--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:26299 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-m-v1.5 widget: - source_sentence: What are the conditions that must be met for the appointment of a depositary established in a third country for non-EU AIFs? sentences: - '(a) for EU AIFs, in the home Member State of the AIF; (b) for non-EU AIFs, in the third country where the AIF is established or in the home Member State of the AIFM managing the AIF or in the Member State of reference of the AIFM managing the AIF. 6. Without prejudice to the requirements set out in paragraph 3, the appointment of a depositary established in a third country shall, at all times, be subject to the following conditions: (a)' - '(c) the financial soundness of the proposed acquirer, in particular in relation to the type of business pursued and envisaged in the investment firm in which the acquisition is proposed; (d) whether the investment firm will be able to comply and continue to comply with the prudential requirements based on this Directive and, where applicable, other Directives, in particular Directives 2002/87/EC and 2013/36/EU, in particular, whether the group of which it will become a part has a structure that makes it possible to exercise effective supervision, effectively exchange information among the competent authorities and determine the allocation of responsibilities among the competent authorities; (e)' - '(f) the undertaking shall describe the expected decarbonisation levers and their overall quantitative contributions to achieve the GHG emission reduction targets (e.g., energy or material efficiency and consumption reduction, fuel switching, use of renewable energy , phase out or substitution of product and process). Disclosure Requirement E1-5 – Energy consumption and mix The undertaking shall provide information on its energy consumption and mix. The objective of this Disclosure Requirement is to provide an understanding of the undertaking’s total energy consumption in absolute value, improvement in energy efficiency, exposure to coal, oil and gas-related activities, and the share of renewable energy in its overall energy mix.' - source_sentence: What factors should be considered when assessing the risk of human rights being affected in a specific case? sentences: - 'of eligible new assets as referred to in the instructions corresponding to column (h) of Template 7. The denominator of the KPI shall be the gross carrying amount of new covered assets from those assets, as referred to in the instructions corresponding to column (a) of Template 7. x | Of which: specialised lending Institutions shall disclose the proportion of new assets (i.e. assets originated within the current disclosure period) categorised as specialised lending funding environmentally sustainable activities for the objective of climate change adaptation in total new eligible assets (i.e. assets originated within the current disclosure period) funding environmentally sustainable activities. New eligible assets shall be calculated net of' - the risk that such human right may be affected, taking into account the circumstances of the specific case, including the nature and extent of the company’s business operations and its chain of activities, the characteristics of the economic sector and the geographical and operational context; ---|--- (d) | ‘adverse impact’ means an adverse environmental impact or adverse human rights impact; ---|--- (e) | ‘subsidiary’ means a legal person, as defined in Article 2, point (10), of Directive 2013/34/EU, and a legal person through which the activity of a controlled undertaking, as defined in Article 2(1), point (f), of Directive 2004/109/EC of the European Parliament and of the Council (46), is exercised; ---|--- (f) | ‘business partner’ - '(f) the undertaking shall describe the expected decarbonisation levers and their overall quantitative contributions to achieve the GHG emission reduction targets (e.g., energy or material efficiency and consumption reduction, fuel switching, use of renewable energy , phase out or substitution of product and process). Disclosure Requirement E1-5 – Energy consumption and mix The undertaking shall provide information on its energy consumption and mix. The objective of this Disclosure Requirement is to provide an understanding of the undertaking’s total energy consumption in absolute value, improvement in energy efficiency, exposure to coal, oil and gas-related activities, and the share of renewable energy in its overall energy mix.' - source_sentence: Can you list the different types of fluorescent lamps referenced, including any specific categories of high intensity discharge lamps? sentences: - 'and other products or equipment for the purpose of recording or reproducing sound or images, including signals or other technologies for the distribution of sound and image than by telecommunications Photovoltaic panels 5. LIGHTING EQUIPMENT Luminaires for fluorescent lamps with the exception of luminaires in households Straight fluorescent lamps Compact fluorescent lamps High intensity discharge lamps, including pressure sodium lamps and metal halide lamps Low pressure sodium lamps Other lighting or equipment for the purpose of spreading or controlling light with the exception of filament bulbs 6. ELECTRICAL AND ELECTRONIC TOOLS (WITH THE EXCEPTION OF LARGE-SCALE STATIONARY INDUSTRIAL TOOLS) Drills Saws Sewing machines' - the principle of recovery of the costs of water use in accordance with Article 9; ---|--- 7.3. | a summary of the measures taken to meet the requirements of Article 7; ---|--- 7.4. | a summary of the controls on abstraction and impoundment of water, including reference to the registers and identifications of the cases where exemptions have been made under Article 11(3)(e); ---|--- 7.5. | a summary of the controls adopted for point source discharges and other activities with an impact on the status of water in accordance with the provisions of Article 11(3)(g) and 11(3)(i); ---|--- 7.6. | an identification of the cases where direct discharges to groundwater have been authorised in accordance with the provisions of Article 11(3)(j); ---|--- - '(158) Member States should have the right to take into account the recycling of metals separated after incineration of waste in proportion to the share of the packaging waste incinerated, provided that the recycled metals meet certain quality criteria laid down in Commission Implementing Decision (EU) 2019/1004 (41). (159) In the case of exports of packaging waste from the Union for recycling, Regulation (EC) No 1013/2006 of the European Parliament and of the Council (42) and Regulation (EU) 2024/1157 of the European Parliament and of the Council (43) apply.' - source_sentence: What are the requirements for cooperation between competent authorities in Member States regarding the supervision of financial institutions and other entities as outlined in the provided text? sentences: - 'AR 9. In Phase 3, to assesses its material risks and opportunities based on the results of Phases 1 and 2, the undertaking may consider the following categories: (a) physical risks : i. acute risks (e.g., natural disasters exacerbated by loss of coastal protection from ecosystems , leading to costs of storm damage to coastal infrastructure, disease or pests affecting the species or variety of crop the undertaking relies on, especially in the case of no or low genetic diversity, species loss and ecosystem degradation ); and ii.' - necessary to demonstrate the conformity of packaging in one or more languages which can be easily understood by that authority; ---|--- (d) | upon a request from a competent national authority, make available relevant documents within 10 days of the receipt of such a request; ---|--- (e) | terminate the mandate if the manufacturer acts contrary to its obligations under this Regulation. ---|--- - 'Each Member State shall require that such cooperation also take place between the competent authorities for the purposes of this Directive or of Regulation (EU) No 600/2014 and the competent authorities responsible in that Member State for the supervision of credit and other financial institutions, pension funds, UCITS, insurance and reinsurance intermediaries and insurance undertakings. Member States shall require that competent authorities exchange any information which is essential or relevant to the exercise of their functions and duties. Article 69 Supervisory powers 1.' - source_sentence: What types of substances or mixtures should be listed in relation to their potential to react and create hazardous situations, and what additional information is required to manage the associated risks? sentences: - 'The undertaking shall specify as part of the contextual information, whether the targets that it has set and presented are mandatory (required by legislation) or voluntary. Disclosure Requirement E2-4 – Pollution of air, water and soil The undertaking shall disclose the pollutants that it emits through its own operations, as well as the microplastics it generates or uses. The objective of this Disclosure Requirement is to provide an understanding of the emissions that the undertaking generates to air, water and soil in its own operations, and of its generation and use of microplastics. The undertaking shall disclose the amounts of: (a)' - 'Families of substances or mixtures or specific substances, such as water, air, acids, bases, oxidising agents, with which the substance or mixture could react to produce a hazardous situation (like an explosion, a release of toxic or flammable materials, or a liberation of excessive heat), shall be listed and if appropriate a brief description of measures to be taken to manage risks associated with such hazards shall be given. 10.6. Hazardous decomposition products Known and reasonably anticipated hazardous decomposition products produced as a result of use, storage, spill and heating shall be listed. Hazardous combustion products shall be included in section 5 of the safety data sheet. 11. SECTION 11: Toxicological information' - '4. In order for a district heating and cooling system to qualify as efficient, Member States shall ensure that where it is built or its supply units are substantially refurbished, the district heating or cooling system meet the criteria set out in paragraph 1 or 2 applicable at the time when it starts or continues its operation after the refurbishment. In addition, Member States shall ensure that when a district heating and cooling system is built or its supply units are substantially refurbished:' 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-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7467552067612436 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8988831874434048 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9305765167521883 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9604587986718985 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7467552067612436 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29962772914780156 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1861153033504377 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09604587986718985 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7467552067612436 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8988831874434048 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9305765167521883 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9604587986718985 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8608067216595782 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8280703960827729 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.829841633884875 name: Cosine Map@100 - type: cosine_accuracy@1 value: 0.7530938726230003 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9079384243887715 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9402354361605796 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9680048294597042 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7530938726230003 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30264614146292385 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1880470872321159 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09680048294597042 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7530938726230003 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9079384243887715 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9402354361605796 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9680048294597042 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8682167825620759 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.835408970913047 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8367990198438501 name: Cosine Map@100 - type: cosine_accuracy@1 value: 0.8397223060670087 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.955629338967703 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9746453365529731 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9897373981285844 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8397223060670087 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3185431129892343 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19492906731059462 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09897373981285845 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8397223060670087 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.955629338967703 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9746453365529731 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9897373981285844 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9225924304434711 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.900205659283534 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9007567489649001 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 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 types of substances or mixtures should be listed in relation to their potential to react and create hazardous situations, and what additional information is required to manage the associated risks?', 'Families of substances or mixtures or specific substances, such as water, air, acids, bases, oxidising agents, with which the substance or mixture could react to produce a hazardous situation (like an explosion, a release of toxic or flammable materials, or a liberation of excessive heat), shall be listed and if appropriate a brief description of measures to be taken to manage risks associated with such hazards shall be given.\n\n10.6. Hazardous decomposition products\n\nKnown and reasonably anticipated hazardous decomposition products produced as a result of use, storage, spill and heating shall be listed. Hazardous combustion products shall be included in section 5 of the safety data sheet.\n\n11. SECTION 11: Toxicological information', 'The undertaking shall specify as part of the contextual information, whether the targets that it has set and presented are mandatory (required by legislation) or voluntary.\n\nDisclosure Requirement E2-4 – Pollution of air, water and soil\n\nThe undertaking shall disclose the pollutants that it emits through its own operations, as well as the microplastics it generates or uses.\n\nThe objective of this Disclosure Requirement is to provide an understanding of the emissions that the undertaking generates to air, water and soil in its own operations, and of its generation and use of microplastics.\n\nThe undertaking shall disclose the amounts of:\n\n(a)', ] 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7468 | | cosine_accuracy@3 | 0.8989 | | cosine_accuracy@5 | 0.9306 | | cosine_accuracy@10 | 0.9605 | | cosine_precision@1 | 0.7468 | | cosine_precision@3 | 0.2996 | | cosine_precision@5 | 0.1861 | | cosine_precision@10 | 0.096 | | cosine_recall@1 | 0.7468 | | cosine_recall@3 | 0.8989 | | cosine_recall@5 | 0.9306 | | cosine_recall@10 | 0.9605 | | **cosine_ndcg@10** | **0.8608** | | cosine_mrr@10 | 0.8281 | | cosine_map@100 | 0.8298 | #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7531 | | cosine_accuracy@3 | 0.9079 | | cosine_accuracy@5 | 0.9402 | | cosine_accuracy@10 | 0.968 | | cosine_precision@1 | 0.7531 | | cosine_precision@3 | 0.3026 | | cosine_precision@5 | 0.188 | | cosine_precision@10 | 0.0968 | | cosine_recall@1 | 0.7531 | | cosine_recall@3 | 0.9079 | | cosine_recall@5 | 0.9402 | | cosine_recall@10 | 0.968 | | **cosine_ndcg@10** | **0.8682** | | cosine_mrr@10 | 0.8354 | | cosine_map@100 | 0.8368 | #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8397 | | cosine_accuracy@3 | 0.9556 | | cosine_accuracy@5 | 0.9746 | | cosine_accuracy@10 | 0.9897 | | cosine_precision@1 | 0.8397 | | cosine_precision@3 | 0.3185 | | cosine_precision@5 | 0.1949 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.8397 | | cosine_recall@3 | 0.9556 | | cosine_recall@5 | 0.9746 | | cosine_recall@10 | 0.9897 | | **cosine_ndcg@10** | **0.9226** | | cosine_mrr@10 | 0.9002 | | cosine_map@100 | 0.9008 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 26,299 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What are the key considerations the Commission must evaluate when assessing the feasibility of including municipal waste incineration installations in the EU ETS by 31 July 2026? | By 31 July 2026, the Commission shall present a report to the European Parliament and to the Council in which it shall assess the feasibility of including municipal waste incineration installations in the EU ETS, including with a view to their inclusion from 2028 and with an assessment of the potential need for an option for a Member State to opt out until 31 December 2030. In that regard, the Commission shall take into account the importance of all sectors contributing to emission reductions and potential diversion of waste towards disposal by landfilling in the Union and waste exports to third countries. The Commission shall in addition take into account relevant criteria such as the effects on the internal market, potential distortions | | What are the conditions under which a registrant can withhold certain information from disclosure, and what steps must they take to justify this decision? | NOTES

Note 1: If it is not technically possible, or if it does not appear scientifically necessary to give information, the reasons shall be clearly stated, in accordance with the relevant provisions.

Note 2: The registrant may wish to declare that certain information submitted in the registration dossier is commercially sensitive and its disclosure might harm him commercially. If this is the case, he shall list the items and provide a justification.

▼C1

INFORMATION REFERRED TO IN ARTICLE 10(a) (i) TO (v)

1. GENERAL REGISTRANT INFORMATION

1.1. Registrant

▼M70

1.1.1. Name, address, telephone number and email address

▼C1

1.1.2. Contact person

1.1.3. Location of the registrant's production and own use site(s), as appropriate

▼M70 | | What are the specific color indices and chemical identifiers for Pigment Red 112 and Pigment Yellow 14, and what is their respective concentration percentage? | 17 (PR17)/CI 12390 229-681-4 6655-84-1 0,1 % Pigment Red 112 (PR112)/CI 12370 229-440-3 6535-46-2 0,1 % Pigment Yellow 14 (PY14)/CI 21095 226-789-3 5468-75-7 0,1 % Pigment Yellow 55 (PY55)/CI 21096 226-789-3 6358-37-8 0,1 % Pigment Red 2 (PR2)/CI 12310 227-930-1 6041-94-7 0,1 % Pigment Red 22 (PR22)/CI 12315 229-245-3 6448-95-9 0,1 % Pigment Red 146 (PR146)/CI 12485 226-103-2 5280-68-2 0,1 % Pigment Red 269 (PR269)/CI 12466 268-028-8 67990-05-0 0,1 % Pigment Orange16 (PO16)/CI 21160 229-388-1 6505-28-8 0,1 % Pigment Yellow 1 (PY1)/CI 11680 219-730-8 2512-29-0 0,1 % Pigment Yellow 12 (PY12)/CI 21090 228-787-8 6358-85-6 0,1 % Pigment Yellow 87 (PY87)/CI 21107:1 239-160-3 15110-84-6, 14110-84-6 0,1 % Pigment Yellow 97 (PY97)/CI 11767 | * Loss: [MatryoshkaLoss](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`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `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`: 4 - `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
### Training Logs
Click to expand | Epoch | Step | Training Loss | cosine_ndcg@10 | |:------:|:-----:|:-------------:|:--------------:| | 0.0228 | 100 | - | 0.6723 | | 0.0456 | 200 | - | 0.7870 | | 0.0684 | 300 | - | 0.8397 | | 0.0912 | 400 | - | 0.8608 | | 0.1141 | 500 | 0.4135 | - | | 0.0228 | 100 | - | 0.8669 | | 0.0456 | 200 | - | 0.8682 | | 0.0228 | 100 | - | 0.8699 | | 0.0456 | 200 | - | 0.8733 | | 0.0684 | 300 | - | 0.8759 | | 0.0912 | 400 | - | 0.8802 | | 0.1141 | 500 | 0.1122 | 0.8823 | | 0.1369 | 600 | - | 0.8847 | | 0.1597 | 700 | - | 0.8835 | | 0.1825 | 800 | - | 0.8862 | | 0.2053 | 900 | - | 0.8864 | | 0.2281 | 1000 | 0.1299 | 0.8860 | | 0.2509 | 1100 | - | 0.8837 | | 0.2737 | 1200 | - | 0.8861 | | 0.2965 | 1300 | - | 0.8882 | | 0.3193 | 1400 | - | 0.8850 | | 0.3422 | 1500 | 0.123 | 0.8916 | | 0.3650 | 1600 | - | 0.8866 | | 0.3878 | 1700 | - | 0.8917 | | 0.4106 | 1800 | - | 0.8918 | | 0.4334 | 1900 | - | 0.8904 | | 0.4562 | 2000 | 0.0769 | 0.8896 | | 0.4790 | 2100 | - | 0.8876 | | 0.5018 | 2200 | - | 0.8956 | | 0.5246 | 2300 | - | 0.8964 | | 0.5474 | 2400 | - | 0.8901 | | 0.5703 | 2500 | 0.0697 | 0.8888 | | 0.5931 | 2600 | - | 0.8872 | | 0.6159 | 2700 | - | 0.8839 | | 0.6387 | 2800 | - | 0.8891 | | 0.6615 | 2900 | - | 0.8890 | | 0.6843 | 3000 | 0.0537 | 0.8867 | | 0.7071 | 3100 | - | 0.8907 | | 0.7299 | 3200 | - | 0.8916 | | 0.7527 | 3300 | - | 0.8933 | | 0.7755 | 3400 | - | 0.8933 | | 0.7984 | 3500 | 0.0772 | 0.8924 | | 0.8212 | 3600 | - | 0.8946 | | 0.8440 | 3700 | - | 0.8953 | | 0.8668 | 3800 | - | 0.8941 | | 0.8896 | 3900 | - | 0.8939 | | 0.9124 | 4000 | 0.065 | 0.8953 | | 0.9352 | 4100 | - | 0.8969 | | 0.9580 | 4200 | - | 0.8993 | | 0.9808 | 4300 | - | 0.9020 | | 1.0 | 4384 | - | 0.9040 | | 1.0036 | 4400 | - | 0.9044 | | 1.0265 | 4500 | 0.0329 | 0.9015 | | 1.0493 | 4600 | - | 0.8999 | | 1.0721 | 4700 | - | 0.9005 | | 1.0949 | 4800 | - | 0.8976 | | 1.1177 | 4900 | - | 0.9001 | | 1.1405 | 5000 | 0.024 | 0.9014 | | 1.1633 | 5100 | - | 0.8995 | | 1.1861 | 5200 | - | 0.9022 | | 1.2089 | 5300 | - | 0.9030 | | 1.2318 | 5400 | - | 0.9027 | | 1.2546 | 5500 | 0.016 | 0.9024 | | 1.2774 | 5600 | - | 0.9012 | | 1.3002 | 5700 | - | 0.9011 | | 1.3230 | 5800 | - | 0.9049 | | 1.3458 | 5900 | - | 0.9094 | | 1.3686 | 6000 | 0.0553 | 0.9094 | | 1.3914 | 6100 | - | 0.9028 | | 1.4142 | 6200 | - | 0.9113 | | 1.4370 | 6300 | - | 0.9118 | | 1.4599 | 6400 | - | 0.9139 | | 1.4827 | 6500 | 0.0416 | 0.9112 | | 1.5055 | 6600 | - | 0.9102 | | 1.5283 | 6700 | - | 0.9092 | | 1.5511 | 6800 | - | 0.9098 | | 1.5739 | 6900 | - | 0.9101 | | 1.5967 | 7000 | 0.0283 | 0.9107 | | 1.6195 | 7100 | - | 0.9114 | | 1.6423 | 7200 | - | 0.9131 | | 1.6651 | 7300 | - | 0.9130 | | 1.6880 | 7400 | - | 0.9144 | | 1.7108 | 7500 | 0.0268 | 0.9126 | | 1.7336 | 7600 | - | 0.9119 | | 1.7564 | 7700 | - | 0.9125 | | 1.7792 | 7800 | - | 0.9111 | | 1.8020 | 7900 | - | 0.9100 | | 1.8248 | 8000 | 0.0252 | 0.9110 | | 1.8476 | 8100 | - | 0.9151 | | 1.8704 | 8200 | - | 0.9123 | | 1.8932 | 8300 | - | 0.9118 | | 1.9161 | 8400 | - | 0.9103 | | 1.9389 | 8500 | 0.0288 | 0.9110 | | 1.9617 | 8600 | - | 0.9106 | | 1.9845 | 8700 | - | 0.9109 | | 2.0 | 8768 | - | 0.9126 | | 2.0073 | 8800 | - | 0.9117 | | 2.0301 | 8900 | - | 0.9114 | | 2.0529 | 9000 | 0.0232 | 0.9123 | | 2.0757 | 9100 | - | 0.9113 | | 2.0985 | 9200 | - | 0.9095 | | 2.1214 | 9300 | - | 0.9086 | | 2.1442 | 9400 | - | 0.9109 | | 2.1670 | 9500 | 0.0188 | 0.9124 | | 2.1898 | 9600 | - | 0.9125 | | 2.2126 | 9700 | - | 0.9121 | | 2.2354 | 9800 | - | 0.9122 | | 2.2582 | 9900 | - | 0.9132 | | 2.2810 | 10000 | 0.0182 | 0.9125 | | 2.3038 | 10100 | - | 0.9142 | | 2.3266 | 10200 | - | 0.9135 | | 2.3495 | 10300 | - | 0.9084 | | 2.3723 | 10400 | - | 0.9147 | | 2.3951 | 10500 | 0.0111 | 0.9170 | | 2.4179 | 10600 | - | 0.9142 | | 2.4407 | 10700 | - | 0.9158 | | 2.4635 | 10800 | - | 0.9174 | | 2.4863 | 10900 | - | 0.9176 | | 2.5091 | 11000 | 0.0153 | 0.9166 | | 2.5319 | 11100 | - | 0.9172 | | 2.5547 | 11200 | - | 0.9171 | | 2.5776 | 11300 | - | 0.9168 | | 2.6004 | 11400 | - | 0.9176 | | 2.6232 | 11500 | 0.0241 | 0.9170 | | 2.6460 | 11600 | - | 0.9177 | | 2.6688 | 11700 | - | 0.9184 | | 2.6916 | 11800 | - | 0.9196 | | 2.7144 | 11900 | - | 0.9211 | | 2.7372 | 12000 | 0.0172 | 0.9209 | | 2.7600 | 12100 | - | 0.9212 | | 2.7828 | 12200 | - | 0.9201 | | 2.8057 | 12300 | - | 0.9194 | | 2.8285 | 12400 | - | 0.9205 | | 2.8513 | 12500 | 0.013 | 0.9202 | | 2.8741 | 12600 | - | 0.9213 | | 2.8969 | 12700 | - | 0.9210 | | 2.9197 | 12800 | - | 0.9203 | | 2.9425 | 12900 | - | 0.9200 | | 2.9653 | 13000 | 0.03 | 0.9209 | | 2.9881 | 13100 | - | 0.9212 | | 3.0 | 13152 | - | 0.9200 | | 3.0109 | 13200 | - | 0.9198 | | 3.0338 | 13300 | - | 0.9192 | | 3.0566 | 13400 | - | 0.9183 | | 3.0794 | 13500 | 0.0133 | 0.9170 | | 3.1022 | 13600 | - | 0.9181 | | 3.125 | 13700 | - | 0.9180 | | 3.1478 | 13800 | - | 0.9176 | | 3.1706 | 13900 | - | 0.9168 | | 3.1934 | 14000 | 0.0185 | 0.9175 | | 3.2162 | 14100 | - | 0.9188 | | 3.2391 | 14200 | - | 0.9182 | | 3.2619 | 14300 | - | 0.9192 | | 3.2847 | 14400 | - | 0.9199 | | 3.3075 | 14500 | 0.0135 | 0.9195 | | 3.3303 | 14600 | - | 0.9190 | | 3.3531 | 14700 | - | 0.9187 | | 3.3759 | 14800 | - | 0.9196 | | 3.3987 | 14900 | - | 0.9202 | | 3.4215 | 15000 | 0.0157 | 0.9214 | | 3.4443 | 15100 | - | 0.9211 | | 3.4672 | 15200 | - | 0.9211 | | 3.4900 | 15300 | - | 0.9208 | | 3.5128 | 15400 | - | 0.9195 | | 3.5356 | 15500 | 0.015 | 0.9207 | | 3.5584 | 15600 | - | 0.9210 | | 3.5812 | 15700 | - | 0.9226 |
### Framework Versions - Python: 3.10.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.1 - PyTorch: 2.4.0+cu121 - Accelerate: 1.4.0 - Datasets: 3.3.2 - 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} } ```