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1_Pooling/config.json

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+{
+  "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
+}

README.md

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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-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) <!-- 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("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]
+```
+
+<!--
+### 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   | 0.666      |
+| 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     |
+
+<!--
+## 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: 11 tokens</li><li>mean: 35.24 tokens</li><li>max: 206 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 193.39 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How is materiality defined in the context of an entity's sustainability reporting as per QC 4?</code>                                                        | <code>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).<br><br>Faithful representation<br><br>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.</code>                                                                                                                                                                                   |
+  | <code>What procedure must be followed for the adoption of implementing acts as mentioned in the text?</code>                                                       | <code>Those implementing acts shall be adopted in accordance with the examination procedure referred to in Article 22a(2).<br><br>3.<br><br>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.</code>                                                                                                                                                                                                                                                                                                                                                                                                             |
+  | <code>How should monitoring points be distributed for groundwater bodies that flow across Member State boundaries to effectively estimate groundwater flow?</code> | <code>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:<br><br>— 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,<br><br>— 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.<br><br>2.2.3. Monitoring frequency</code> |
+* Loss: [<code>MatryoshkaLoss</code>](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
+<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   | 0.938         | -              |
+| 0.1726 | 1000  | 0.2188        | -              |
+| 0.2589 | 1500  | 0.1998        | -              |
+| 0.3452 | 2000  | 0.2162        | 0.7843         |
+| 0.4316 | 2500  | 0.1921        | -              |
+| 0.5179 | 3000  | 0.1749        | -              |
+| 0.6042 | 3500  | 0.1741        | -              |
+| 0.6905 | 4000  | 0.2007        | 0.7779         |
+| 0.7768 | 4500  | 0.1456        | -              |
+| 0.8631 | 5000  | 0.1034        | -              |
+| 0.9494 | 5500  | 0.1285        | -              |
+| 1.0    | 5793  | -             | 0.7806         |
+| 1.0357 | 6000  | 0.1011        | 0.7879         |
+| 1.1220 | 6500  | 0.065         | -              |
+| 1.2084 | 7000  | 0.0754        | -              |
+| 1.2947 | 7500  | 0.067         | -              |
+| 1.3810 | 8000  | 0.059         | 0.7953         |
+| 1.4673 | 8500  | 0.0644        | -              |
+| 1.5536 | 9000  | 0.0705        | -              |
+| 1.6399 | 9500  | 0.0425        | -              |
+| 1.7262 | 10000 | 0.0515        | 0.8171         |
+| 1.8125 | 10500 | 0.0358        | -              |
+| 1.8988 | 11000 | 0.0515        | -              |
+| 1.9852 | 11500 | 0.043         | -              |
+| 2.0    | 11586 | -             | 0.8201         |
+| 2.0715 | 12000 | 0.0257        | 0.8208         |
+| 2.1578 | 12500 | 0.0343        | -              |
+| 2.2441 | 13000 | 0.0307        | -              |
+| 2.3304 | 13500 | 0.0324        | -              |
+| 2.4167 | 14000 | 0.0225        | 0.8236         |
+| 2.5030 | 14500 | 0.0362        | -              |
+| 2.5893 | 15000 | 0.0255        | -              |
+| 2.6756 | 15500 | 0.0203        | -              |
+| 2.7620 | 16000 | 0.0244        | 0.8240         |
+| 2.8483 | 16500 | 0.0461        | -              |
+| 2.9346 | 17000 | 0.0226        | -              |
+| 3.0    | 17379 | -             | 0.8278         |
+
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-l",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/Information-Retrieval_evaluation_results.csv

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+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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+2.0,11586,0.659243915069912,0.871569135163128,0.9214569307785259,0.9615052649749698,0.659243915069912,0.659243915069912,0.290523045054376,0.871569135163128,0.18429138615570514,0.9214569307785259,0.09615052649749696,0.9615052649749698,0.7736913598513812,0.8201268727574789,0.7756028742712455
+3.0,17379,0.6659761781460383,0.8841705506645952,0.9312963921974797,0.9672017952701536,0.6659761781460383,0.6659761781460383,0.29472351688819837,0.8841705506645952,0.1862592784394959,0.9312963921974797,0.09672017952701535,0.9672017952701536,0.7818480980055302,0.8278291318026204,0.783515504381956

model.safetensors

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+oid sha256:c4714f59abe642537ff6370e30d2d97e5807b3222c2ac015a42456e4eeb7f6b4
+size 1336413848

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}