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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:46338
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-m-v2.0
widget:
- source_sentence: What is the definition of 'Union processing capacity' and how does
it relate to the location of processing operations for strategic raw materials?
sentences:
- '(9)
‘Union processing capacity’ means an aggregate of the maximum annual production
volumes of processing operations for strategic raw materials, excluding such operations
that are typically located at or near the extraction site, located in the Union;
(10)
‘recycling’ means recycling as defined in Article 3, point (17), of Directive
2008/98/EC;
(11)
‘Union recycling capacity’ means an aggregate of the maximum annual production
volume of recycling operations for strategic raw materials after re- processing,
including the sorting and pre-treatment of waste, and its processing into secondary
raw materials, located in the Union;
(12)'
- 206-44-0 205-912-4 Fluoranthene (16) 118-74-1 204-273-9 Hexachlorobenzene X (17)
87-68-3 201-765-5 Hexachlorobutadiene X (18) 608-73-1 210-168-9 Hexachlorocyclohexane
X (19) 34123-59-6 251-835-4 Isoproturon (20) 7439-92-1 231-100-4 Lead and its
compounds (21) 7439-97-6 231-106-7 Mercury and its compounds X (22) 91-20-3 202-049-5
Naphthalene (23) 7440-02-0 231-111-4 Nickel and its compounds (24) not applicable
not applicable Nonylphenols X (5) (25) not applicable not applicable Octylphenols
(6) (26) 608-93-5 210-172-0 Pentachlorobenzene X (27) 87-86-5 201-778-6 Pentachlorophenol
(28) not applicable not applicable Polyaromatic hydrocarbons (PAH) (7) X (29)
122-34-9 204-535-2 Simazine (30) not applicable not applicable Tributyltin compounds
X
- '7.
The principles governing public procurement procedures, including the principles
of proportionality, non-discrimination, equal treatment, transparency and competition,
shall be observed as regards all economic operators involved in the public procurement
procedure. The investigation of foreign subsidies pursuant to this Regulation
shall not result in the contracting authority or the contracting entity treating
the economic operators concerned in a way that is contrary to those principles.
Environmental, social and labour requirements shall apply to economic operators
in accordance with Directives 2014/23/EU, 2014/24/EU and 2014/25/EU, or other
Union law.
8.'
- source_sentence: What types of services are one-stop shops or similar mechanisms
expected to provide to households and small non-household entities regarding energy
efficiency?
sentences:
- Low boiling point cat-reformed naphtha; 649-302-00-X 270-687-1 68476-47-1 P Residues
(petroleum), C6-8 catalytic reformer; Low boiling point cat-reformed naphtha;
[A complex residuum from the catalytic reforming of C6-8 feed. It consists of
hydrocarbons having carbon numbers predominantly in the range of C2 through C6.]
649-303-00-5 270-794-3 68478-15-9 P Naphtha (petroleum), light catalytic reformed,
arom.-free; Low boiling point cat-reformed naphtha; [A complex combination of
hydrocarbons obtained from distillation of products from a catalytic reforming
process. It consists predominantly of hydrocarbons having carbon numbers predominantly
in the range of C5 through C8 and boiling in the range of approximately 35 °C
to 120 °C (95 °F to
- 'The undertaking may disclose by head count or full time equivalent (FTE) the
following information:
(a)
full-time employees , and breakdowns by gender and by region; and
(b)
part-time employees, and breakdowns by gender and by region.
Disclosure Requirement S1-7 – Characteristics of non-employees in the undertaking’s
own workforce
The undertaking shall describe key characteristics of non-employees in its own
workforce.'
- (a) the creation of one-stop shops or similar mechanisms for the provision of
technical, administrative and financial advice and assistance on energy efficiency,
such as energy checks for households, energy renovations of buildings, information
on the replacement of old and inefficient heating systems with modern and more
efficient appliances and the take-up of renewable energy and energy storage for
buildings to final customers and final users, especially household and small non-household
ones, including SMEs and microenterprises; (b) cooperation with private actors
that provide services such as energy audits and energy consumption assessments,
financing solutions and execution of energy renovations; --- --- (c) the communication
of
- source_sentence: What procedures must competent authorities follow to verify compliance
of operators and traders with the specified regulations regarding products they
place or intend to place on the market?
sentences:
- '1.
The competent authorities shall carry out checks within their territory to establish
whether operators and traders established in the Union comply with this Regulation.
The competent authorities shall carry out checks within their territory to establish
whether the relevant products that the operator or trader has placed or intends
to place on the market, has made available or intends to make available on the
market or has exported or intends to export comply with this Regulation.
2.
The checks referred to in paragraph 1 of this Article shall be carried out in
accordance with Articles 18 and 19.
3.'
- '▼M2 —————
▼B
7.
Implementing bodies, other than executive agencies, and entities to which the
management of the Innovation Fund revenues has been delegated pursuant Article
20(3) shall provide the Commission with the following:
(a)
by 15 February, unaudited financial statements covering the preceding financial
year, which shall run from 1 January to 31 December, in respect of the activities
delegated to those implementing bodies and entities;
(b)
by 15 March of the year of the transmission of the unaudited financial statements,
the audited financial statements covering the preceding financial year, which
shall run from 1 January to 31 December, in respect of the activities delegated
to those implementing bodies and entities.'
- (44) Where a company cannot prevent, mitigate, bring to an end or minimise the
extent of all the identified actual and potential adverse impacts at the same
time to the full extent, it should prioritise the adverse impacts based on their
severity and likelihood. The severity of an adverse impact should be assessed
based on the scale, scope or irremediable character of the adverse impact, taking
into account the gravity of the impact, including the number of individuals that
are or will be affected, the extent to which the environment is or may be damaged
or otherwise affected, its irreversibility and the limits on the ability to restore
affected individuals or the environment to a situation equivalent to their situation
prior to the impact
- source_sentence: What are the possible outcomes for a testing proposal that does
not comply with the requirements outlined in Annexes IX, X, and XI?
sentences:
- '3.
Where the competent authority of the Member State of reference considers that
an authorised non-EU AIFM is in breach of its obligations under this Directive,
it shall notify ESMA, setting out full reasons as soon as possible.
4.
Member States shall ensure that the competent authorities have the powers necessary
to take all measures required in order to ensure the orderly functioning of markets
in those cases where the activity of one or more AIFs in the market for a financial
instrument could jeopardise the orderly functioning of that market.
Article 47
Powers and competences of ESMA
1.'
- '40.
not chemically modified substance: means a substance whose chemical structure
remains unchanged, even if it has undergone a chemical process or treatment, or
a physical mineralogical transformation, for instance to remove impurities;
41.
alloy: means a metallic material, homogenous on a macroscopic scale, consisting
of two or more elements so combined that they cannot be readily separated by mechanical
means.
Article 4
General provision'
- '(c)
a decision in accordance with points (a), (b) or (d) but requiring registrant(s)
or downstream user(s) to carry out one or more additional tests in cases of non-compliance
of the testing proposal with Annexes IX, X and XI;
(d)
a decision rejecting the testing proposal;
(e)'
- source_sentence: What conditions must a new registrant meet in order to refer to
previously submitted study summaries for a substance that has already been registered?
sentences:
- '5.
If a substance has already been registered, a new registrant shall be entitled
to refer to the study summaries or robust study summaries, for the same substance
submitted earlier, provided that he can show that the substance that he is now
registering is the same as the one previously registered, including the degree
of purity and the nature of impurities, and that the previous registrant(s) have
given permission to refer to the full study reports for the purpose of registration.
A new registrant shall not refer to such studies in order to provide the information
required in Section 2 of Annex VI.
Article 14
Chemical safety report and duty to apply and recommend risk reduction measures
1.'
- of high boiling fractions from bituminous coal high temperature tar and/or pitch
coke oil, with a softening point of 140 to 170 °C according to DIN 52025. Composed
primarily of tri- and polynuclear aromatic compounds which also contain heteroatoms.)
648-057-00-6 302-650-3 94114-13-3 M Residues (coal tar), pitch distillation; Pitch
redistillate (Residue from the fractional distillation of pitch distillate boiling
in the range of approximately 400 to 470 °C. Composed primarily of polynuclear
aromatic hydrocarbons, and heterocyclic compounds.) 648-058-00-1 295-507-9 92061-94-4
M Tar, coal, high-temperature, distillation and storage residues; Coal tar solids
residue (Coke- and ash-containing solid residues that separate on distillation
and thermal treatment of bituminous coal high temperature tar in distillation
installations and storage vessels. Consists predominantly of carbon and contains
a small quantity of hetero compounds as well as ash components.) 648-059-00-7
295-535-1 92062-20-9 M Tar, coal, storage residues; Coal tar solids residue (The
deposit removed from crude coal tar storages. Composed primarily of coal tar and
carbonaceous particulate matter.) 648-060-00-2 293-764-1 91082-50-7 M Tar, coal,
high-temperature, residues; Coal tar solids residue (Solids formed during the
coking of bituminous coal to produce crude bituminous coal high temperature tar.
Composed primarily of coke and coal particles, highly aromatised compounds and
mineral substances.) 648-061-00-8 309-726-5 100684-51-3 M Tar, coal, high-temperature,
high-solids; Coal tar solids residue (The condensation product obtained by cooling,
to approximately ambient temperature, the gas evolved in the high temperature
(greater than 700 °C) destructive distillation of coal. Composed primarily of
a complex mixture of condensed ring aromatic hydrocarbons with a high solid content
of coal-type materials.) 648-062-00-3 273-615-7 68990-61-4 M Waste solids, coal-tar
pitch coking; Coal tar solids residue (The combination of wastes formed by the
coking of bituminous coal tar pitch. It consists predominantly of carbon.) 648-063-00-9
295-549-8 92062-34-5 M Extract residues (coal), brown; Coal tar extract (The residue
from extraction of dried coal.)
- '4. Member States shall establish a network of experts from various sectors such
as the health, building and social sectors, or entrust an existing network, to
develop strategies to support local and national decision makers in implementing
energy efficiency improvement measures, technical assistance and financial tools
aiming to alleviate energy poverty. Member States shall strive to ensure that
the composition of the network of experts ensures gender balance and reflects
the perspectives of all people.
Member States may entrust the network of experts to offer advice on:'
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-m-v2.0
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7203521491455205
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.923701018470568
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9554634904194718
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9796305886414638
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7203521491455205
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3079003394901893
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1910926980838943
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09796305886414639
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7203521491455205
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.923701018470568
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9554634904194718
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9796305886414638
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8635032698612493
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8247555204831233
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8257385664756074
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0). 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:** [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) <!-- at revision 0d1661ceed1cb456c85726749d5be61ebb30d4f1 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 8192, 'do_lower_case': False}) with Transformer model: GteModel
(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:
```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 conditions must a new registrant meet in order to refer to previously submitted study summaries for a substance that has already been registered?',
'5.\n\nIf a substance has already been registered, a new registrant shall be entitled to refer to the study summaries or robust study summaries, for the same substance submitted earlier, provided that he can show that the substance that he is now registering is the same as the one previously registered, including the degree of purity and the nature of impurities, and that the previous registrant(s) have given permission to refer to the full study reports for the purpose of registration.\n\nA new registrant shall not refer to such studies in order to provide the information required in Section 2 of Annex VI.\n\nArticle 14\n\nChemical safety report and duty to apply and recommend risk reduction measures\n\n1.',
'of high boiling fractions from bituminous coal high temperature tar and/or pitch coke oil, with a softening point of 140 to 170 °C according to DIN 52025. Composed primarily of tri- and polynuclear aromatic compounds which also contain heteroatoms.) 648-057-00-6 302-650-3 94114-13-3 M Residues (coal tar), pitch distillation; Pitch redistillate (Residue from the fractional distillation of pitch distillate boiling in the range of approximately 400 to 470 °C. Composed primarily of polynuclear aromatic hydrocarbons, and heterocyclic compounds.) 648-058-00-1 295-507-9 92061-94-4 M Tar, coal, high-temperature, distillation and storage residues; Coal tar solids residue (Coke- and ash-containing solid residues that separate on distillation and thermal treatment of bituminous coal high temperature tar in distillation installations and storage vessels. Consists predominantly of carbon and contains a small quantity of hetero compounds as well as ash components.) 648-059-00-7 295-535-1 92062-20-9 M Tar, coal, storage residues; Coal tar solids residue (The deposit removed from crude coal tar storages. Composed primarily of coal tar and carbonaceous particulate matter.) 648-060-00-2 293-764-1 91082-50-7 M Tar, coal, high-temperature, residues; Coal tar solids residue (Solids formed during the coking of bituminous coal to produce crude bituminous coal high temperature tar. Composed primarily of coke and coal particles, highly aromatised compounds and mineral substances.) 648-061-00-8 309-726-5 100684-51-3 M Tar, coal, high-temperature, high-solids; Coal tar solids residue (The condensation product obtained by cooling, to approximately ambient temperature, the gas evolved in the high temperature (greater than 700 °C) destructive distillation of coal. Composed primarily of a complex mixture of condensed ring aromatic hydrocarbons with a high solid content of coal-type materials.) 648-062-00-3 273-615-7 68990-61-4 M Waste solids, coal-tar pitch coking; Coal tar solids residue (The combination of wastes formed by the coking of bituminous coal tar pitch. It consists predominantly of carbon.) 648-063-00-9 295-549-8 92062-34-5 M Extract residues (coal), brown; Coal tar extract (The residue from extraction of dried coal.)',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7204 |
| cosine_accuracy@3 | 0.9237 |
| cosine_accuracy@5 | 0.9555 |
| cosine_accuracy@10 | 0.9796 |
| cosine_precision@1 | 0.7204 |
| cosine_precision@3 | 0.3079 |
| cosine_precision@5 | 0.1911 |
| cosine_precision@10 | 0.098 |
| cosine_recall@1 | 0.7204 |
| cosine_recall@3 | 0.9237 |
| cosine_recall@5 | 0.9555 |
| cosine_recall@10 | 0.9796 |
| **cosine_ndcg@10** | **0.8635** |
| cosine_mrr@10 | 0.8248 |
| cosine_map@100 | 0.8257 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 46,338 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 42.34 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 260.86 tokens</li><li>max: 2053 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What actions can a Member State take if it believes urgent measures are necessary to protect human health or the environment regarding a substance, and what are the requirements for informing other entities about these actions?</code> | <code>1.<br><br>Where a Member State has justifiable grounds for believing that urgent action is essential to protect human health or the environment in respect of a substance, on its own, in a ►M3 mixture ◄ or in an article, even if satisfying the requirements of this Regulation, it may take appropriate provisional measures. The Member State shall immediately inform the Commission, the Agency and the other Member States thereof, giving reasons for its decision and submitting the scientific or technical information on which the provisional measure is based.<br><br>2.</code> |
| <code>Under what circumstances can Member States extend the time limits for the permit-granting process for Strategic Projects, and what is the maximum extension period allowed?</code> | <code>(b)<br><br>12 months for Strategic Projects involving only processing or recycling.<br><br>3.<br><br>Where an environmental impact assessment is required pursuant to Directive 2011/92/EU, the step of the assessment referred to in Article 1(2), point (g)(i), of that Directive shall not be included in the duration for permit- granting process referred to in paragraphs 1 and 2 of this Article.<br><br>4.<br><br>In exceptional cases, where the nature, complexity, location or size of the Strategic Project so require, Member States may extend, before their expiry and on a case-by-case basis, the time limits referred to in:<br><br>(a)<br><br>paragraph 1, point (a), and paragraph 2, point (a), by a maximum of six months;<br><br>(b)</code> |
| <code>What types of compounds primarily compose the distillates mentioned in the context?</code> | <code>(86 °F to 572 °F). Composed primarily of partly hydrogenated condensed-ring aromatic hydrocarbons, aromatic compounds containing nitrogen, oxygen and sulfur, and their alkyl derivatives having carbon numbers predominantly in the range of C4 through C14.] 648-148-00-0 302-688-0 94114-52-0 J Distillates (coal), solvent extn., hydrocracked; [Distillate obtained by hydrocracking of coal extract or solution produced by the liquid solvent extraction or supercritical gas extraction processes and boiling in the range of approximately 30 °C to 300 °C (86 °F to 572 °F). Composed primarily of aromatic, hydrogenated aromatic and naphthenic compounds, their alkyl derivatives and alkanes with carbon numbers predominantly in the range of C4 through C14.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:------:|:-----:|:-------------:|:--------------:|
| 0.0863 | 500 | 0.2243 | 0.8154 |
| 0.1726 | 1000 | 0.1242 | 0.8270 |
| 0.2589 | 1500 | 0.0877 | 0.8298 |
| 0.3452 | 2000 | 0.0823 | 0.8284 |
| 0.4316 | 2500 | 0.0627 | 0.8351 |
| 0.5179 | 3000 | 0.0636 | 0.8385 |
| 0.6042 | 3500 | 0.0587 | 0.8356 |
| 0.6905 | 4000 | 0.0746 | 0.8398 |
| 0.7768 | 4500 | 0.05 | 0.8440 |
| 0.8631 | 5000 | 0.0495 | 0.8441 |
| 0.9494 | 5500 | 0.0569 | 0.8451 |
| 1.0 | 5793 | - | 0.8432 |
| 1.0357 | 6000 | 0.0368 | 0.8458 |
| 1.1220 | 6500 | 0.0267 | 0.8501 |
| 1.2084 | 7000 | 0.0402 | 0.8451 |
| 1.2947 | 7500 | 0.0261 | 0.8524 |
| 1.3810 | 8000 | 0.0304 | 0.8503 |
| 1.4673 | 8500 | 0.0345 | 0.8521 |
| 1.5536 | 9000 | 0.0337 | 0.8551 |
| 1.6399 | 9500 | 0.0221 | 0.8525 |
| 1.7262 | 10000 | 0.0287 | 0.8560 |
| 1.8125 | 10500 | 0.0291 | 0.8549 |
| 1.8988 | 11000 | 0.0315 | 0.8577 |
| 1.9852 | 11500 | 0.0226 | 0.8577 |
| 2.0 | 11586 | - | 0.8578 |
| 2.0715 | 12000 | 0.0162 | 0.8552 |
| 2.1578 | 12500 | 0.0161 | 0.8561 |
| 2.2441 | 13000 | 0.0224 | 0.8550 |
| 2.3304 | 13500 | 0.0277 | 0.8601 |
| 2.4167 | 14000 | 0.0238 | 0.8591 |
| 2.5030 | 14500 | 0.0155 | 0.8593 |
| 2.5893 | 15000 | 0.0164 | 0.8598 |
| 2.6756 | 15500 | 0.0259 | 0.8624 |
| 2.7620 | 16000 | 0.0114 | 0.8617 |
| 2.8483 | 16500 | 0.025 | 0.8635 |
### Framework Versions
- Python: 3.10.15
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu126
- Accelerate: 1.5.2
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## 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}
}
```
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