot learning thanks to the universality of text generation. Many users, however, do not need the broad capabilities of generative LLMs when they only want to automate a classification task. Smaller BERT-like models can also learn universal tasks, which allow them to do any text classification task without requiring fine-tuning (zeroshot classification) or to learn new tasks with only a few examples (fewshot), while being significantly more efficient than generative LLMs. This paper (1) explains how Natural Language Inference (NLI) can be used as a universal classification task that follows similar principles as instruction fine-tuning of generative LLMs, (2) provides a step-by-step guide with reusable Jupyter notebooks for building a universal classifier, and (3) shares the resulting universal classifier that is trained on 33 datasets with 389 diverse classes. Parts of the code we share has been used to train our older zeroshot classifiers that have been downloaded more than 55 million times via the Hugging Face Hub as of December 2023. Our new classifier improves zeroshot performance by 9.4\%.},
    urldate = {2024-01-05},
    publisher = {arXiv},
    author = {Laurer, Moritz and van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
    month = dec,
    year = {2023},
    note = {arXiv:2312.17543 [cs]},
    keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

Ideas for cooperation or questions?

If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn

Debugging and issues

Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers can have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.

Hypotheses used for classification

The hypotheses in the tables below were used to fine-tune the model. Inspecting them can help users get a feeling for which type of hypotheses and tasks the model was trained on. You can formulate your own hypotheses by changing the hypothesis_template of the zeroshot pipeline. For example:

from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "Merkel is the leader of the party: {}"
classes_verbalized = ["CDU", "SPD", "Greens"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False)
print(output)

Note that a few rows in the massive and banking77 datasets contain nan because some classes were so ambiguous/unclear that I excluded them from the data.

wellformedquery

label

hypothesis

not_well_formed

This example is not a well formed Google query

well_formed

This example is a well formed Google query.

biasframes_sex

label

hypothesis

not_sex

This example does not contain allusions to sexual content.

sex

This example contains allusions to sexual content.

biasframes_intent

label

hypothesis

intent

The intent of this example is to be offensive/disrespectful.

not_intent

The intent of this example is not to be offensive/disrespectful.

biasframes_offensive

label

hypothesis

not_offensive

This example could not be considered offensive, disrespectful, or toxic.

offensive

This example could be considered offensive, disrespectful, or toxic.

financialphrasebank

label

hypothesis

This example tweet contains offensive language without hate speech.

hatexplain

label

hypothesis

hate_speech

This example text from twitter or gab contains hate speech.

neither

This example text from twitter or gab contains neither offensive language nor hate speech.

offensive

This example text from twitter or gab contains offensive language without hate speech.

spam

label

hypothesis

not_spam

This example sms is not spam.

spam

This example sms is spam.

emotiondair

label

hypothesis

anger

This example tweet expresses the emotion: anger

fear

This example tweet expresses the emotion: fear

joy

This example tweet expresses the emotion: joy

love

This example tweet expresses the emotion: love

sadness

This example tweet expresses the emotion: sadness

surprise

This example tweet expresses the emotion: surprise

emocontext

label

hypothesis

angry

This example tweet expresses the emotion: anger

happy

This example tweet expresses the emotion: happiness

others

This example tweet does not express any of the emotions: anger, sadness, or happiness

sad

This example tweet expresses the emotion: sadness

empathetic

label

hypothesis

afraid

The main emotion of this example dialogue is: afraid

angry

The main emotion of this example dialogue is: angry

annoyed

The main emotion of this example dialogue is: annoyed

anticipating

The main emotion of this example dialogue is: anticipating

anxious

The main emotion of this example dialogue is: anxious

apprehensive

The main emotion of this example dialogue is: apprehensive

ashamed

The main emotion of this example dialogue is: ashamed

caring

The main emotion of this example dialogue is: caring

confident

The main emotion of this example dialogue is: confident

content

The main emotion of this example dialogue is: content

devastated

The main emotion of this example dialogue is: devastated

disappointed

The main emotion of this example dialogue is: disappointed

disgusted

The main emotion of this example dialogue is: disgusted

embarrassed

The main emotion of this example dialogue is: embarrassed

excited

The main emotion of this example dialogue is: excited

faithful

The main emotion of this example dialogue is: faithful

furious

The main emotion of this example dialogue is: furious

grateful

The main emotion of this example dialogue is: grateful

guilty

The main emotion of this example dialogue is: guilty

hopeful

The main emotion of this example dialogue is: hopeful

impressed

The main emotion of this example dialogue is: impressed

jealous

The main emotion of this example dialogue is: jealous

joyful

The main emotion of this example dialogue is: joyful

lonely

The main emotion of this example dialogue is: lonely

nostalgic

The main emotion of this example dialogue is: nostalgic

prepared

The main emotion of this example dialogue is: prepared

proud

The main emotion of this example dialogue is: proud

sad

The main emotion of this example dialogue is: sad

sentimental

The main emotion of this example dialogue is: sentimental

surprised

The main emotion of this example dialogue is: surprised

terrified

The main emotion of this example dialogue is: terrified

trusting

The main emotion of this example dialogue is: trusting

agnews

label

hypothesis

Business

This example news text is about business news

Sci/Tech

This example news text is about science and technology

Sports

This example news text is about sports

World

This example news text is about world news

yahootopics

label

hypothesis

Business & Finance

This example question from the Yahoo Q&A forum is categorized in the topic: Business & Finance

Computers & Internet

This example question from the Yahoo Q&A forum is categorized in the topic: Computers & Internet

Education & Reference

This example question from the Yahoo Q&A forum is categorized in the topic: Education & Reference

Entertainment & Music

This example question from the Yahoo Q&A forum is categorized in the topic: Entertainment & Music

Family & Relationships

This example question from the Yahoo Q&A forum is categorized in the topic: Family & Relationships

Health

This example question from the Yahoo Q&A forum is categorized in the topic: Health

Politics & Government

This example question from the Yahoo Q&A forum is categorized in the topic: Politics & Government

Science & Mathematics

This example question from the Yahoo Q&A forum is categorized in the topic: Science & Mathematics

Society & Culture

This example question from the Yahoo Q&A forum is categorized in the topic: Society & Culture

Sports

This example question from the Yahoo Q&A forum is categorized in the topic: Sports

massive

label

hypothesis

alarm_query

The example utterance is a query about alarms.

alarm_remove

The intent of this example utterance is to remove an alarm.

alarm_set

The intent of the example utterance is to set an alarm.

audio_volume_down

The intent of the example utterance is to lower the volume.

audio_volume_mute

The intent of this example utterance is to mute the volume.

audio_volume_other

The example utterance is related to audio volume.

audio_volume_up

The intent of this example utterance is turning the audio volume up.

calendar_query

The example utterance is a query about a calendar.

calendar_remove

The intent of the example utterance is to remove something from a calendar.

calendar_set

The intent of this example utterance is to set something in a calendar.

cooking_query

The example utterance is a query about cooking.

cooking_recipe

This example utterance is about cooking recipies.

datetime_convert

The example utterance is related to date time changes or conversion.

datetime_query

The intent of this example utterance is a datetime query.

email_addcontact

The intent of this example utterance is adding an email address to contacts.

email_query

The example utterance is a query about emails.

email_querycontact

The intent of this example utterance is to query contact details.

email_sendemail

The intent of the example utterance is to send an email.

general_greet

This example utterance is a general greet.

general_joke

The intent of the example utterance is to hear a joke.

general_quirky

nan

iot_cleaning

The intent of the example utterance is for an IoT device to start cleaning.

iot_coffee

The intent of this example utterance is for an IoT device to make coffee.

iot_hue_lightchange

The intent of this example utterance is changing the light.

iot_hue_lightdim

The intent of the example utterance is to dim the lights.

iot_hue_lightoff

The example utterance is related to turning the lights off.

iot_hue_lighton

The example utterance is related to turning the lights on.

iot_hue_lightup

The intent of this example utterance is to brighten lights.

iot_wemo_off

The intent of this example utterance is turning an IoT device off.

iot_wemo_on

The intent of the example utterance is to turn an IoT device on.

lists_createoradd

The example utterance is related to creating or adding to lists.

lists_query

The example utterance is a query about a list.

lists_remove

The intent of this example utterance is to remove a list or remove something from a list.

music_dislikeness

The intent of this example utterance is signalling music dislike.

music_likeness

The example utterance is related to liking music.

music_query

The example utterance is a query about music.

music_settings

The intent of the example utterance is to change music settings.

news_query

The example utterance is a query about the news.

play_audiobook

The example utterance is related to playing audiobooks.

play_game

The intent of this example utterance is to start playing a game.

play_music

The intent of this example utterance is for an IoT device to play music.

play_podcasts

The example utterance is related to playing podcasts.

play_radio

The intent of the example utterance is to play something on the radio.

qa_currency

This example utteranceis about currencies.

qa_definition

The example utterance is a query about a definition.

qa_factoid

The example utterance is a factoid question.

qa_maths

The example utterance is a question about maths.

qa_stock

This example utterance is about stocks.

recommendation_events

This example utterance is about event recommendations.

recommendation_locations

The intent of this example utterance is receiving recommendations for good locations.

recommendation_movies

This example utterance is about movie recommendations.

social_post

The example utterance is about social media posts.

social_query

The example utterance is a query about a social network.

takeaway_order

The intent of this example utterance is to order takeaway food.

takeaway_query

This example utterance is about takeaway food.

transport_query

The example utterance is a query about transport or travels.

transport_taxi

The intent of this example utterance is to get a taxi.

transport_ticket

This example utterance is about transport tickets.

transport_traffic

This example utterance is about transport or traffic.

weather_query

This example utterance is a query about the wheather.

banking77

label

hypothesis

Refund_not_showing_up

This customer example message is about a refund not showing up.

activate_my_card

This banking customer example message is about activating a card.

age_limit

This banking customer example message is related to age limits.

apple_pay_or_google_pay

This banking customer example message is about apple pay or google pay

atm_support

This banking customer example message requests ATM support.

automatic_top_up

This banking customer example message is about automatic top up.

balance_not_updated_after_bank_transfer

This banking customer example message is about a balance not updated after a transfer.

balance_not_updated_after_cheque_or_cash_deposit

This banking customer example message is about a balance not updated after a cheque or cash deposit.

beneficiary_not_allowed

This banking customer example message is related to a beneficiary not being allowed or a failed transfer.

cancel_transfer

This banking customer example message is related to the cancellation of a transfer.

card_about_to_expire

This banking customer example message is related to the expiration of a card.

card_acceptance

This banking customer example message is related to the scope of acceptance of a card.

card_arrival

This banking customer example message is about the arrival of a card.

card_delivery_estimate

This banking customer example message is about a card delivery estimate or timing.

card_linking

nan

card_not_working

This banking customer example message is about a card not working.

card_payment_fee_charged

This banking customer example message is about a card payment fee.

card_payment_not_recognised

This banking customer example message is about a payment the customer does not recognise.

card_payment_wrong_exchange_rate

This banking customer example message is about a wrong exchange rate.

card_swallowed

This banking customer example message is about a card swallowed by a machine.

cash_withdrawal_charge

This banking customer example message is about a cash withdrawal charge.

cash_withdrawal_not_recognised

This banking customer example message is about an unrecognised cash withdrawal.

change_pin

This banking customer example message is about changing a pin code.

compromised_card

This banking customer example message is about a compromised card.

contactless_not_working

This banking customer example message is about contactless not working

country_support

This banking customer example message is about country-specific support.

declined_card_payment

This banking customer example message is about a declined card payment.

declined_cash_withdrawal

This banking customer example message is about a declined cash withdrawal.

declined_transfer

This banking customer example message is about a declined transfer.

direct_debit_payment_not_recognised

This banking customer example message is about an unrecognised direct debit payment.

disposable_card_limits

This banking customer example message is about the limits of disposable cards.

edit_personal_details

This banking customer example message is about editing personal details.

exchange_charge

This banking customer example message is about exchange rate charges.

exchange_rate

This banking customer example message is about exchange rates.

exchange_via_app

nan

extra_charge_on_statement

This banking customer example message is about an extra charge.

failed_transfer

This banking customer example message is about a failed transfer.

fiat_currency_support

This banking customer example message is about fiat currency support

get_disposable_virtual_card

This banking customer example message is about getting a disposable virtual card.

get_physical_card

nan

getting_spare_card

This banking customer example message is about getting a spare card.

getting_virtual_card

This banking customer example message is about getting a virtual card.

lost_or_stolen_card

This banking customer example message is about a lost or stolen card.

lost_or_stolen_phone

This banking customer example message is about a lost or stolen phone.

order_physical_card

This banking customer example message is about ordering a card.

passcode_forgotten

This banking customer example message is about a forgotten passcode.

pending_card_payment

This banking customer example message is about a pending card payment.

pending_cash_withdrawal

This banking customer example message is about a pending cash withdrawal.

pending_top_up

This banking customer example message is about a pending top up.

pending_transfer

This banking customer example message is about a pending transfer.

pin_blocked

This banking customer example message is about a blocked pin.

receiving_money

This banking customer example message is about receiving money.

request_refund

This banking customer example message is about a refund request.

reverted_card_payment?

This banking customer example message is about reverting a card payment.

supported_cards_and_currencies

nan

terminate_account

This banking customer example message is about terminating an account.

top_up_by_bank_transfer_charge

nan

top_up_by_card_charge

This banking customer example message is about the charge for topping up by card.

top_up_by_cash_or_cheque

This banking customer example message is about topping up by cash or cheque.

top_up_failed

This banking customer example message is about top up issues or failures.

top_up_limits

This banking customer example message is about top up limitations.

top_up_reverted

This banking customer example message is about issues with topping up.

topping_up_by_card

This banking customer example message is about topping up by card.

transaction_charged_twice

This banking customer example message is about a transaction charged twice.

transfer_fee_charged

This banking customer example message is about an issue with a transfer fee charge.

transfer_into_account

This banking customer example message is about transfers into the customer's own account.

transfer_not_received_by_recipient

This banking customer example message is about a transfer that has not arrived yet.

transfer_timing

This banking customer example message is about transfer timing.

unable_to_verify_identity

This banking customer example message is about an issue with identity verification.

verify_my_identity

This banking customer example message is about identity verification.

verify_source_of_funds

This banking customer example message is about the source of funds.

verify_top_up

This banking customer example message is about verification and top ups

virtual_card_not_working

This banking customer example message is about a virtual card not working

visa_or_mastercard

This banking customer example message is about types of bank cards.

why_verify_identity

This banking customer example message questions why identity verification is necessary.

wrong_amount_of_cash_received

This banking customer example message is about a wrong amount of cash received.

wrong_exchange_rate_for_cash_withdrawal

This banking customer example message is about a wrong exchange rate for a cash withdrawal.

trueteacher

label

hypothesis

factually_consistent

The example summary is factually consistent with the full article.

factually_inconsistent

The example summary is factually inconsistent with the full article.

capsotu

label

hypothesis

Agriculture

This example text from a US presidential speech is about agriculture

Civil Rights

This example text from a US presidential speech is about civil rights or minorities or civil liberties

Culture

This example text from a US presidential speech is about cultural policy

Defense

This example text from a US presidential speech is about defense or military

Domestic Commerce

This example text from a US presidential speech is about banking or finance or commerce

Education

This example text from a US presidential speech is about education

Energy

This example text from a US presidential speech is about energy or electricity or fossil fuels

Environment

This example text from a US presidential speech is about the environment or water or waste or pollution

Foreign Trade

This example text from a US presidential speech is about foreign trade

Government Operations

This example text from a US presidential speech is about government operations or administration

Health

This example text from a US presidential speech is about health

Housing

This example text from a US presidential speech is about community development or housing issues

Immigration

This example text from a US presidential speech is about migration

International Affairs

This example text from a US presidential speech is about international affairs or foreign aid

Labor

This example text from a US presidential speech is about employment or labour

Law and Crime

This example text from a US presidential speech is about law, crime or family issues

Macroeconomics

This example text from a US presidential speech is about macroeconomics

Public Lands

This example text from a US presidential speech is about public lands or water management

Social Welfare

This example text from a US presidential speech is about social welfare

Technology

This example text from a US presidential speech is about space or science or technology or communications

Transportation

This example text from a US presidential speech is about transportation

manifesto

label

hypothesis

Agriculture and Farmers: Positive

This example text from a political party manifesto is positive towards policies for agriculture and farmers

Anti-Growth Economy: Positive

This example text from a political party manifesto is in favour of anti-growth politics

Anti-Imperialism

This example text from a political party manifesto is anti-imperialistic, for example against controlling other countries and for greater self-government of colonies

Centralisation

This example text from a political party manifesto is in favour of political centralisation

Civic Mindedness: Positive

This example text from a political party manifesto is positive towards national solidarity, civil society or appeals for public spiritedness or against anti-social attitudes

Constitutionalism: Negative

This example text from a political party manifesto is positive towards constitutionalism

Constitutionalism: Positive

This example text from a political party manifesto is positive towards constitutionalism and the status quo of the constitution

Controlled Economy

This example text from a political party manifesto is supportive of direct government control of the economy, e.g. price control or minimum wages

Corporatism/Mixed Economy

This example text from a political party manifesto is positive towards cooperation of government, employers, and trade unions simultaneously

Culture: Positive

This example text from a political party manifesto is in favour of cultural policies or leisure facilities, for example museus, libraries or public sport clubs

Decentralization

This example text from a political party manifesto is for decentralisation or federalism

Democracy

This example text from a political party manifesto favourably mentions democracy or democratic procedures or institutions

Economic Goals

This example text from a political party manifesto is a broad/general statement on economic goals without specifics

Economic Growth: Positive

This example text from a political party manifesto is supportive of economic growth, for example facilitation of more production or government aid for growth

Economic Orthodoxy

This example text from a political party manifesto is for economic orthodoxy, for example reduction of budget deficits, thrift or a strong currency

Economic Planning

This example text from a political party manifesto is positive towards government economic planning, e.g. policy plans or strategies

Education Expansion

This example text from a political party manifesto is about the need to expand/improve policy on education

Education Limitation

This example text from a political party manifesto is sceptical towards state expenditure on education, for example in favour of study fees or private schools

Environmental Protection

This example text from a political party manifesto is in favour of environmental protection, e.g. fighting climate change or 'green' policies or preservation of natural resources or animal rights

Equality: Positive

This example text from a political party manifesto is positive towards equality or social justice, e.g. protection of underprivileged groups or fair distribution of resources

European Community/Union: Negative

This example text from a political party manifesto negatively mentions the EU or European Community

European Community/Union: Positive

This example text from a political party manifesto is positive towards the EU or European Community, for example EU expansion and integration

Foreign Special Relationships: Negative

This example text from a political party manifesto is negative towards particular countries

Foreign Special Relationships: Positive

This example text from a political party manifesto is positive towards particular countries

Free Market Economy

This example text from a political party manifesto is in favour of a free market economy and capitalism

Freedom and Human Rights

This example text from a political party manifesto is in favour of freedom and human rights, for example freedom of speech, assembly or against state coercion or for individualism

Governmental and Administrative Efficiency

This example text from a political party manifesto is in favour of efficiency in government/administration, for example by restructuring civil service or improving bureaucracy

Incentives: Positive

This example text from a political party manifesto is favourable towards supply side economic policies supporting businesses, for example for incentives like subsidies or tax breaks

Internationalism: Negative

This example text from a political party manifesto is sceptical of internationalism, for example negative towards international cooperation, in favour of national sovereignty and unilaterialism

Internationalism: Positive

This example text from a political party manifesto is in favour of international cooperation with other countries, for example mentions the need for aid to developing countries, or global governance

Keynesian Demand Management

This example text from a political party manifesto is for keynesian demand management and demand side economic policies

Labour Groups: Negative

This example text from a political party manifesto is negative towards labour groups and unions

Labour Groups: Positive

This example text from a political party manifesto is positive towards labour groups, for example for good working conditions, fair wages or unions

Law and Order: Positive

This example text from a political party manifesto is positive towards law and order and strict law enforcement

Market Regulation

This example text from a political party manifesto is supports market regulation for a fair and open market, for example for consumer protection or for increased competition or for social market economy

Marxist Analysis

This example text from a political party manifesto is positive towards Marxist-Leninist ideas or uses specific Marxist terminology

Middle Class and Professional Groups

This example text from a political party manifesto favourably references the middle class, e.g. white colar groups or the service sector

Military: Negative

This example text from a political party manifesto is negative towards the military, for example for decreasing military spending or disarmament

Military: Positive

This example text from a political party manifesto is positive towards the military, for example for military spending or rearmament or military treaty obligations

Multiculturalism: Negative

This example text from a political party manifesto is sceptical towards multiculturalism, or for cultural integration or appeals to cultural homogeneity in society

Multiculturalism: Positive

This example text from a political party manifesto favourably mentions cultural diversity, for example for freedom of religion or linguistic heritages

National Way of Life: Negative

This example text from a political party manifesto unfavourably mentions a country's nation and history, for example sceptical towards patriotism or national pride

National Way of Life: Positive

This example text from a political party manifesto is positive towards the national way of life and history, for example pride of citizenship or appeals to patriotism

Nationalisation

This example text from a political party manifesto is positive towards government ownership of industries or land or for economic nationalisation

Non-economic Demographic Groups

This example text from a political party manifesto favourably mentions non-economic demographic groups like women, students or specific age groups

Peace

This example text from a political party manifesto is positive towards peace and peaceful means of solving crises, for example in favour of negotiations and ending wars

Political Authority

This example text from a political party manifesto mentions the speaker's competence to govern or other party's lack of such competence, or favourably mentions a strong/stable government

Political Corruption

This example text from a political party manifesto is negative towards political corruption or abuse of political/bureaucratic power

Protectionism: Negative

This example text from a political party manifesto is negative towards protectionism, in favour of free trade

Protectionism: Positive

This example text from a political party manifesto is in favour of protectionism, for example tariffs, export subsidies

Technology and Infrastructure: Positive

This example text from a political party manifesto is about technology and infrastructure, e.g. the importance of modernisation of industry, or supportive of public spending on infrastructure/tech

Traditional Morality: Negative

This example text from a political party manifesto is negative towards traditional morality, for example against religious moral values, for divorce or abortion, for modern families or separation of church and state

Traditional Morality: Positive

This example text from a political party manifesto is favourable towards traditional or religious values, for example for censorship of immoral behavour, for traditional family values or religious institutions

Underprivileged Minority Groups

This example text from a political party manifesto favourably mentions underprivileged minorities, for example handicapped, homosexuals or immigrants

Welfare State Expansion

This example text from a political party manifesto is positive towards the welfare state, e.g. health care, pensions or social housing

Welfare State Limitation

This example text from a political party manifesto is for limiting the welfare state, for example public funding for social services or social security, e.g. private care before state care

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!