metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:28450
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
What are the five criteria that community projects must meet to be
considered for funding by the Community Ownership Fund?
sentences:
- >-
We want to fund community projects that do at least 1 of these 5 things:
increase feelings of pride in, and improve perceptions of, the local
area as a place to live
improve social trust, cohesion, and sense of belonging
increase local participation in community life, arts, culture, or sport
improve local economic outcomes – including creating jobs, volunteering
opportunities, and improving employability and skills levels in the
local community
improve social and wellbeing outcomes – including having a positive
impact on physical and mental health of local people, and reducing
loneliness and social isolation
Strengthening community ownership across the UK
The Fund will be delivered directly by the UK government to communities
in England, Scotland, Wales, and Northern Ireland. The UK government is
committed to fair opportunities to access funding through the Community
Ownership Fund across the UK.
A minimum target of spending in line with per-capita allocations has
therefore been set in Scotland, Wales, and Northern Ireland. The
Community Ownership Fund will target a minimum of £12.3 million in
Scotland, £7.1 million in Wales, and £4.3 million in Northern Ireland of
the total Fund over the 4 years until March 2025.
The design of the Fund recognises the different landscapes for community
ownership across the UK, with different legislation in England and
Wales, Scotland, and Northern Ireland. We have engaged widely with local
stakeholders to ensure the Fund is effective, accessible and achieves
its objectives.
Applications will be assessed against a consistent framework.
Eligibility for the Fund and the bidding assessment criteria are
consistent in all 4 nations.
Glossary
Community asset
For this fund, an asset is physical building or space. It must be used
by the community and accessible to as many people as possible.
Community Asset Transfer
Across the United Kingdom, Community Asset Transfer (CAT) policy
frameworks support the transfer of community assets from public
authorities to community organisations. The legislation and policy
contexts work slightly differently in parts of the United Kingdom.
England
Asset of community value
In England the Localism Act 2011 introduced a right for community groups
to nominate buildings or land to their local authority as an asset of
community value.
If the local authority agreed that the nomination met the test of being
land of community value, the council would place the asset on a list of
assets of community value for a period of 5 years.
What this did was introduce a community right to bid. If the owner of a
listed asset decided that they wish to sell the asset during the 5-year
period of listing, then they must notify the local authority who would
inform the nominating community group.
- >-
In designated catchments, water companies have a duty to ensure
wastewater treatment works serving a population equivalent over 2,000
meet specified nutrient removal standards by 1 April 2030 where the
designation takes effect from 25 January 2024. For designations that
take effect subsequent to that date, the upgrade date is specified in
the notice. Competent authorities (including local planning authorities)
considering planning proposals for development draining via a sewer to a
wastewater treatment works subject to the upgrade duty are required to
consider that the nutrient pollution standard will be met by the upgrade
date for the purposes of Habitats Regulations Assessments.
Whilst the upgrade date under the Water Industry Act 1991 for this
catchment is 16 May 2031, the sewerage undertaker has committed to the
delivery of the wastewater treatment work upgrades by 1 April 2030. The
Environment Agency has also committed to varying Environmental Permits
for the relevant wastewater treatment works so that the permits will
require compliance with the nutrient pollution standard by 1 April 2030.
↩
- >-
https://gcscc.ox.ac.uk/cmm-reviews#/ ↩
World Bank, ‘Green Digital Transformation: How to Sustainably Close the
Digital Divide and Harness Digital Tools for Climate Action’
https://openknowledge.worldbank.org/entities/
publication/6be73f14-f899-4a6d-a26e-56d98393acf3 ↩
Ritchie, 2020 https://ourworldindata.org/ghg-emissions-by-sector ↩
WHO, e-waste factsheet, 2023:
https://www.who.int/news-room/fact-sheets/detail/
electronic-waste-(e-waste) ↩
International development in a contested world: ending extreme poverty
and tackling climate change
https://www.gov.uk/government/publications/international-development-in-a-contested-world-ending-extreme-poverty-and-tackling-climate-change
↩
https://www.gov.uk/government/publications/greening-government-ict-and-digitalservices-strategy-2020-2025
↩
UK Government’s Department for Environment, Food & Rural Affairs ↩
https://digitalprinciples.org/ ↩
https://www.dynamicspectrumalliance.org/ ↩
https://www.itu.int/itu-d/sites/partner2connect/ ↩
https://www.govstack.global/ ↩
- source_sentence: >-
What specific actions is the UK government implementing as part of the
third National Adaptation Programme (NAP3) to address the impacts of
climate change?
sentences:
- >-
(The Thames Barrier in London, shown at low tide. Photo by mikeinlondon
via Getty Images.)
The government is taking action to adapt the UK to climate change. This
can help reduce the costs from climate change impacts and make our
economy and society more resilient.
This page explains more about:
climate change and adaptation
the risks and opportunities of climate change
what the government is doing to make sure that the UK is prepared for
climate change – including the third National Adaptation Programme
(NAP3)
Climate change
Our climate is changing. The main cause is human activity: in
particular, burning fossil fuels for energy, which emits greenhouse
gases into the atmosphere and causes the world’s temperature to rise.
In the UK we can see the effects of climate change already. In 2022 the
UK recorded the warmest year on record with temperatures reaching over
40°C, which had impacts on public health and the environment. These
temperatures would not have been possible without climate change caused
by human activity. The frequency of hotter summers will increase in the
future, and we can expect the winters to become wetter, which will make
flooding more likely across the UK.
The government is taking action to limit climate change through its
commitment to reach net zero greenhouse gas emissions by 2050. One of
these actions is reducing our reliance on fossil fuels. Achieving ‘net
zero’ in the UK and across the world will help to limit temperature
rises in the future and reduce the level of climate change we need to
adapt to.
Climate adaptation
Climate adaptation relates to actions that protect us against the
impacts of climate change. This includes reacting to the changes we have
seen already, as well as preparing for what will happen in the future.
The UK government is taking steps to address the impacts of climate
change to protect communities, our economy and the environment.
Examples of the government’s approach to climate adaptation include:
building new flood defences to protect against rising sea levels
planning for more green spaces in urban areas to help keep them cool and
planting more drought-resistant crops
building infrastructure that can withstand expected climate impacts such
as extreme heat and flooding
Many of the actions in NAP3 can help to improve our standard of living
too, by upgrading our buildings and infrastructure, improving the
sustainability and productivity of important sectors such as agriculture
and forestry, and restoring our natural environment.
Climate risks and opportunities
Climate change can lead to both risks and opportunities, although there
are more risks than opportunities. Without measures to adapt to climate
change, we would experience additional issues including:
health risks
damage to houses and infrastructure
- >-
We will help shape an international order in which all citizens are well
informed, able to participate in democratic processes and enjoy their
rights in offline and online public spaces, as well as freedom of
expression; and we will promote an information ecosystem that supports
accountability and inclusive deliberative democracy.
The UK commits to an open, free, global, interoperable, reliable and
secure Internet; and to ensuring emerging tech supports, rather than
erodes, the enjoyment of democracy, human rights and fundamental
freedoms. Working collectively with international partners, civil
society and the tech sector is critical in ensuring that the online
world and technologies promote freedom, democracy and inclusion, and
protect human rights and fundamental freedoms.
We will strengthen our collaboration in the multi-stakeholder spaces
that support digital democracy. We will enhance our advisory support to
the Freedom Online Coalition (FOC) and will bid to continue as a member
of the FOC Steering Committee and to maintain our role as co-chairs of
the Taskforce on Internet Shutdowns (TFIS).
We will support our overseas network to better understand the threat
posed by information disorder through digital platforms. In doing so, we
will identify international best practice and increase our understanding
of information disorder in elections, independent media as well as
gendered disinformation impacts on women’s political empowerment and
participation in electoral processes.
We will champion the importance of a vibrant, independent, and
pluralistic civic space online and offline, where people can exercise
their freedoms. We will work in collaboration with other donors, civil
society, academia and the private sector to leverage the opportunities
and mitigate the risks that digital transformation provides for civil
society and civic space.
We will support open and accountable use of emerging digital
technologies, especially the need for democratic and human rights
safeguards. This includes grant support for the Open Government
Partnership to help enable open and accountable use of emerging digital
technologies by driving digital governance reforms in 10 countries
(Ghana, Indonesia, Kenya, Nigeria, Dominic Republic, Armenia, Colombia,
Zambia, the Philippines and Ukraine), accelerating collective action and
norm-raising on digital governance and increasing impact through better
connection between global pledges and country action.
Chapter 3 – Digital inclusion: leaving no one behind in a digital world
The benefits of digital transformation are not evenly distributed. A
third of the world’s population is offline, and that is concentrated
within the poorest and most marginalised groups.
- >-
Estimated one-off impact on administrative burden (£ million)
One-off impact (£ million) £30,000 to £50,000 threshold Above £50,000
threshold Total mandated population above £30,000
Costs 338 223 561
Savings — — —
Estimated continuing impact on administrative burden (£ million)
Continuing average annual impact (£ million) £30,000 to £50,000
threshold Above £50,000 threshold Total mandated population above
£30,000
Costs 110 90 201
Savings 2 3 5
Net impact on annual administrative burden +108 +88 +196
Numbers do not sum due to rounding.
Operational impact (£ million) (HMRC or other)
There will be both IT and resource costs for HMRC in developing,
applying, and policing this measure, and in updating guidance.
HMRC IT and non-IT costs for this next phase of MTD expansion are
expected to be in the region of £0.5bn to the end of March 2028.
Other impacts
HMRC is required to consider the justice impact test and rural proofing
measures in relation to their impacts on rural communities and the
justice system.
HMRC’s assessments suggest any impact is likely to be negligible.
Mitigations are in place for those whose rural location impacts their
internet access to the point where it is not feasible to operate MTD, as
discussed in the ‘Equalities impacts’ section.
This measure does not fall within the scope of the environmental
principles duty.
Other impacts have been considered and none have been identified.
Monitoring and evaluation
HMRC’s communications programme includes work to build software
developer, agent and taxpayer readiness, to promote inclusion in the
large-scale public beta testing programme beginning in 2025 and
encourage voluntary early adoption of MTD for ITSA.
HMRC is committed to monitoring and formally evaluating the impact of
MTD for ITSA, including both customer and revenue impacts. This will
build on HMRC’s track record in successfully evaluating MTD for VAT and
publishing the findings. Independent social research will be undertaken
both before and after MTD for ITSA is introduced to gather evidence of
customer impacts and behaviour change. Self Assessment data will be used
to monitor take-up and estimate additional tax revenue due to MTD. The
evaluation will take until at least 2029, when all data for the 2027 to
28 tax year becomes available for analysis.
Further advice
- source_sentence: >-
Who are the joint leaders of the new Anti-social Behaviour Taskforce
responsible for overseeing the implementation and delivery of the action
plan?
sentences:
- >-
80. It is also vital that we measure the overall success of this plan in
tackling anti-social behaviour to ensure that it is meeting the
commitments we have set out. We will assess the impact of our proposals
on both communities’ experience and perceptions of anti-social behaviour
and their effectiveness in tackling it. To achieve this, we will draw
from the wide range of data enhancements outlined throughout this plan,
alongside wider measures, to monitor and evaluate its success and to
further inform our understanding of what works in driving down
anti-social behaviour.
81. We will oversee the implementation and delivery to this action plan
with a new Anti-social Behaviour Taskforce jointly led by the Home
Secretary and the Secretary of State for Levelling Up that will bring
together national and local partners, with a sole focus of addressing
anti-social behaviour and restoring pride in place in communities.
Home Office. Anti-social behaviour: impacts on individuals and local
communities. 2023 ↩
Home Office. Guidance: Anti-social behaviour principles. 2022. ↩
Home Office. Anti-social behaviour: impacts on individuals and local
communities. 2023. ↩
YouGov. Anti-Social Behaviour. 2023. ↩
A legal definition of ASB can be found in the Anti-Social Behaviour Act
2014: a) conduct that has caused, or is likely to cause, harassment,
alarm or distress to any person, b) conduct capable of causing nuisance
or annoyance to a person in relation to that person’s occupation of
residential premises, or c) conduct capable of causing housing-related
nuisance or annoyance to any person. ↩
Ipsos. Ipsos Levelling Up Index: Levelling up Panel. 2022. ↩
Public First. Levelling Up Poll. 2021. ↩
Office for National Statistics. Crime in England and Wales: Other
related tables . 2022. ↩
Office for National Statistics. Crime Survey for England and Wales
(CSEW) estimates of personal and household crime, anti-social behaviour,
and public perceptions, by police force area, year ending September
2022. ↩
Office for National Statistics. Crime in England and Wales: Police Force
Area data tables. 2023. Office for National Statistics. Crime in England
and Wales: Other related tables. 2023. Office for National Statistics.
Crime in England and Wales: Annual Trend and Demographic Tables. 2022. ↩
- >-
323. Similarly, DCMS Ministers in both Houses of Parliament expressed at
the dispatch box their disappointment about the proposed changes to BBC
local radio services. There have also been several instances over the
Charter period where a lack of effective transparency in engaging the
public has been highlighted in the media and by Parliamentarians. For
example, the BBC’s failure to explain how it was dealing with complaints
about the anti-semitic incident on a bus on Oxford Street at the end of
2021 in the face of significant public pressure received widespread
media coverage. The announcement of the closure of BBC Singers led to
Parliamentary discussions and media reports raising concerns about how
the decision had been made and communicated, including internally within
the BBC.
The government’s response
324. When considering how the BBC communicates with audiences, it is our
view that the BBC should be held to a higher standard than other
organisations given the extent of its public funding. This higher
standard needs to go beyond publication of more data and information, to
straightforward and open communication with audiences. The BBC Board has
overall responsibility for ensuring that the BBC communicates changes
that have an impact on audiences effectively with those audiences. This
has to be accompanied by equally effective communication with its
workforce. Evidence received indicates that the BBC has not always
achieved this.
7.1 We recommend that the BBC continues to learn from recent experiences
where announcements about service changes have led to criticism about
the BBC’s approach to transparency.
7.2 We also recommend that the BBC publishes details of its strategy for
communicating with audiences which explains improvements to its
communications approach already made, but also how it identifies any
changes needed so that audiences and staff can be confident that future
service changes and their impact will be explained clearly.
Understanding audience needs
What we learnt
325. During evidence gathering, many stakeholders made proposals
regarding how the BBC could improve its transparency in specific ways to
help audiences hold it to account. All of these proposals related to
individual specific themes in previous chapters. Ofcom’s research
suggests that there are perception issues with the BBC’s impartiality
that more effective transparency could help address.
The government’s response
326. It is important that licence fee payers do not just have the
opportunity to shape the services that the BBC provides, but that they
also have the opportunity to tell the BBC how they would like the BBC to
be more transparent.
- >-
67. Building on our Fraud Plan, DWP is investing £70 million between
2022/23 and 2024/25 in advanced analytics to tackle fraud and error,
which it expects will help it to generate savings of around £1.6 billion
by 2030/31[footnote 24].
68. Investing in advanced analytics, such as machine learning, is
essential to enable the public sector to keep up with offenders.
Sophisticated crimminals already utilise such tools to analyse large
amounts of data to exploit existing weaknesses and vulnerabilities in
public sector systems. In DWP these tools can play a crucial role in
detecting and preventing fraudulent activities in DWPs benefit systems.
Going forward we want to maximise the benefits that advanced analytics
and machine learning can offer.
69. Where these tools are used to assist in the prevention and detection
of fraud, DWP always ensures appropriate safeguards are in place to
ensure the proportionate, ethical, and lawful use of data with human
input. In decision making, any final decision will always be made by a
member of DWP staff and DWP seeks to ensure compliance using internal
monitoring protocols. DWPs Personal Information Charter sets out in more
detail how the Department uses these tools, as well as Artificial
Intelligence and automated decision making.
Continuous improvement to Universal Credit (UC)
70. As we complete the Move to UC, the Department’s spending on UC alone
is forecast to double (relative to 2022/23 in nominal terms) to reach
over £85 billion by 2028/29[footnote 25].
71. We are constantly improving UC to reduce fraud and error and to
ensure the right support reaches the right people.
72. Building on our previous Fraud Plan our UC Continuous Improvement
plan brings together multi-disciplinary teams to look at the largest
areas of loss within UC and considers how we can improve our processes
to reduce these.
73. These teams focus on understanding the root-causes and scale of the
losses, design and test solutions with a view to implementing them more
widely if the tests are successful. The implementation of these
solutions may involve changes to policy, improvements to the operation
of UC service or greater use of data and automation to prevent the
fraud.
- source_sentence: What is the date and time of the next meeting?
sentences:
- >-
Defra is working with the British Standards Institution (BSI) to develop
a suite of nature investment standards that will support best practice
standardisation of methodologies with regards to best practices for
assessing the baseline, monitoring, and verifying the delivery of
nature-based carbon removals. This will be critical for the purposes of
supplying and selling credits into nature markets, and for quantifying
within value chain mitigation of environmental impacts. These standards
will build on and aim to align with the work of international integrity
initiatives, including the Integrity Council for Voluntary Carbon
Markets (ICVCM) and the Voluntary Carbon Markets Initiative (VCMI).
As part of this programme, BSI is developing the ‘Nature markets -
Overarching principles and framework’, which will apply to nature-based
environmental improvement projects and the quantification of ecosystem
services. These principles will set the basis by which nature markets
can be more effectively designed and governed. A first draft of the BSI
Flex 701 standard was published for consultation in March 2024.
Further to this, BSI will be developing more specific thematic and
market specific standards to follow over the course of 2024 to 2025, for
example, for nature-based carbon and biodiversity. This will include a
certification mechanism to allow methodologies which meet these
standards to become certified as offering high integrity.
1.2 A standardised approach to product level impact quantification
Increasingly, businesses are seeing the benefits of communicating
product level impact data to consumers and other businesses in the
supply chain. Product level accounting can help improve understanding of
the impacts of specific products and supply chains to inform changes at
the supplier and product level to reduce impacts. Product level data can
also enable more accurate reporting of company impacts from the
‘bottom-up’, by summing up the impact of all products sold by the
company, in addition to any energy use or emissions on site.
Product level impact data is generated through lifecycle assessments
(LCAs). Although there are many commonalities between Scope 3 and
product carbon footprinting, there are a number of practical and
methodological differences summarised in section 4.1 of the WRAP
Protocol.
Relevant priorities
1.3 – A standardised product level accounting method (including
multi-metric approach)
Developing a product level accounting method
- >-
To enable efficient and extensive use of genomic AMR data, the design
and implementation of data handling solutions will be explored. The
design should accommodate complexities such as AMR outbreaks caused by
the same AMR-causing mobile genetic element transferred among different
pathogen species, or longer-term trends in AMR epidemiology. These
should provide new or use existing open standards, for the handling of
AMR-related information, to facilitate working with international
partners and allow convenient and effective querying for surveillance
and response planning. Few countries offer large scale sequencing and
analysis of AMR associated isolates so UK data would provide vital
insight into the molecular epidemiology of these infections and position
the UK to exploit the knowledge these new methods can provide.
Theme 2 - Optimising the use of antimicrobials
Outcome 4 - Antimicrobial stewardship and disposal
By 2029, the UK has strengthened antimicrobial stewardship and
diagnostic stewardship by improved targeting of antimicrobials and
diagnostic tools for humans, animals and plants, and improved the
disposal of antimicrobials, informed by the right data, risk
stratification and guidance.
This outcome has:
3 commitments:
clinical decision support
appropriate prescribing and disposal
behavioural interventions
2 human health targets (see appendix B):
target 4a: by 2029, we aim to reduce total antibiotic use in human
populations by 5% from the 2019 baseline
target 4b: by 2029, we aim to achieve 70% of total use of antibiotics
from the Access category (new UK category) across the human healthcare
system
While all use of antimicrobials drives AMR, there is an opportunity to
reduce inappropriate use of antimicrobials occurring, for example, when
antimicrobials are taken when they are not needed, or when taken for
longer than necessary.
According to the National Institute for Health and Care Excellence’s
NICE guideline (NG15):
The term ‘antimicrobial stewardship’ is defined as ‘an organisational or
healthcare‑system‑wide approach to promoting and monitoring judicious
use of antimicrobials to preserve their future effectiveness’.
- |-
None.
Date of next meeting: 1 December 2021 at 11am to 12.30pm
- source_sentence: >-
How much funding has the government committed to expand the Public Sector
Fraud Authority to deploy AI in combating fraud?
sentences:
- >-
2) Embracing the opportunities presented by making greater use of
cutting-edge technology, such as AI, across the public sector. The
government is:
More than doubling the size of i.AI, the AI incubator team, ensuring
that the UK government has the in-house expertise consisting of the most
talented technology professionals in the UK, who can apply their skills
and expertise to appropriately seize the benefits of AI across the
public sector and Civil Service.
Committing £34 million to expand the Public Sector Fraud Authority by
deploying AI to help combat fraud across the public sector, making it
easier to spot, stop and catch fraudsters thereby saving £100 million
for the public purse.
Committing £17 million to accelerate DWP’s digital transformation,
replacing paper-based processes with simplified online services, such as
a new system for the Child Maintenance Service.
Committing £14 million for public sector research and innovation
infrastructure. This includes funding to develop the next generation of
health and security technologies, unlocking productivity improvements in
the public and private sector alike.
3) Strengthening preventative action to reduce demand on public
services. The government is:
Committing an initial £105 million towards a wave of 15 new special free
schools to create over 2,000 additional places for children with special
educational needs and disabilities (SEND) across England. This will help
more children receive a world-class education and builds on the
significant levels of capital funding for SEND invested at the 2021
Spending Review. The locations of these special free schools will be
announced by May 2024.
Confirming the location of 20 Alternative Provision (AP) free schools,
which will create over 1,600 additional AP places across England as part
of the Spending Review 2021 commitment to invest £2.6 billion capital in
high needs provision. This will support early intervention, helping
improve outcomes for children requiring alternative provision, and
helping them to fulfil their potential.
- >-
We will help build the UKDev (UK International Development) approach and
brand by leveraging the UK’s comparative advantage within both the
public and private sectors. We will build first and foremost on existing
successful partnerships, through which we share UK models and expertise
to support digital transformation in partner countries. For example,
through our collaboration with the British Standards Institution (BSI)
we will expand our collaboration to build the capacity of partner
countries in Africa and South-East Asia (including through ASEAN) on
digital standards, working with local private sector and national
standards-setting bodies.
We will strengthen our delivery of peer learning activities in
collaboration with Ofcom, exchanging experiences and sharing the UK
models on spectrum management, local networks and other technical areas
with telecoms regulators in partner countries, building on the positive
peer-learning experience with Kenya and South Africa.
We will collaborate with Government Digital Service (GDS) to share
know-how with partner countries on digitalisation in the public sector,
building on our advisory role in GovStack[footnote 56]. We will leverage
the UK experience of DPI for public or regulated services (health,
transport, banking, land registries) based on the significant demand for
this expertise from developing countries and riding the momentum on DPI
generated by the G20 India presidency of 2023.
6.4 Enhancing FCDO’s digital development capability
The UK government will also enhance its own digital development
capability to keep up with the pace of technological change, to be
forward-looking and anticipate emergent benefits and risks of digital
transformation. We will invest in new research on digital technologies
and on their inclusive business models to build the global evidence
base, share lessons learned and improve knowledge management through our
portfolio of digital development and technology programmes, including
the FCDO’s new Technology Centre for Expertise (Tech CoE), which will
complement and support our programming portfolio.
Since all sectors within international development are underpinned by
digital technologies, we will ensure that digital development skills are
mainstreamed across the FCDO. We will raise awareness and upgrade staff
knowledge through new training opportunities on best practice in the
complex and evolving area of digital development, through partnering
with existing FCDO capability initiatives, ie the International
Academy’s Development Faculty, the Cyber Network and the International
Technology curriculum.
- >-
The Burma (Sanctions) (EU Exit) Regulations 2019 (S.I. 2019/136)
(revoked) 29 January 2019 To ensure that the UK continues to operate an
effective sanctions regime in relation to Burma after end of the
Transition Period, replacing with substantially the same effect the EU
sanctions regime relating to Burma that was previously in force in the
UK under EU legislation and related UK legislation. Section 2(4) report
(PDF, 74 KB) and section 18 report (PDF, 65 KB).
The Burma (Sanctions) (Overseas Territories) Order 2020 (S.I. 2020/1264)
(revoked)[footnote 81] 11 November 2020 To extend with modifications The
Burma (Sanctions) (EU Exit) Regulations 2019 (S.I. 2019/136) as amended
from time to time to all British Overseas Territories except Bermuda and
Gibraltar (which implement sanctions under their own legislative
arrangements).
The Myanmar (Sanctions) Regulations 2021 (S.I. 2021/496) 26 April 2021
To establish a UK autonomous sanctions regime in respect of Myanmar
comprising financial, immigration and trade sanctions, replacing the
existing sanctions regime established by The Burma (Sanctions) (EU Exit)
Regulations 2019 (S.I. 2019/136).
The Myanmar (Sanctions) (Overseas Territories) Order 2021 (S.I.
2021/528) 28 April 2021 To extend with modifications The Myanmar
(Sanctions) Regulations 2021 (S.I. 2021/496) as amended from time to
time to all British Overseas Territories except Bermuda and Gibraltar
(which implement sanctions under their own legislative arrangements).
The Myanmar (Sanctions) (Isle of Man) Order 2021 (S.I. 2021/529) 28
April 2021 To extend to the Isle of Man with modifications The Myanmar
(Sanctions) Regulations 2021 (S.I. 2021/496) as amended from time to
time.
See also in section (C) of this Annex:
the Sanctions Regulations (Commencement No. 1) (EU Exit) Regulations
2019 (S.I. 2019/627)
the Sanctions (EU Exit) (Miscellaneous Amendments) (No. 2) Regulations
2020 (S.I. 2020/590)
the Sanctions (EU Exit) (Miscellaneous Amendments) (No. 4) Regulations
2020 (S.I. 2020/951)
the Sanctions (EU Exit) (Miscellaneous Amendments) (No. 2) Regulations
2022 (S.I. 2022/818)
Statutory guidance for this regime was published on 29 April 2021.
19. Nicaragua
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8601045098831278
name: Pearson Cosine
- type: spearman_cosine
value: 0.8581596602965272
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8604789808039027
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8571595448874573
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8615938042335468
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8581596602965272
name: Spearman Euclidean
- type: pearson_dot
value: 0.8601045118561034
name: Pearson Dot
- type: spearman_dot
value: 0.8581596602965272
name: Spearman Dot
- type: pearson_max
value: 0.8615938042335468
name: Pearson Max
- type: spearman_max
value: 0.8581596602965272
name: Spearman Max
SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
It has been finetuned on a range of Q&A pairs based of UK government policy documents.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: sentence-transformers/all-MiniLM-L6-v2
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("AndreasThinks/all-MiniLM-L6-v2_policy_doc_finetune")
# Run inference
sentences = [
'How much funding has the government committed to expand the Public Sector Fraud Authority to deploy AI in combating fraud?',
'2) Embracing the opportunities presented by making greater use of cutting-edge technology, such as AI, across the public sector. The government is:\nMore than doubling the size of i.AI, the AI incubator team, ensuring that the UK government has the in-house expertise consisting of the most talented technology professionals in the UK, who can apply their skills and expertise to appropriately seize the benefits of AI across the public sector and Civil Service.\nCommitting £34 million to expand the Public Sector Fraud Authority by deploying AI to help combat fraud across the public sector, making it easier to spot, stop and catch fraudsters thereby saving £100 million for the public purse.\nCommitting £17 million to accelerate DWP’s digital transformation, replacing paper-based processes with simplified online services, such as a new system for the Child Maintenance Service.\nCommitting £14 million for public sector research and innovation infrastructure. This includes funding to develop the next generation of health and security technologies, unlocking productivity improvements in the public and private sector alike.\n3) Strengthening preventative action to reduce demand on public services. The government is:\nCommitting an initial £105 million towards a wave of 15 new special free schools to create over 2,000 additional places for children with special educational needs and disabilities (SEND) across England. This will help more children receive a world-class education and builds on the significant levels of capital funding for SEND invested at the 2021 Spending Review. The locations of these special free schools will be announced by May 2024.\nConfirming the location of 20 Alternative Provision (AP) free schools, which will create over 1,600 additional AP places across England as part of the Spending Review 2021 commitment to invest £2.6 billion capital in high needs provision. This will support early intervention, helping improve outcomes for children requiring alternative provision, and helping them to fulfil their potential.',
'We will help build the UKDev (UK International Development) approach and brand by leveraging the UK’s comparative advantage within both the public and private sectors. We will build first and foremost on existing successful partnerships, through which we share UK models and expertise to support digital transformation in partner countries. For example, through our collaboration with the British Standards Institution (BSI) we will expand our collaboration to build the capacity of partner countries in Africa and South-East Asia (including through ASEAN) on digital standards, working with local private sector and national standards-setting bodies.\nWe will strengthen our delivery of peer learning activities in collaboration with Ofcom, exchanging experiences and sharing the UK models on spectrum management, local networks and other technical areas with telecoms regulators in partner countries, building on the positive peer-learning experience with Kenya and South Africa.\nWe will collaborate with Government Digital Service (GDS) to share know-how with partner countries on digitalisation in the public sector, building on our advisory role in GovStack[footnote 56]. We will leverage the UK experience of DPI for public or regulated services (health, transport, banking, land registries) based on the significant demand for this expertise from developing countries and riding the momentum on DPI generated by the G20 India presidency of 2023.\n 6.4 Enhancing FCDO’s digital development capability\nThe UK government will also enhance its own digital development capability to keep up with the pace of technological change, to be forward-looking and anticipate emergent benefits and risks of digital transformation. We will invest in new research on digital technologies and on their inclusive business models to build the global evidence base, share lessons learned and improve knowledge management through our portfolio of digital development and technology programmes, including the FCDO’s new Technology Centre for Expertise (Tech CoE), which will complement and support our programming portfolio.\nSince all sectors within international development are underpinned by digital technologies, we will ensure that digital development skills are mainstreamed across the FCDO. We will raise awareness and upgrade staff knowledge through new training opportunities on best practice in the complex and evolving area of digital development, through partnering with existing FCDO capability initiatives, ie the International Academy’s Development Faculty, the Cyber Network and the International Technology curriculum.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev - Evaluated with
EmbeddingSimilarityEvaluator
| Metric | Value |
|---|---|
| pearson_cosine | 0.8601 |
| spearman_cosine | 0.8582 |
| pearson_manhattan | 0.8605 |
| spearman_manhattan | 0.8572 |
| pearson_euclidean | 0.8616 |
| spearman_euclidean | 0.8582 |
| pearson_dot | 0.8601 |
| spearman_dot | 0.8582 |
| pearson_max | 0.8616 |
| spearman_max | 0.8582 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 16learning_rate: 2e-05num_train_epochs: 2warmup_ratio: 0.1use_mps_device: Truebatch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonelearning_rate: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 2max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Trueseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseeval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falsebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|---|---|---|---|---|
| 0.0562 | 100 | 0.3598 | 0.8263 | 0.8672 |
| 0.1124 | 200 | 0.1983 | 0.7948 | 0.8666 |
| 0.1686 | 300 | 0.2021 | 0.7623 | 0.8666 |
| 0.2248 | 400 | 0.1844 | 0.7510 | 0.8657 |
| 0.2811 | 500 | 0.1704 | 0.7575 | 0.8629 |
| 0.3373 | 600 | 0.1643 | 0.7348 | 0.8641 |
| 0.3935 | 700 | 0.1808 | 0.7293 | 0.8604 |
| 0.4497 | 800 | 0.1494 | 0.7232 | 0.8636 |
| 0.5059 | 900 | 0.1563 | 0.7161 | 0.8634 |
| 0.5621 | 1000 | 0.1345 | 0.7115 | 0.8643 |
| 0.6183 | 1100 | 0.1344 | 0.7142 | 0.8617 |
| 0.6745 | 1200 | 0.1584 | 0.7106 | 0.8622 |
| 0.7307 | 1300 | 0.1488 | 0.7130 | 0.8592 |
| 0.7870 | 1400 | 0.1391 | 0.7034 | 0.8635 |
| 0.8432 | 1500 | 0.1433 | 0.7140 | 0.8614 |
| 0.8994 | 1600 | 0.1393 | 0.7067 | 0.8612 |
| 0.9556 | 1700 | 0.1644 | 0.6950 | 0.8628 |
| 1.0118 | 1800 | 0.1399 | 0.7072 | 0.8594 |
| 1.0680 | 1900 | 0.12 | 0.7093 | 0.8594 |
| 1.1242 | 2000 | 0.0904 | 0.7040 | 0.8587 |
| 1.1804 | 2100 | 0.082 | 0.6962 | 0.8585 |
| 1.2366 | 2200 | 0.0715 | 0.6985 | 0.8593 |
| 1.2929 | 2300 | 0.0624 | 0.7233 | 0.8562 |
| 1.3491 | 2400 | 0.0725 | 0.7064 | 0.8581 |
| 1.4053 | 2500 | 0.0665 | 0.7034 | 0.8570 |
| 1.4615 | 2600 | 0.0616 | 0.6940 | 0.8584 |
| 1.5177 | 2700 | 0.0703 | 0.6886 | 0.8599 |
| 1.5739 | 2800 | 0.0564 | 0.6860 | 0.8603 |
| 1.6301 | 2900 | 0.0603 | 0.6962 | 0.8590 |
| 1.6863 | 3000 | 0.0729 | 0.6906 | 0.8589 |
| 1.7426 | 3100 | 0.0753 | 0.6946 | 0.8579 |
| 1.7988 | 3200 | 0.0711 | 0.6909 | 0.8582 |
| 1.8550 | 3300 | 0.0743 | 0.6896 | 0.8583 |
| 1.9112 | 3400 | 0.0693 | 0.6902 | 0.8581 |
| 1.9674 | 3500 | 0.0845 | 0.6904 | 0.8582 |
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}