---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:46338
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: What criteria must Member States consider when establishing penalties
for infringements of the specified Regulation, and what is the deadline for notifying
the Commission about these rules?
sentences:
- 'Enforcement
1.
Member States shall lay down the rules on penalties applicable to infringements
of this Regulation and shall take all measures necessary to ensure that they are
implemented. The penalties provided for must be effective, proportionate and dissuasive
taking into account, in particular, the nature, duration, recurrence and gravity
of the infringement. Member States shall, by 31 December 2024, notify the Commission
of those rules and of those measures and shall notify it without delay of any
subsequent amendment affecting them.
2.'
- Within the transitional periods established, Member States shall progressively
reduce their respective gaps with regard to the new minimum levels of taxation.
However, where the difference between the national level and the minimum level
does not exceed 3 % of that minimum level, the Member State concerned may wait
until the end of the period to adjust its national level.
- 'AR 10. ‘Indirect political contribution’ refers to those political contributions
made through an intermediary organisation such as a lobbyist or charity, or support
given to an organisation such as a think tank or trade association linked to or
supporting particular political parties or causes.
AR 11. When determining ‘comparable position’ in this standard, the undertaking
shall consider various factors, including level of responsibility and scope of
activities undertaken.
AR 12. The undertaking may provide the following information on its financial
or in-kind contributions with regard to its lobbying expenses:
(a)
the total monetary amount of such internal and external expenses; and
(b)'
- source_sentence: How does the use of AI systems impact access to essential public
assistance benefits and services?
sentences:
- X. Among these substances there are ‘priority hazardous substances’ which means
substances identified in accordance with Article 16(3) and (6) for which measures
have to be taken in accordance with Article 16(1) and (8). --- --- 31. ‘Pollutant’means
any substance liable to cause pollution, in particular those listed in Annex VIII.
--- --- 32. ‘Direct discharge to groundwater’means discharge of pollutants into
groundwater without percolation throughout the soil or subsoil. --- --- 33. ‘Pollution’means
the direct or indirect introduction, as a result of human activity, of substances
or heat into the air, water or land which may be harmful to human health or the
quality of aquatic ecosystems or terrestrial ecosystems directly depending on
- 'The competent authorities shall inform the requesting competent authorities of
any decision taken under the first subparagraph, stating the reasons therefor.
4.
In order to ensure uniform application of this Article, ESMA may develop draft
implementing technical standards to establish common procedures for competent
authorities to cooperate in on-the-spot verifications and investigations.
Power is conferred on the Commission to adopt the implementing technical standards
referred to in the first subparagraph in accordance with Article 15 of Regulation
(EU) No 1095/2010.
Article 55
Dispute settlement'
- (58) Another area in which the use of AI systems deserves special consideration
is the access to and enjoyment of certain essential private and public services
and benefits necessary for people to fully participate in society or to improve
one’s standard of living. In particular, natural persons applying for or receiving
essential public assistance benefits and services from public authorities namely
healthcare services, social security benefits, social services providing protection
in cases such as maternity, illness, industrial accidents, dependency or old age
and loss of employment and social and housing assistance, are typically dependent
on those benefits and services and in a vulnerable position in relation to the
responsible
- source_sentence: How does the context suggest promoting vulnerable customers' active
engagement in the energy market?
sentences:
- energy efficiency improvement measures as priority actions; --- --- (c) carry
out early, forward-looking investments in energy efficiency improvement measures
before distributional impacts from other policies and measures show their effect;
--- --- (d) foster technical assistance and the roll-out of enabling funding and
financial tools, such as on-bill schemes, local loan-loss reserve, guarantee funds,
funds targeting deep renovations and renovations with minimum energy gains; ---
--- (e) foster technical assistance for social actors to promote vulnerable customer’s
active engagement in the energy market, and positive changes in their energy consumption
behaviour; --- --- (f) ensure access to finance, grants or subsidies bound to minimum
- '4.
To the extent that the tasks relating to the implementation of the Innovation
Fund are not delegated to an implementing body, the Commission shall carry out
those tasks.
Article 18
Tasks of the implementing body
►M2 The implementing body designated in accordance with Article 17(1) of this
Regulation to implement the Innovation Fund in accordance with Article 17(2) may
be entrusted with the overall management of the calls for proposals, the disbursement
of the Innovation Fund support and the monitoring of the implementation of selected
projects. ◄ For that purpose, the implementing body may be entrusted with the
following tasks:
(a)
organising the call for proposals;
(b)'
- 'Calculation
Calculations of emissions shall be performed using the formula:
Activity data × Emission factor × Oxidation factor
Activity data (fuel used, production rate etc.) shall be monitored on the basis
of supply data or measurement.'
- source_sentence: What is the purpose of Directive 2004/109/EC of the European Parliament
and of the Council of 15 December 2004?
sentences:
- '3.7. Uses advised against ►M7 (see Section 1 of the safety data sheet) ◄
Where applicable, an indication of the uses which the registrant advises against
and why (i.e. non-statutory recommendations by supplier). This need not be an
exhaustive list.
4. CLASSIFICATION AND LABELLING
▼M3
4.1 The hazard classification of the substance(s), resulting from the application
of Title I and II of Regulation (EC) No 1272/2008 for all hazard classes and categories
in that Regulation,
In addition, for each entry, the reasons why no classification is given for a
hazard class or differentiation of a hazard class should be provided (i.e. if
data are lacking, inconclusive, or conclusive but not sufficient for classification),'
- '(b)
operations by which the user of an energy product makes its reuse possible in
his own undertaking provided that the taxation already paid on such product is
not less than the taxation which would be due if the reused energy product were
again to be liable to taxation;
(c)
an operation consisting of mixing, outside a production establishment or a tax
warehouse, energy products with other energy products or other materials, provided
that:
(i)
taxation on the components has been paid previously; and
(ii)
the amount paid is not less than the amount of the tax which would be chargeable
on the mixture.
The condition under (i) shall not apply where the mixture is exempted for a specific
use.
Article 22'
- '( 15 ) Directive 2004/109/EC of the European Parliament and of the Council of
15 December 2004 on the harmonisation of transparency requirements in relation
to information about issuers whose securities are admitted to trading on a regulated
market and amending Directive 2001/34/EC (OJ L 390, 31.12.2004, p. 38).
( 16 ) Regulation (EU) 2020/852 of the European Parliament and of the Council
of 18 June 2020 on the establishment of a framework to facilitate sustainable
investment, and amending Regulation (EU) 2019/2088 (OJ L 198, 22.6.2020, p. 13).
( 17 ) OJ L 142, 30.4.2004, p. 12.
( 18 ) OJ L 340, 22.12.2007, p. 66.'
- source_sentence: What are the main objectives of the directives mentioned in the
text regarding greenhouse gas emissions and carbon dioxide storage, and how do
they relate to environmental protection and sustainability within the European
Union?
sentences:
- '(24) Directive 2003/87/EC of the European Parliament and of the Council of 13
October 2003 establishing a scheme for greenhouse gas emission allowance trading
within the Union and amending Council Directive 96/61/EC (OJ L 275, 25.10.2003,
p. 32).
(25) Directive 2009/31/EC of the European Parliament and of the Council of 23
April 2009 on the geological storage of carbon dioxide and amending Council Directive
85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC,
2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006 (OJ L 140,
5.6.2009, p. 114).
(26) Directive 2014/23/EU of the European Parliament and of the Council of 26
February 2014 on the award of concession contracts (OJ L 94, 28.3.2014, p. 1).'
- 'Article 33
Responsibility and liability for drawing up and publishing the financial statements
and the management report
▼M4
1.'
- '(b)
risks related to the undertaking’s dependencies on consumers and/or end-users
may include the loss of business continuity where an economic crisis makes consumers
unable to afford certain products or services;
(c)
►C1 opportunities related to the undertaking’s impacts on consumers and/or end-
users may include market differentiation and greater customer appeal from offering
safe products or privacy-respecting services; and ◄
(d)'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.6659761781460383
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8841705506645952
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9312963921974797
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9672017952701536
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6659761781460383
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29472351688819837
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1862592784394959
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09672017952701535
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6659761781460383
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8841705506645952
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9312963921974797
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9672017952701536
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8278291318026204
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7818480980055302
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.783515504381956
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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:
```bash
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("sentence_transformers_model_id")
# Run inference
sentences = [
'What are the main objectives of the directives mentioned in the text regarding greenhouse gas emissions and carbon dioxide storage, and how do they relate to environmental protection and sustainability within the European Union?',
'(24) Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003 establishing a scheme for greenhouse gas emission allowance trading within the Union and amending Council Directive 96/61/EC (OJ L 275, 25.10.2003, p. 32).\n\n(25) Directive 2009/31/EC of the European Parliament and of the Council of 23 April 2009 on the geological storage of carbon dioxide and amending Council Directive 85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC, 2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006 (OJ L 140, 5.6.2009, p. 114).\n\n(26) Directive 2014/23/EU of the European Parliament and of the Council of 26 February 2014 on the award of concession contracts (OJ L 94, 28.3.2014, p. 1).',
'Article 33\n\nResponsibility and liability for drawing up and publishing the financial statements and the management report\n\n▼M4\n\n1.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.666 |
| cosine_accuracy@3 | 0.8842 |
| cosine_accuracy@5 | 0.9313 |
| cosine_accuracy@10 | 0.9672 |
| cosine_precision@1 | 0.666 |
| cosine_precision@3 | 0.2947 |
| cosine_precision@5 | 0.1863 |
| cosine_precision@10 | 0.0967 |
| cosine_recall@1 | 0.666 |
| cosine_recall@3 | 0.8842 |
| cosine_recall@5 | 0.9313 |
| cosine_recall@10 | 0.9672 |
| **cosine_ndcg@10** | **0.8278** |
| cosine_mrr@10 | 0.7818 |
| cosine_map@100 | 0.7835 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 46,338 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 11 tokens
- mean: 35.24 tokens
- max: 206 tokens
| - min: 4 tokens
- mean: 193.39 tokens
- max: 512 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| How is materiality defined in the context of an entity's sustainability reporting as per QC 4? | QC 4. Materiality is an entity-specific aspect of relevance based on the nature or magnitude, or both, of the items to which the information relates, as assessed in the context of the undertaking’s sustainability reporting (see chapter 3 of this Standard).
Faithful representation
QC 5. To be useful, the information must not only represent relevant phenomena, it must also faithfully represent the substance of the phenomena that it purports to represent. Faithful representation requires information to be (i) complete, (ii) neutral and (iii) accurate. |
| What procedure must be followed for the adoption of implementing acts as mentioned in the text? | Those implementing acts shall be adopted in accordance with the examination procedure referred to in Article 22a(2).
3.
Articles 9, 9a and 10 shall apply to maritime transport activities in the same manner as they apply to other activities covered by the EU ETS with the following exception with regard to the application of Article 10. |
| How should monitoring points be distributed for groundwater bodies that flow across Member State boundaries to effectively estimate groundwater flow? | The network shall include sufficient representative monitoring points to estimate the groundwater level in each groundwater body or group of bodies taking into account short and long-term variations in recharge and in particular:
— for groundwater bodies identified as being at risk of failing to achieve environmental objectives under Article 4, ensure sufficient density of monitoring points to assess the impact of abstractions and discharges on the groundwater level,
— for groundwater bodies within which groundwater flows across a Member State boundary, ensure sufficient monitoring points are provided to estimate the direction and rate of groundwater flow across the Member State boundary.
2.2.3. Monitoring frequency |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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
- `torch_empty_cache_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
- `num_train_epochs`: 3
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin