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daniellgoncalves/
bge-base-horizon-europe-matryoshka

Name: bge-base-horizon-europe-matryoshka
Rating: 2.5 (78 reviews)

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BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Language: en
License: apache-2.0

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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("daniellgoncalves/bge-base-horizon-europe-matryoshka")
# Run inference
sentences = [
    'including  data. They are globally unique and long -lasting references to digital objects \r\n(such as data, publications and other research outputs)  or non -digital objects such as \r\nresearchers, research institutions, grants, etc. Frequently used persistent identifiers \r\ninclude digital object identifiers (DOIs), Handles, and others.  For further reading on PID \r\ntypes, please refer to https://www.dpconline.org/handbook/technical -solutions -and-\r\ntools/persistent -identifiers . \r\nTo enhance the findability of r esearch outputs, and their potential reuse, standardised \r\nmetadata frameworks are essential, ensuring that data and other research outputs \r\nare accompanied by rich metadata that provides them with context.  \r\nTo enhance the re -usability of research data, they must be licenced. For more \r\ninformation on the licences required for data under Horizon Europe, please refer to the \r\nAGA (article 17).  \r\nTrusted repositories  assume a central role in the Horizon Europe for the deposition of \r\nand access to publications and resea rch data. For a definition of trusted repositories in \r\nHorizon Europe please refer to the AGA (article 17). Proposers, with the help of data \r\nand research support staff (e.g. data stewards, data librarians, et c), should check \r\nwhether the repositories that th ey plan to deposit their data have the features of \r\ntrusted repositories, and justify this accordingly in their Data Management Plans.  \r\nData management plans (DMPs) are a cornerstone for responsible management of \r\nresearch outputs, notably data and are mandatory in Horizon Europe for projects \r\ngenerating  and/or reusing data (on requirements and the frequency of DMPs as \r\ndeliverables consult the AGA article 17). A template for a DMP  is provided under the \r\nreporting templates in the reference documents  of the Funding and Tenders portal of \r\nthe European Commission. Its use is recom mended but not mandatory.  DMPs are',
    'What is a cornerstone for responsible management of research outputs in Horizon Europe?\r\n',
    'How can a Beneficiary submit a Financial Statement?\r\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 45 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 54 tokens
mean: 309.0 tokens
max: 512 tokens
min: 8 tokens
mean: 18.09 tokens
max: 34 tokens

Samples:

positive	anchor
`and rules and procedures for sharing of data including licensing.`
Research outputs — Results to which access can be given in the form of scientific publications, data or other
engineered results and processes such as software, algorithms, protocols, models, workflows
and electronic notebooks.
[Scope of the obligations
For this section, references to ‘beneficiary’or ‘beneficiaries’do not include affiliated entities (if any). ]
[Agreement on background — Background free from restrictions
The beneficiaries must identify in a written agreement the background as needed for implementing the action
or for exploiting its results.
Where the call conditions restrict control due to strategic interests reasons, background that is subject to
control or other restrictions by a country (or entity from a country) which is not one of the eligible countries
or target countries set out in the call conditions and that impact the exploitation of the results (i.e. would
make the exploitation of the results subject to control or restrictions) must not be used and must be explicitly
excluded in the agreement on background —unless otherwise agreed with the granting authority. ]
[Results free from restrictions
Where the call conditions restrict control due to [strategic interests reasons ], the beneficiaries must ensure
that the results of the action are not subject to control or other restrictions by a country (or entity from a
country) which is not one of the eligible countries or target countries set out in the call conditions — unless
otherwise agreed with the granting authority. ]
Ownership of results
Results are owned by the beneficiaries that generate them. [[…]]	`Who owns the results of the action in the given context?`

`EU Grants: HE Programme Guide : V4.0 – 15.10.2023`
60
To a degree matching the type of research being proposed (from basic to
precompetitiv e) and as appropriate, AI -based systems or techniques should be, or be
developed to become (implicitly or explicitly contributing to one or several of the
following objectives):
• technically robust, accurate and reproducible, and able to deal with and infor m
about possible failures, inaccuracies and errors, proportionate to the assessed
risk posed by the AI -based system or technique
• socially robust, in that they duly consider the context and environment in which
they operate
• reliable and to function as inte nded, minimising unintentional and unexpected
harm, preventing unacceptable harm and safeguarding the physical and mental
integrity of humans
• able to provide a suitable explanation of its decision -making process, whenever
an AI -based system can have a sign ificant impact on people’s lives.	`What should AI-based systems be able to provide in terms of decision-making process?`

`EU Funding & Tenders Portal: Online Manual: V1.1 – 15.09.2022`
15 • 1 Participant Contact (PaCo) per beneficiar y
• 1 LEAR per organisation
• 1 Project Legal Signatory (PLSIGN) per organisation
• 1 Project Financial Signatory (PFSIGN) per organi sation .
One person can have several roles at the same time.
Organisations that participate as Affiliated Entities (or other type of participant — Associated
Partner, Subcontractor, etc) do NOT need any access roles in the Portal, since they are not
allowed to use it. All actions in the Portal are handled for them by the Coordinator /Beneficiary
they are linked to .

FAQ
• FAQ on users' roles and access rights

1.3 Accepting the Terms and Conditions of Us e
The Portal is part of the Single Electronic Data Exchange Area set up under Articles 147 and
128 of the EU Financial Regulation .
On the first login to M y Area, user s must agree to the Portal Terms and Conditions and the
Portal Privacy Statement.
Organisations will be asked to agree to the Terms and Conditions when they appoint their LEAR
(declaration of consent). Every time you access to My Area, you are implicitly reaffirming
your acceptance of the Terms and Conditions valid at that time.
Personal data will be kept and processed for th e purposes of the Single Electronic Data Exchange
Area, i.e. for the manage ment and implement ation of y our EU grants, contracts, prizes and
other transactions managed through the Portal. The detailed conditions for the processing of
your personal data are set out in the Portal Privacy Statement .
2. Participant Register — Register your organisation |

Who is responsible for agreeing to the Portal Terms and Conditions and the Portal Privacy Statement for an organization?
[object Object]

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 5e-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: 3.0
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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: batch_sampler
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.32.1
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	positive	anchor
type	string	string
details	min: 54 tokens mean: 309.0 tokens max: 512 tokens	min: 8 tokens mean: 18.09 tokens max: 34 tokens