Instructions to use AndreasThinks/all-MiniLM-L6-v2_policy_doc_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AndreasThinks/all-MiniLM-L6-v2_policy_doc_finetune with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("AndreasThinks/all-MiniLM-L6-v2_policy_doc_finetune")

sentences = [
    "What are the five criteria that community projects must meet to be considered for funding by the Community Ownership Fund?",
    "We want to fund community projects that do at least 1 of these 5 things:\nincrease feelings of pride in, and improve perceptions of, the local area as a place to live\nimprove social trust, cohesion, and sense of belonging\nincrease local participation in community life, arts, culture, or sport\nimprove local economic outcomes – including creating jobs, volunteering opportunities, and improving employability and skills levels in the local community\nimprove social and wellbeing outcomes – including having a positive impact on physical and mental health of local people, and reducing loneliness and social isolation\nStrengthening community ownership across the UK\nThe 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.\nA 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.\nThe 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.\nApplications will be assessed against a consistent framework. Eligibility for the Fund and the bidding assessment criteria are consistent in all 4 nations.\nGlossary\nCommunity asset\nFor this fund, an asset is physical building or space. It must be used by the community and accessible to as many people as possible.\nCommunity Asset Transfer\nAcross 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.\nEngland\nAsset of community value\nIn 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.\nIf 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.\nWhat 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.  \nWhilst 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#/ ↩\nWorld 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 ↩\nRitchie, 2020 https://ourworldindata.org/ghg-emissions-by-sector ↩\nWHO, e-waste factsheet, 2023: https://www.who.int/news-room/fact-sheets/detail/ electronic-waste-(e-waste) ↩\nInternational 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 ↩\nhttps://www.gov.uk/government/publications/greening-government-ict-and-digitalservices-strategy-2020-2025 ↩\nUK Government’s Department for Environment, Food & Rural Affairs ↩\nhttps://digitalprinciples.org/ ↩\nhttps://www.dynamicspectrumalliance.org/ ↩\nhttps://www.itu.int/itu-d/sites/partner2connect/ ↩\nhttps://www.govstack.global/ ↩"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

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: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
use_mps_device: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: True
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_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}
}