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eu-legal-ft-2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1658
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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eu-legal-ft-2 / README.md
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1658
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: How are representatives of Member States designated in relation
to their contact responsibilities towards the Board and stakeholders?
sentences:
- "4.\nMember States shall ensure that their representatives on the Board:\n(a)\
\ have the relevant competences and powers in their Member State so as to contribute\
\ actively to the achievement of the \nBoard’s tasks referred to in Article 66;\n\
(b) are designated as a single contact point vis-à-vis the Board and, where appropriate,\
\ taking into account Member States’ \nneeds, as a single contact point for stakeholders;\n\
OJ L, 12.7.2024\nEN\nELI: http://data.europa.eu/eli/reg/2024/1689/oj\n95/144"
- "43/144\n(54)\nDirective (EU) 2019/1937 of the European Parliament and of the\
\ Council of 23 October 2019 on the protection of persons who \nreport breaches\
\ of Union law (OJ L 305, 26.11.2019, p. 17).\n(55)\nOJ L 123, 12.5.2016, p. 1.\n\
(56)\nRegulation (EU) No 182/2011 of the European Parliament and of the Council\
\ of 16 February 2011 laying down the rules and \ngeneral principles concerning\
\ mechanisms for control by Member States of the Commission’s exercise of implementing\
\ powers (OJ \nL 55, 28.2.2011, p. 13)."
- "Article 65\nEstablishment and structure of the European Artificial Intelligence\
\ Board\n1.\nA European Artificial Intelligence Board (the ‘Board’) is hereby\
\ established.\n2.\nThe Board shall be composed of one representative per Member\
\ State. The European Data Protection Supervisor shall \nparticipate as observer.\
\ The AI Office shall also attend the Board’s meetings, without taking part in\
\ the votes. Other national \nand Union authorities, bodies or experts may be\
\ invited to the meetings by the Board on a case by case basis, where the \nissues\
\ discussed are of relevance for them.\n3.\nEach representative shall be designated\
\ by their Member State for a period of three years, renewable once.\n4."
- source_sentence: What criteria should be used to define an 'AI system' in this Regulation?
sentences:
- "12.\nNotified bodies shall participate in coordination activities as referred\
\ to in Article 38. They shall also take part \ndirectly, or be represented in,\
\ European standardisation organisations, or ensure that they are aware and up\
\ to date in \nrespect of relevant standards.\nArticle 32\nPresumption of conformity\
\ with requirements relating to notified bodies\nWhere a conformity assessment\
\ body demonstrates its conformity with the criteria laid down in the relevant\
\ harmonised \nstandards or parts thereof, the references of which have been published\
\ in the Official Journal of the European Union, it shall \nbe presumed to comply\
\ with the requirements set out in Article 31 in so far as the applicable harmonised\
\ standards cover \nthose requirements.\nEN"
- "1.\nProviders of high-risk AI systems shall, upon a reasoned request by a competent\
\ authority, provide that authority all \nthe information and documentation necessary\
\ to demonstrate the conformity of the high-risk AI system with the \nrequirements\
\ set out in Section 2, in a language which can be easily understood by the authority\
\ in one of the official \nlanguages of the institutions of the Union as indicated\
\ by the Member State concerned.\n2.\nUpon a reasoned request by a competent authority,\
\ providers shall also give the requesting competent authority, as \napplicable,\
\ access to the automatically generated logs of the high-risk AI system referred\
\ to in Article 12(1), to the extent \nsuch logs are under their control.\n3."
- "(12)\nThe notion of ‘AI system’ in this Regulation should be clearly defined\
\ and should be closely aligned with the work of \ninternational organisations\
\ working on AI to ensure legal certainty, facilitate international convergence\
\ and wide \nacceptance, while providing the flexibility to accommodate the rapid\
\ technological developments in this field. \nMoreover, the definition should\
\ be based on key characteristics of AI systems that distinguish it from simpler\
\ \ntraditional software systems or programming approaches and should not cover\
\ systems that are based on the rules \ndefined solely by natural persons to automatically\
\ execute operations. A key characteristic of AI systems is their"
- source_sentence: What conditions must be met for the use of a 'real-time' remote
biometric identification system in publicly accessible spaces?
sentences:
- "the relevant law enforcement authority has completed a fundamental rights impact\
\ assessment and, unless provided \notherwise in this Regulation, has registered\
\ the system in the database as set out in this Regulation. The reference \ndatabase\
\ of persons should be appropriate for each use case in each of the situations\
\ mentioned above.\n(35)\nEach use of a ‘real-time’ remote biometric identification\
\ system in publicly accessible spaces for the purpose of law \nenforcement should\
\ be subject to an express and specific authorisation by a judicial authority\
\ or by an independent \nadministrative authority of a Member State whose decision\
\ is binding. Such authorisation should, in principle, be"
- "(i)\ncontribute to effective cooperation with the competent authorities of third\
\ countries and with international \norganisations;\n(j)\nassist national competent\
\ authorities and the Commission in developing the organisational and technical\
\ expertise \nrequired for the implementation of this Regulation, including by\
\ contributing to the assessment of training needs for \nstaff of Member States\
\ involved in implementing this Regulation;\n(k) assist the AI Office in supporting\
\ national competent authorities in the establishment and development of AI \n\
regulatory sandboxes, and facilitate cooperation and information-sharing among\
\ AI regulatory sandboxes;\n(l)\ncontribute to, and provide relevant advice on,\
\ the development of guidance documents;"
- "referred to in Article 35 of Regulation (EU) 2016/679 and in Article 39 of Regulation\
\ (EU) 2018/1725, may arise \nduring the sandbox experimentation, as well as response\
\ mechanisms to promptly mitigate those risks and, where \nnecessary, stop the\
\ processing;\n(d) any personal data to be processed in the context of the sandbox\
\ are in a functionally separate, isolated and protected \ndata processing environment\
\ under the control of the prospective provider and only authorised persons have\
\ access to \nthose data;\n(e) providers can further share the originally collected\
\ data only in accordance with Union data protection law; any \npersonal data\
\ created in the sandbox cannot be shared outside the sandbox;"
- source_sentence: What responsibilities does the AI Office have in monitoring general-purpose
AI models according to the Regulation?
sentences:
- 'of the tasks of the scientific panel under Article 68(2).
OJ L, 12.7.2024
EN
ELI: http://data.europa.eu/eli/reg/2024/1689/oj
111/144'
- "Commission. The AI Office should be able to carry out all necessary actions to\
\ monitor the effective implementation \nof this Regulation as regards general-purpose\
\ AI models. It should be able to investigate possible infringements of \nthe\
\ rules on providers of general-purpose AI models both on its own initiative,\
\ following the results of its \nmonitoring activities, or upon request from market\
\ surveillance authorities in line with the conditions set out in this \nRegulation.\
\ To support effective monitoring of the AI Office, it should provide for the\
\ possibility that downstream \nproviders lodge complaints about possible infringements\
\ of the rules on providers of general-purpose AI models and \nsystems.\n(163)"
- "representative and importers accordingly.\n2.\nWhere the high-risk AI system\
\ presents a risk within the meaning of Article 79(1) and the provider becomes\
\ aware of \nthat risk, it shall immediately investigate the causes, in collaboration\
\ with the reporting deployer, where applicable, and \ninform the market surveillance\
\ authorities competent for the high-risk AI system concerned and, where applicable,\
\ the \nnotified body that issued a certificate for that high-risk AI system in\
\ accordance with Article 44, in particular, of the nature \nof the non-compliance\
\ and of any relevant corrective action taken.\nArticle 21\nCooperation with competent\
\ authorities\n1."
- source_sentence: What is the role of the Commission in assessing a harmonised standard
proposed by a European standardisation organisation?
sentences:
- "biometric identification systems in publicly accessible spaces for purposes other\
\ than law enforcement, including by \ncompetent authorities, should not be covered\
\ by the specific framework regarding such use for the purpose of law \nenforcement\
\ set by this Regulation. Such use for purposes other than law enforcement should\
\ therefore not be \nsubject to the requirement of an authorisation under this\
\ Regulation and the applicable detailed rules of national law \nthat may give\
\ effect to that authorisation.\n(39)\nAny processing of biometric data and other\
\ personal data involved in the use of AI systems for biometric \nidentification,\
\ other than in connection to the use of real-time remote biometric identification\
\ systems in publicly"
- Member States relating to the making available on the market of measuring instruments
(OJ L 96, 29.3.2014, p. 149).
- "to in paragraph 1, or parts of those specifications, shall be presumed to be\
\ in conformity with the requirements set out in \nSection 2 of this Chapter or,\
\ as applicable, to comply with the obligations referred to in Sections 2 and\
\ 3 of Chapter V, to \nthe extent those common specifications cover those requirements\
\ or those obligations.\n4.\nWhere a harmonised standard is adopted by a European\
\ standardisation organisation and proposed to the \nCommission for the publication\
\ of its reference in the Official Journal of the European Union, the Commission\
\ shall assess the \nharmonised standard in accordance with Regulation (EU) No\
\ 1025/2012. When reference to a harmonised standard is"
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: 0.81
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.93
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.95
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.81
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30999999999999994
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18999999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.81
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.93
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.95
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9068999830894289
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8770119047619047
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8770119047619047
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) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 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("KatGaw/eu-legal-ft-2")
# Run inference
sentences = [
'What is the role of the Commission in assessing a harmonised standard proposed by a European standardisation organisation?',
'to in paragraph 1, or parts of those specifications, shall be presumed to be in conformity with the requirements set out in \nSection 2 of this Chapter or, as applicable, to comply with the obligations referred to in Sections 2 and 3 of Chapter V, to \nthe extent those common specifications cover those requirements or those obligations.\n4.\nWhere a harmonised standard is adopted by a European standardisation organisation and proposed to the \nCommission for the publication of its reference in the Official Journal of the European Union, the Commission shall assess the \nharmonised standard in accordance with Regulation (EU) No 1025/2012. When reference to a harmonised standard is',
'Member States relating to the making available on the market of measuring instruments (OJ L 96, 29.3.2014, p. 149).',
]
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]
```
<!--
### 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 | 0.81 |
| cosine_accuracy@3 | 0.93 |
| cosine_accuracy@5 | 0.95 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.81 |
| cosine_precision@3 | 0.31 |
| cosine_precision@5 | 0.19 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.81 |
| cosine_recall@3 | 0.93 |
| cosine_recall@5 | 0.95 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9069** |
| cosine_mrr@10 | 0.877 |
| cosine_map@100 | 0.877 |
<!--
## 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: 1,658 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: 2 tokens</li><li>mean: 21.21 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 126.72 tokens</li><li>max: 217 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What documentation must the provider prepare according to Article 11 and Annex IV?</code> | <code>(b) the provider has drawn up the technical documentation in accordance with Article 11 and Annex IV;<br>(c) the system bears the required CE marking and is accompanied by the EU declaration of conformity referred to in <br>Article 47 and instructions for use;<br>(d) the provider has appointed an authorised representative in accordance with Article 22(1).<br>OJ L, 12.7.2024<br>EN<br>ELI: http://data.europa.eu/eli/reg/2024/1689/oj<br>65/144</code> |
| <code>What must accompany the system alongside the CE marking as per the context provided?</code> | <code>(b) the provider has drawn up the technical documentation in accordance with Article 11 and Annex IV;<br>(c) the system bears the required CE marking and is accompanied by the EU declaration of conformity referred to in <br>Article 47 and instructions for use;<br>(d) the provider has appointed an authorised representative in accordance with Article 22(1).<br>OJ L, 12.7.2024<br>EN<br>ELI: http://data.europa.eu/eli/reg/2024/1689/oj<br>65/144</code> |
| <code>What actions will the Commission take if there are doubts about a notified body's competence?</code> | <code>1.<br>The Commission shall, where necessary, investigate all cases where there are reasons to doubt the competence of <br>a notified body or the continued fulfilment by a notified body of the requirements laid down in Article 31 and of its <br>applicable responsibilities.<br>2.<br>The notifying authority shall provide the Commission, on request, with all relevant information relating to the <br>notification or the maintenance of the competence of the notified body concerned.<br>3.<br>The Commission shall ensure that all sensitive information obtained in the course of its investigations pursuant to this <br>Article is treated confidentially in accordance with Article 78.<br>4.</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
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 30
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 30
- `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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:-------:|:----:|:-------------:|:--------------:|
| 0.3012 | 50 | - | 0.8523 |
| 0.6024 | 100 | - | 0.8744 |
| 0.9036 | 150 | - | 0.8993 |
| 1.0 | 166 | - | 0.9049 |
| 1.2048 | 200 | - | 0.8871 |
| 1.5060 | 250 | - | 0.8737 |
| 1.8072 | 300 | - | 0.8864 |
| 2.0 | 332 | - | 0.8850 |
| 2.1084 | 350 | - | 0.8884 |
| 2.4096 | 400 | - | 0.8776 |
| 2.7108 | 450 | - | 0.8779 |
| 3.0 | 498 | - | 0.8864 |
| 3.0120 | 500 | 1.1103 | 0.8866 |
| 3.3133 | 550 | - | 0.8956 |
| 3.6145 | 600 | - | 0.9069 |
| 3.9157 | 650 | - | 0.9079 |
| 4.0 | 664 | - | 0.9055 |
| 4.2169 | 700 | - | 0.9000 |
| 4.5181 | 750 | - | 0.8907 |
| 4.8193 | 800 | - | 0.9033 |
| 5.0 | 830 | - | 0.9016 |
| 5.1205 | 850 | - | 0.8950 |
| 5.4217 | 900 | - | 0.9047 |
| 5.7229 | 950 | - | 0.9134 |
| 6.0 | 996 | - | 0.9048 |
| 6.0241 | 1000 | 0.1809 | 0.9092 |
| 6.3253 | 1050 | - | 0.8953 |
| 6.6265 | 1100 | - | 0.8866 |
| 6.9277 | 1150 | - | 0.9021 |
| 7.0 | 1162 | - | 0.9021 |
| 7.2289 | 1200 | - | 0.9003 |
| 7.5301 | 1250 | - | 0.8908 |
| 7.8313 | 1300 | - | 0.8979 |
| 8.0 | 1328 | - | 0.9024 |
| 8.1325 | 1350 | - | 0.9008 |
| 8.4337 | 1400 | - | 0.9061 |
| 8.7349 | 1450 | - | 0.9125 |
| 9.0 | 1494 | - | 0.9152 |
| 9.0361 | 1500 | 0.0889 | 0.9152 |
| 9.3373 | 1550 | - | 0.9097 |
| 9.6386 | 1600 | - | 0.8966 |
| 9.9398 | 1650 | - | 0.8991 |
| 10.0 | 1660 | - | 0.9014 |
| 10.2410 | 1700 | - | 0.9027 |
| 10.5422 | 1750 | - | 0.9052 |
| 10.8434 | 1800 | - | 0.8917 |
| 11.0 | 1826 | - | 0.8936 |
| 11.1446 | 1850 | - | 0.8941 |
| 11.4458 | 1900 | - | 0.9058 |
| 11.7470 | 1950 | - | 0.8983 |
| 12.0 | 1992 | - | 0.9083 |
| 12.0482 | 2000 | 0.0658 | 0.9044 |
| 12.3494 | 2050 | - | 0.9063 |
| 12.6506 | 2100 | - | 0.9047 |
| 12.9518 | 2150 | - | 0.9115 |
| 13.0 | 2158 | - | 0.9152 |
| 13.2530 | 2200 | - | 0.9111 |
| 13.5542 | 2250 | - | 0.9000 |
| 13.8554 | 2300 | - | 0.9049 |
| 14.0 | 2324 | - | 0.8991 |
| 14.1566 | 2350 | - | 0.8891 |
| 14.4578 | 2400 | - | 0.9017 |
| 14.7590 | 2450 | - | 0.9050 |
| 15.0 | 2490 | - | 0.9012 |
| 15.0602 | 2500 | 0.0517 | 0.9014 |
| 15.3614 | 2550 | - | 0.8998 |
| 15.6627 | 2600 | - | 0.8947 |
| 15.9639 | 2650 | - | 0.9002 |
| 16.0 | 2656 | - | 0.8965 |
| 16.2651 | 2700 | - | 0.9085 |
| 16.5663 | 2750 | - | 0.8940 |
| 16.8675 | 2800 | - | 0.8932 |
| 17.0 | 2822 | - | 0.9066 |
| 17.1687 | 2850 | - | 0.8960 |
| 17.4699 | 2900 | - | 0.8908 |
| 17.7711 | 2950 | - | 0.8991 |
| 18.0 | 2988 | - | 0.8983 |
| 18.0723 | 3000 | 0.0569 | 0.9005 |
| 18.3735 | 3050 | - | 0.8945 |
| 18.6747 | 3100 | - | 0.9003 |
| 18.9759 | 3150 | - | 0.8994 |
| 19.0 | 3154 | - | 0.9024 |
| 19.2771 | 3200 | - | 0.9032 |
| 19.5783 | 3250 | - | 0.8980 |
| 19.8795 | 3300 | - | 0.8989 |
| 20.0 | 3320 | - | 0.9020 |
| 20.1807 | 3350 | - | 0.9023 |
| 20.4819 | 3400 | - | 0.9033 |
| 20.7831 | 3450 | - | 0.8907 |
| 21.0 | 3486 | - | 0.9063 |
| 21.0843 | 3500 | 0.0318 | 0.9026 |
| 21.3855 | 3550 | - | 0.8989 |
| 21.6867 | 3600 | - | 0.8965 |
| 21.9880 | 3650 | - | 0.8976 |
| 22.0 | 3652 | - | 0.8976 |
| 22.2892 | 3700 | - | 0.8972 |
| 22.5904 | 3750 | - | 0.9030 |
| 22.8916 | 3800 | - | 0.8955 |
| 23.0 | 3818 | - | 0.9011 |
| 23.1928 | 3850 | - | 0.8968 |
| 23.4940 | 3900 | - | 0.8970 |
| 23.7952 | 3950 | - | 0.8978 |
| 24.0 | 3984 | - | 0.8964 |
| 24.0964 | 4000 | 0.047 | 0.8976 |
| 24.3976 | 4050 | - | 0.9005 |
| 24.6988 | 4100 | - | 0.9021 |
| 25.0 | 4150 | - | 0.8991 |
| 25.3012 | 4200 | - | 0.9021 |
| 25.6024 | 4250 | - | 0.8944 |
| 25.9036 | 4300 | - | 0.8984 |
| 26.0 | 4316 | - | 0.8995 |
| 26.2048 | 4350 | - | 0.8963 |
| 26.5060 | 4400 | - | 0.8973 |
| 26.8072 | 4450 | - | 0.9037 |
| 27.0 | 4482 | - | 0.9040 |
| 27.1084 | 4500 | 0.0325 | 0.8974 |
| 27.4096 | 4550 | - | 0.8966 |
| 27.7108 | 4600 | - | 0.8995 |
| 28.0 | 4648 | - | 0.9012 |
| 28.0120 | 4650 | - | 0.9012 |
| 28.3133 | 4700 | - | 0.9068 |
| 28.6145 | 4750 | - | 0.9069 |
| 28.9157 | 4800 | - | 0.9072 |
| 29.0 | 4814 | - | 0.9072 |
| 29.2169 | 4850 | - | 0.9069 |
| 29.5181 | 4900 | - | 0.9069 |
| 29.8193 | 4950 | - | 0.9069 |
| 30.0 | 4980 | - | 0.9069 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.1
- Tokenizers: 0.21.0
## 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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