IoannisKat1/multilingual-e5-large-ft-new

multilingual_e5_large Finetuned on Data

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large. 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: intfloat/multilingual-e5-large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'In what situations are restrictions imposed for the prevention of threats?',
    'Restrictions concerning specific principles and the rights of information, access to and rectification or erasure of personal data, the right to data portability, the right to object, decisions based on profiling, as well as the communication of a personal data breach to a data subject and certain related obligations of the controllers may be imposed by Union or Member State law, as far as necessary and proportionate in a democratic society to safeguard public security, including the protection of human life especially in response to natural or manmade disasters, the prevention, investigation and prosecution of criminal offences or the execution of criminal penalties, including the safeguarding against and the prevention of threats to public security, or of breaches of ethics for regulated professions, other important objectives of general public interest of the Union or of a Member State, in particular an important economic or financial interest of the Union or of a Member State, the keeping of public registers kept for reasons of general public interest, further processing of archived personal data to provide specific information related to the political behaviour under former totalitarian state regimes or the protection of the data subject or the rights and freedoms of others, including social protection, public health and humanitarian purposes. Those restrictions should be in accordance with the requirements set out in the Charter and in the European Convention for the Protection of Human Rights and Fundamental Freedoms.',
    "The data subject should have the right not to be subject to a decision, which may include a measure, evaluating personal aspects relating to him or her which is based solely on automated processing and which produces legal effects concerning him or her or similarly significantly affects him or her, such as automatic refusal of an online credit application or e-recruiting practices without any human intervention. Such processing includes ‘profiling’ that consists of any form of automated processing of personal data evaluating the personal aspects relating to a natural person, in particular to analyse or predict aspects concerning the data subject's performance at work, economic situation, health, personal preferences or interests, reliability or behaviour, location or movements, where it produces legal effects concerning him or her or similarly significantly affects him or her. However, decision-making based on such processing, including profiling, should be allowed where expressly authorised by Union or Member State law to which the controller is subject, including for fraud and tax-evasion monitoring and prevention purposes conducted in accordance with the regulations, standards and recommendations of Union institutions or national oversight bodies and to ensure the security and reliability of a service provided by the controller, or necessary for the entering or performance of a contract between the data subject and a controller, or when the data subject has given his or her explicit consent. In any case, such processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision. Such measure should not concern a child. In order to ensure fair and transparent processing in respect of the data subject, taking into account the specific circumstances and context in which the personal data are processed, the controller should use appropriate mathematical or statistical procedures for the profiling, implement technical and organisational measures appropriate to ensure, in particular, that factors which result in inaccuracies in personal data are corrected and the risk of errors is minimised, secure personal data in a manner that takes account of the potential risks involved for the interests and rights of the data subject and that prevents, inter alia, discriminatory effects on natural persons on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status or sexual orientation, or that result in measures having such an effect. Automated decision-making and profiling based on special categories of personal data should be allowed only under specific conditions.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5753, 0.0974],
#         [0.5753, 1.0000, 0.2109],
#         [0.0974, 0.2109, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: dim_1024
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 1024
  }
  

Metric	Value
cosine_accuracy@1	0.4963
cosine_accuracy@3	0.5332
cosine_accuracy@5	0.5749
cosine_accuracy@10	0.6167
cosine_precision@1	0.4963
cosine_precision@3	0.4816
cosine_precision@5	0.4595
cosine_precision@10	0.4086
cosine_recall@1	0.0882
cosine_recall@3	0.2296
cosine_recall@5	0.3155
cosine_recall@10	0.4461
cosine_ndcg@10	0.5529
cosine_mrr@10	0.5231
cosine_map@100	0.5976

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.5037
cosine_accuracy@3	0.5381
cosine_accuracy@5	0.5749
cosine_accuracy@10	0.6216
cosine_precision@1	0.5037
cosine_precision@3	0.4881
cosine_precision@5	0.4634
cosine_precision@10	0.4145
cosine_recall@1	0.0883
cosine_recall@3	0.2296
cosine_recall@5	0.3143
cosine_recall@10	0.4458
cosine_ndcg@10	0.5576
cosine_mrr@10	0.5293
cosine_map@100	0.5996

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.4988
cosine_accuracy@3	0.5307
cosine_accuracy@5	0.57
cosine_accuracy@10	0.6143
cosine_precision@1	0.4988
cosine_precision@3	0.4824
cosine_precision@5	0.46
cosine_precision@10	0.4098
cosine_recall@1	0.0879
cosine_recall@3	0.2273
cosine_recall@5	0.3142
cosine_recall@10	0.4446
cosine_ndcg@10	0.5521
cosine_mrr@10	0.5239
cosine_map@100	0.5948

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.484
cosine_accuracy@3	0.5086
cosine_accuracy@5	0.5504
cosine_accuracy@10	0.5995
cosine_precision@1	0.484
cosine_precision@3	0.4652
cosine_precision@5	0.4457
cosine_precision@10	0.4025
cosine_recall@1	0.0832
cosine_recall@3	0.2115
cosine_recall@5	0.2936
cosine_recall@10	0.4266
cosine_ndcg@10	0.5352
cosine_mrr@10	0.5074
cosine_map@100	0.5804

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.4472
cosine_accuracy@3	0.4742
cosine_accuracy@5	0.5209
cosine_accuracy@10	0.57
cosine_precision@1	0.4472
cosine_precision@3	0.4292
cosine_precision@5	0.4113
cosine_precision@10	0.3754
cosine_recall@1	0.0803
cosine_recall@3	0.2022
cosine_recall@5	0.2793
cosine_recall@10	0.4092
cosine_ndcg@10	0.5041
cosine_mrr@10	0.4728
cosine_map@100	0.552

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.4324
cosine_accuracy@3	0.457
cosine_accuracy@5	0.5061
cosine_accuracy@10	0.5651
cosine_precision@1	0.4324
cosine_precision@3	0.4193
cosine_precision@5	0.4029
cosine_precision@10	0.372
cosine_recall@1	0.0727
cosine_recall@3	0.1871
cosine_recall@5	0.2616
cosine_recall@10	0.3928
cosine_ndcg@10	0.4886
cosine_mrr@10	0.4589
cosine_map@100	0.5318

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,627 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 17.25 tokens max: 39 tokens	min: 27 tokens mean: 384.26 tokens max: 512 tokens

• Samples:

  anchor	positive
  According to the text, what Article is the basis for authorisations by a Member State or supervisory authority to remain valid?	1.In the absence of a decision pursuant to Article 45(3), a controller or processor may transfer personal data to a third country or an international organisation only if the controller or processor has provided appropriate safeguards, and on condition that enforceable data subject rights and effective legal remedies for data subjects are available. 2.The appropriate safeguards referred to in paragraph 1 may be provided for, without requiring any specific authorisation from a supervisory authority, by: (a) a legally binding and enforceable instrument between public authorities or bodies; (b) binding corporate rules in accordance with Article 47; (c) standard data protection clauses adopted by the Commission in accordance with the examination procedure referred to in Article 93(2); (d) standard data protection clauses adopted by a supervisory authority and approved by the Commission pursuant to the examination procedure referred to in Article 93(2); (e) an approved code of conduct ...
  For what purposes can the processing of personal data for direct marketing purposes be regarded?	The legitimate interests of a controller, including those of a controller to which the personal data may be disclosed, or of a third party, may provide a legal basis for processing, provided that the interests or the fundamental rights and freedoms of the data subject are not overriding, taking into consideration the reasonable expectations of data subjects based on their relationship with the controller. Such legitimate interest could exist for example where there is a relevant and appropriate relationship between the data subject and the controller in situations such as where the data subject is a client or in the service of the controller. At any rate the existence of a legitimate interest would need careful assessment including whether a data subject can reasonably expect at the time and in the context of the collection of the personal data that processing for that purpose may take place. The interests and fundamental rights of the data subject could in particular override the inte...
  What types of processing operations may be considered high risk?	Directive 95/46/EC provided for a general obligation to notify the processing of personal data to the supervisory authorities. While that obligation produces administrative and financial burdens, it did not in all cases contribute to improving the protection of personal data. Such indiscriminate general notification obligations should therefore be abolished, and replaced by effective procedures and mechanisms which focus instead on those types of processing operations which are likely to result in a high risk to the rights and freedoms of natural persons by virtue of their nature, scope, context and purposes. Such types of processing operations may be those which in, particular, involve using new technologies, or are of a new kind and where no data protection impact assessment has been carried out before by the controller, or where they become necessary in the light of the time that has elapsed since the initial processing.

• Loss: MatryoshkaLoss with these parameters:

  {
      "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: epoch
• gradient_accumulation_steps: 2
• learning_rate: 2e-05
• num_train_epochs: 10
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• 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: 2
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 10
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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: True
• 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}
• tp_size: 0
• 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_fused
• 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
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.0098	1	15.324	-	-	-	-	-	-
0.0196	2	17.0147	-	-	-	-	-	-
0.0294	3	14.8225	-	-	-	-	-	-
0.0392	4	13.8462	-	-	-	-	-	-
0.0490	5	16.2999	-	-	-	-	-	-
0.0588	6	17.4877	-	-	-	-	-	-
0.0686	7	17.8605	-	-	-	-	-	-
0.0784	8	15.6806	-	-	-	-	-	-
0.0882	9	13.2464	-	-	-	-	-	-
0.0980	10	15.3883	-	-	-	-	-	-
0.1078	11	13.7659	-	-	-	-	-	-
0.1176	12	13.4036	-	-	-	-	-	-
0.1275	13	14.9356	-	-	-	-	-	-
0.1373	14	16.1803	-	-	-	-	-	-
0.1471	15	16.3122	-	-	-	-	-	-
0.1569	16	15.4618	-	-	-	-	-	-
0.1667	17	15.5073	-	-	-	-	-	-
0.1765	18	11.6437	-	-	-	-	-	-
0.1863	19	12.2171	-	-	-	-	-	-
0.1961	20	14.9219	-	-	-	-	-	-
0.2059	21	14.5069	-	-	-	-	-	-
0.2157	22	15.0117	-	-	-	-	-	-
0.2255	23	14.6466	-	-	-	-	-	-
0.2353	24	13.3104	-	-	-	-	-	-
0.2451	25	12.5101	-	-	-	-	-	-
0.2549	26	11.3922	-	-	-	-	-	-
0.2647	27	11.772	-	-	-	-	-	-
0.2745	28	11.8186	-	-	-	-	-	-
0.2843	29	11.6001	-	-	-	-	-	-
0.2941	30	9.9045	-	-	-	-	-	-
0.3039	31	13.2828	-	-	-	-	-	-
0.3137	32	12.6591	-	-	-	-	-	-
0.3235	33	12.3677	-	-	-	-	-	-
0.3333	34	8.0986	-	-	-	-	-	-
0.3431	35	10.4323	-	-	-	-	-	-
0.3529	36	11.9792	-	-	-	-	-	-
0.3627	37	9.8071	-	-	-	-	-	-
0.3725	38	11.7875	-	-	-	-	-	-
0.3824	39	10.6545	-	-	-	-	-	-
0.3922	40	8.9779	-	-	-	-	-	-
0.4020	41	12.0673	-	-	-	-	-	-
0.4118	42	13.7131	-	-	-	-	-	-
0.4216	43	8.3107	-	-	-	-	-	-
0.4314	44	7.9659	-	-	-	-	-	-
0.4412	45	9.0921	-	-	-	-	-	-
0.4510	46	9.7038	-	-	-	-	-	-
0.4608	47	10.4994	-	-	-	-	-	-
0.4706	48	12.5112	-	-	-	-	-	-
0.4804	49	8.3138	-	-	-	-	-	-
0.4902	50	6.2068	-	-	-	-	-	-
0.5	51	9.1321	-	-	-	-	-	-
0.5098	52	8.7041	-	-	-	-	-	-
0.5196	53	7.4798	-	-	-	-	-	-
0.5294	54	9.5009	-	-	-	-	-	-
0.5392	55	9.628	-	-	-	-	-	-
0.5490	56	16.058	-	-	-	-	-	-
0.5588	57	11.9913	-	-	-	-	-	-
0.5686	58	11.1787	-	-	-	-	-	-
0.5784	59	7.6791	-	-	-	-	-	-
0.5882	60	6.8413	-	-	-	-	-	-
0.5980	61	4.0237	-	-	-	-	-	-
0.6078	62	7.5481	-	-	-	-	-	-
0.6176	63	6.2388	-	-	-	-	-	-
0.6275	64	6.7651	-	-	-	-	-	-
0.6373	65	7.0816	-	-	-	-	-	-
0.6471	66	8.7256	-	-	-	-	-	-
0.6569	67	6.9846	-	-	-	-	-	-
0.6667	68	4.045	-	-	-	-	-	-
0.6765	69	9.9729	-	-	-	-	-	-
0.6863	70	9.2729	-	-	-	-	-	-
0.6961	71	6.4106	-	-	-	-	-	-
0.7059	72	10.5278	-	-	-	-	-	-
0.7157	73	8.4437	-	-	-	-	-	-
0.7255	74	7.8812	-	-	-	-	-	-
0.7353	75	7.5065	-	-	-	-	-	-
0.7451	76	10.6546	-	-	-	-	-	-
0.7549	77	7.9648	-	-	-	-	-	-
0.7647	78	5.0529	-	-	-	-	-	-
0.7745	79	10.1484	-	-	-	-	-	-
0.7843	80	9.2155	-	-	-	-	-	-
0.7941	81	6.6863	-	-	-	-	-	-
0.8039	82	7.5756	-	-	-	-	-	-
0.8137	83	7.6175	-	-	-	-	-	-
0.8235	84	7.3882	-	-	-	-	-	-
0.8333	85	6.3436	-	-	-	-	-	-
0.8431	86	10.3158	-	-	-	-	-	-
0.8529	87	3.3064	-	-	-	-	-	-
0.8627	88	7.6493	-	-	-	-	-	-
0.8725	89	4.8372	-	-	-	-	-	-
0.8824	90	5.256	-	-	-	-	-	-
0.8922	91	9.2291	-	-	-	-	-	-
0.9020	92	6.9306	-	-	-	-	-	-
0.9118	93	4.5077	-	-	-	-	-	-
0.9216	94	5.7115	-	-	-	-	-	-
0.9314	95	5.5939	-	-	-	-	-	-
0.9412	96	8.0183	-	-	-	-	-	-
0.9510	97	6.2882	-	-	-	-	-	-
0.9608	98	12.8056	-	-	-	-	-	-
0.9706	99	9.9956	-	-	-	-	-	-
0.9804	100	7.4971	-	-	-	-	-	-
0.9902	101	10.13	-	-	-	-	-	-
1.0	102	3.2411	0.4211	0.4325	0.4349	0.4208	0.4012	0.3538
1.0098	103	5.3225	-	-	-	-	-	-
1.0196	104	4.9438	-	-	-	-	-	-
1.0294	105	7.9672	-	-	-	-	-	-
1.0392	106	5.7196	-	-	-	-	-	-
1.0490	107	3.5121	-	-	-	-	-	-
1.0588	108	3.3512	-	-	-	-	-	-
1.0686	109	6.7686	-	-	-	-	-	-
1.0784	110	7.7489	-	-	-	-	-	-
1.0882	111	2.7172	-	-	-	-	-	-
1.0980	112	4.3175	-	-	-	-	-	-
1.1078	113	8.4022	-	-	-	-	-	-
1.1176	114	5.1214	-	-	-	-	-	-
1.1275	115	7.8083	-	-	-	-	-	-
1.1373	116	3.657	-	-	-	-	-	-
1.1471	117	9.7885	-	-	-	-	-	-
1.1569	118	7.1842	-	-	-	-	-	-
1.1667	119	3.904	-	-	-	-	-	-
1.1765	120	4.5248	-	-	-	-	-	-
1.1863	121	4.0514	-	-	-	-	-	-
1.1961	122	7.3121	-	-	-	-	-	-
1.2059	123	7.195	-	-	-	-	-	-
1.2157	124	4.2942	-	-	-	-	-	-
1.2255	125	7.4596	-	-	-	-	-	-
1.2353	126	5.2864	-	-	-	-	-	-
1.2451	127	1.1805	-	-	-	-	-	-
1.2549	128	2.9964	-	-	-	-	-	-
1.2647	129	6.7119	-	-	-	-	-	-
1.2745	130	5.7116	-	-	-	-	-	-
1.2843	131	5.7917	-	-	-	-	-	-
1.2941	132	6.4809	-	-	-	-	-	-
1.3039	133	8.5369	-	-	-	-	-	-
1.3137	134	5.5397	-	-	-	-	-	-
1.3235	135	5.5748	-	-	-	-	-	-
1.3333	136	5.435	-	-	-	-	-	-
1.3431	137	4.3399	-	-	-	-	-	-
1.3529	138	12.5114	-	-	-	-	-	-
1.3627	139	5.0801	-	-	-	-	-	-
1.3725	140	3.1477	-	-	-	-	-	-
1.3824	141	6.1467	-	-	-	-	-	-
1.3922	142	4.1269	-	-	-	-	-	-
1.4020	143	9.0184	-	-	-	-	-	-
1.4118	144	1.5786	-	-	-	-	-	-
1.4216	145	4.143	-	-	-	-	-	-
1.4314	146	6.3611	-	-	-	-	-	-
1.4412	147	4.9894	-	-	-	-	-	-
1.4510	148	6.227	-	-	-	-	-	-
1.4608	149	1.9417	-	-	-	-	-	-
1.4706	150	5.8058	-	-	-	-	-	-
1.4804	151	5.3881	-	-	-	-	-	-
1.4902	152	8.852	-	-	-	-	-	-
1.5	153	8.7416	-	-	-	-	-	-
1.5098	154	4.1655	-	-	-	-	-	-
1.5196	155	3.7727	-	-	-	-	-	-
1.5294	156	8.1223	-	-	-	-	-	-
1.5392	157	4.1344	-	-	-	-	-	-
1.5490	158	8.3959	-	-	-	-	-	-
1.5588	159	3.1913	-	-	-	-	-	-
1.5686	160	5.2804	-	-	-	-	-	-
1.5784	161	4.0358	-	-	-	-	-	-
1.5882	162	8.2456	-	-	-	-	-	-
1.5980	163	4.315	-	-	-	-	-	-
1.6078	164	7.5692	-	-	-	-	-	-
1.6176	165	1.8122	-	-	-	-	-	-
1.6275	166	4.7641	-	-	-	-	-	-
1.6373	167	3.7781	-	-	-	-	-	-
1.6471	168	1.7589	-	-	-	-	-	-
1.6569	169	11.5894	-	-	-	-	-	-
1.6667	170	2.8734	-	-	-	-	-	-
1.6765	171	3.6949	-	-	-	-	-	-
1.6863	172	2.5401	-	-	-	-	-	-
1.6961	173	6.0829	-	-	-	-	-	-
1.7059	174	7.1026	-	-	-	-	-	-
1.7157	175	6.5876	-	-	-	-	-	-
1.7255	176	3.7488	-	-	-	-	-	-
1.7353	177	7.7088	-	-	-	-	-	-
1.7451	178	3.2405	-	-	-	-	-	-
1.7549	179	2.1942	-	-	-	-	-	-
1.7647	180	7.0455	-	-	-	-	-	-
1.7745	181	2.2278	-	-	-	-	-	-
1.7843	182	2.4134	-	-	-	-	-	-
1.7941	183	6.6062	-	-	-	-	-	-
1.8039	184	6.1013	-	-	-	-	-	-
1.8137	185	4.5338	-	-	-	-	-	-
1.8235	186	3.0266	-	-	-	-	-	-
1.8333	187	7.3554	-	-	-	-	-	-
1.8431	188	3.9763	-	-	-	-	-	-
1.8529	189	2.7192	-	-	-	-	-	-
1.8627	190	3.9852	-	-	-	-	-	-
1.8725	191	9.0477	-	-	-	-	-	-
1.8824	192	8.3791	-	-	-	-	-	-
1.8922	193	5.4102	-	-	-	-	-	-
1.9020	194	8.0572	-	-	-	-	-	-
1.9118	195	5.6405	-	-	-	-	-	-
1.9216	196	4.3343	-	-	-	-	-	-
1.9314	197	5.4153	-	-	-	-	-	-
1.9412	198	4.6588	-	-	-	-	-	-
1.9510	199	8.9407	-	-	-	-	-	-
1.9608	200	8.0808	-	-	-	-	-	-
1.9706	201	4.5329	-	-	-	-	-	-
1.9804	202	4.1046	-	-	-	-	-	-
1.9902	203	6.2329	-	-	-	-	-	-
2.0	204	2.1011	0.4713	0.4656	0.4712	0.4576	0.4470	0.3994
2.0098	205	2.9678	-	-	-	-	-	-
2.0196	206	4.2794	-	-	-	-	-	-
2.0294	207	8.0298	-	-	-	-	-	-
2.0392	208	5.9295	-	-	-	-	-	-
2.0490	209	6.5868	-	-	-	-	-	-
2.0588	210	2.7326	-	-	-	-	-	-
2.0686	211	4.1257	-	-	-	-	-	-
2.0784	212	3.9128	-	-	-	-	-	-
2.0882	213	3.5703	-	-	-	-	-	-
2.0980	214	1.5844	-	-	-	-	-	-
2.1078	215	3.0082	-	-	-	-	-	-
2.1176	216	1.4631	-	-	-	-	-	-
2.1275	217	1.43	-	-	-	-	-	-
2.1373	218	2.6126	-	-	-	-	-	-
2.1471	219	1.3341	-	-	-	-	-	-
2.1569	220	0.6117	-	-	-	-	-	-
2.1667	221	0.5586	-	-	-	-	-	-
2.1765	222	4.7394	-	-	-	-	-	-
2.1863	223	1.6508	-	-	-	-	-	-
2.1961	224	3.1046	-	-	-	-	-	-
2.2059	225	2.4088	-	-	-	-	-	-
2.2157	226	2.2547	-	-	-	-	-	-
2.2255	227	8.064	-	-	-	-	-	-
2.2353	228	7.0364	-	-	-	-	-	-
2.2451	229	3.3294	-	-	-	-	-	-
2.2549	230	4.6495	-	-	-	-	-	-
2.2647	231	1.2139	-	-	-	-	-	-
2.2745	232	5.4169	-	-	-	-	-	-
2.2843	233	5.2787	-	-	-	-	-	-
2.2941	234	3.6312	-	-	-	-	-	-
2.3039	235	2.3246	-	-	-	-	-	-
2.3137	236	2.8142	-	-	-	-	-	-
2.3235	237	3.7731	-	-	-	-	-	-
2.3333	238	1.6033	-	-	-	-	-	-
2.3431	239	1.2218	-	-	-	-	-	-
2.3529	240	7.9152	-	-	-	-	-	-
2.3627	241	4.5164	-	-	-	-	-	-
2.3725	242	5.2189	-	-	-	-	-	-
2.3824	243	4.8904	-	-	-	-	-	-
2.3922	244	1.9568	-	-	-	-	-	-
2.4020	245	1.973	-	-	-	-	-	-
2.4118	246	12.3473	-	-	-	-	-	-
2.4216	247	1.9683	-	-	-	-	-	-
2.4314	248	6.7897	-	-	-	-	-	-
2.4412	249	2.0394	-	-	-	-	-	-
2.4510	250	4.1793	-	-	-	-	-	-
2.4608	251	3.7605	-	-	-	-	-	-
2.4706	252	3.2396	-	-	-	-	-	-
2.4804	253	4.9677	-	-	-	-	-	-
2.4902	254	3.2917	-	-	-	-	-	-
2.5	255	3.6739	-	-	-	-	-	-
2.5098	256	4.0818	-	-	-	-	-	-
2.5196	257	2.997	-	-	-	-	-	-
2.5294	258	5.2039	-	-	-	-	-	-
2.5392	259	2.886	-	-	-	-	-	-
2.5490	260	2.4548	-	-	-	-	-	-
2.5588	261	1.7319	-	-	-	-	-	-
2.5686	262	2.6275	-	-	-	-	-	-
2.5784	263	7.2434	-	-	-	-	-	-
2.5882	264	4.7648	-	-	-	-	-	-
2.5980	265	3.6364	-	-	-	-	-	-
2.6078	266	3.7914	-	-	-	-	-	-
2.6176	267	5.0426	-	-	-	-	-	-
2.6275	268	2.2009	-	-	-	-	-	-
2.6373	269	9.0089	-	-	-	-	-	-
2.6471	270	3.8599	-	-	-	-	-	-
2.6569	271	6.5447	-	-	-	-	-	-
2.6667	272	3.0264	-	-	-	-	-	-
2.6765	273	4.9369	-	-	-	-	-	-
2.6863	274	2.3111	-	-	-	-	-	-
2.6961	275	3.1818	-	-	-	-	-	-
2.7059	276	2.8315	-	-	-	-	-	-
2.7157	277	1.2825	-	-	-	-	-	-
2.7255	278	5.6546	-	-	-	-	-	-
2.7353	279	1.3959	-	-	-	-	-	-
2.7451	280	3.6887	-	-	-	-	-	-
2.7549	281	2.4156	-	-	-	-	-	-
2.7647	282	2.326	-	-	-	-	-	-
2.7745	283	2.1192	-	-	-	-	-	-
2.7843	284	6.8289	-	-	-	-	-	-
2.7941	285	4.0186	-	-	-	-	-	-
2.8039	286	7.0156	-	-	-	-	-	-
2.8137	287	1.9199	-	-	-	-	-	-
2.8235	288	1.6497	-	-	-	-	-	-
2.8333	289	1.1444	-	-	-	-	-	-
2.8431	290	2.7633	-	-	-	-	-	-
2.8529	291	3.2981	-	-	-	-	-	-
2.8627	292	3.9193	-	-	-	-	-	-
2.8725	293	3.8872	-	-	-	-	-	-
2.8824	294	4.3814	-	-	-	-	-	-
2.8922	295	3.5998	-	-	-	-	-	-
2.9020	296	4.9401	-	-	-	-	-	-
2.9118	297	2.7544	-	-	-	-	-	-
2.9216	298	3.0925	-	-	-	-	-	-
2.9314	299	1.9969	-	-	-	-	-	-
2.9412	300	2.9074	-	-	-	-	-	-
2.9510	301	3.322	-	-	-	-	-	-
2.9608	302	7.719	-	-	-	-	-	-
2.9706	303	2.2305	-	-	-	-	-	-
2.9804	304	2.4653	-	-	-	-	-	-
2.9902	305	2.6506	-	-	-	-	-	-
3.0	306	0.8072	0.5359	0.5182	0.5196	0.5022	0.4773	0.4093
3.0098	307	1.451	-	-	-	-	-	-
3.0196	308	0.5015	-	-	-	-	-	-
3.0294	309	1.7901	-	-	-	-	-	-
3.0392	310	1.3272	-	-	-	-	-	-
3.0490	311	2.3769	-	-	-	-	-	-
3.0588	312	2.5458	-	-	-	-	-	-
3.0686	313	3.0403	-	-	-	-	-	-
3.0784	314	3.5089	-	-	-	-	-	-
3.0882	315	2.8959	-	-	-	-	-	-
3.0980	316	2.4988	-	-	-	-	-	-
3.1078	317	2.66	-	-	-	-	-	-
3.1176	318	1.276	-	-	-	-	-	-
3.1275	319	5.7555	-	-	-	-	-	-
3.1373	320	2.1283	-	-	-	-	-	-
3.1471	321	0.5059	-	-	-	-	-	-
3.1569	322	1.0991	-	-	-	-	-	-
3.1667	323	1.6624	-	-	-	-	-	-
3.1765	324	0.4748	-	-	-	-	-	-
3.1863	325	2.3259	-	-	-	-	-	-
3.1961	326	1.2119	-	-	-	-	-	-
3.2059	327	2.9754	-	-	-	-	-	-
3.2157	328	1.3821	-	-	-	-	-	-
3.2255	329	0.5291	-	-	-	-	-	-
3.2353	330	0.8716	-	-	-	-	-	-
3.2451	331	0.3839	-	-	-	-	-	-
3.2549	332	4.9776	-	-	-	-	-	-
3.2647	333	0.1569	-	-	-	-	-	-
3.2745	334	2.5764	-	-	-	-	-	-
3.2843	335	4.1433	-	-	-	-	-	-
3.2941	336	3.1134	-	-	-	-	-	-
3.3039	337	4.1822	-	-	-	-	-	-
3.3137	338	3.2529	-	-	-	-	-	-
3.3235	339	1.0987	-	-	-	-	-	-
3.3333	340	6.1991	-	-	-	-	-	-
3.3431	341	3.094	-	-	-	-	-	-
3.3529	342	0.7698	-	-	-	-	-	-
3.3627	343	1.2345	-	-	-	-	-	-
3.3725	344	1.9209	-	-	-	-	-	-
3.3824	345	5.9982	-	-	-	-	-	-
3.3922	346	4.39	-	-	-	-	-	-
3.4020	347	2.8024	-	-	-	-	-	-
3.4118	348	5.9221	-	-	-	-	-	-
3.4216	349	5.6582	-	-	-	-	-	-
3.4314	350	0.5768	-	-	-	-	-	-
3.4412	351	2.8013	-	-	-	-	-	-
3.4510	352	1.5867	-	-	-	-	-	-
3.4608	353	1.9682	-	-	-	-	-	-
3.4706	354	1.5387	-	-	-	-	-	-
3.4804	355	3.6408	-	-	-	-	-	-
3.4902	356	1.4683	-	-	-	-	-	-
3.5	357	3.6072	-	-	-	-	-	-
3.5098	358	4.7459	-	-	-	-	-	-
3.5196	359	5.3846	-	-	-	-	-	-
3.5294	360	1.6786	-	-	-	-	-	-
3.5392	361	7.1925	-	-	-	-	-	-
3.5490	362	1.6855	-	-	-	-	-	-
3.5588	363	2.1178	-	-	-	-	-	-
3.5686	364	3.2173	-	-	-	-	-	-
3.5784	365	2.4904	-	-	-	-	-	-
3.5882	366	0.7341	-	-	-	-	-	-
3.5980	367	1.6829	-	-	-	-	-	-
3.6078	368	2.9732	-	-	-	-	-	-
3.6176	369	2.2218	-	-	-	-	-	-
3.6275	370	3.5776	-	-	-	-	-	-
3.6373	371	3.1861	-	-	-	-	-	-
3.6471	372	3.4713	-	-	-	-	-	-
3.6569	373	3.5881	-	-	-	-	-	-
3.6667	374	2.0863	-	-	-	-	-	-
3.6765	375	1.8114	-	-	-	-	-	-
3.6863	376	6.5803	-	-	-	-	-	-
3.6961	377	6.3244	-	-	-	-	-	-
3.7059	378	1.4123	-	-	-	-	-	-
3.7157	379	6.1084	-	-	-	-	-	-
3.7255	380	1.0318	-	-	-	-	-	-
3.7353	381	2.3362	-	-	-	-	-	-
3.7451	382	5.2456	-	-	-	-	-	-
3.7549	383	2.9123	-	-	-	-	-	-
3.7647	384	0.894	-	-	-	-	-	-
3.7745	385	0.4678	-	-	-	-	-	-
3.7843	386	1.2149	-	-	-	-	-	-
3.7941	387	5.9688	-	-	-	-	-	-
3.8039	388	4.1268	-	-	-	-	-	-
3.8137	389	5.912	-	-	-	-	-	-
3.8235	390	0.7395	-	-	-	-	-	-
3.8333	391	2.4926	-	-	-	-	-	-
3.8431	392	0.1932	-	-	-	-	-	-
3.8529	393	2.1133	-	-	-	-	-	-
3.8627	394	0.6252	-	-	-	-	-	-
3.8725	395	1.0917	-	-	-	-	-	-
3.8824	396	0.8632	-	-	-	-	-	-
3.8922	397	1.2754	-	-	-	-	-	-
3.9020	398	2.7626	-	-	-	-	-	-
3.9118	399	3.4405	-	-	-	-	-	-
3.9216	400	2.8913	-	-	-	-	-	-
3.9314	401	1.7128	-	-	-	-	-	-
3.9412	402	7.7663	-	-	-	-	-	-
3.9510	403	2.0075	-	-	-	-	-	-
3.9608	404	4.7753	-	-	-	-	-	-
3.9706	405	3.6381	-	-	-	-	-	-
3.9804	406	1.8823	-	-	-	-	-	-
3.9902	407	2.4635	-	-	-	-	-	-
4.0	408	0.1052	0.5362	0.5427	0.5429	0.5083	0.4882	0.4309
4.0098	409	0.3451	-	-	-	-	-	-
4.0196	410	1.6322	-	-	-	-	-	-
4.0294	411	1.0172	-	-	-	-	-	-
4.0392	412	0.3349	-	-	-	-	-	-
4.0490	413	3.2899	-	-	-	-	-	-
4.0588	414	2.4181	-	-	-	-	-	-
4.0686	415	2.087	-	-	-	-	-	-
4.0784	416	1.6162	-	-	-	-	-	-
4.0882	417	0.6417	-	-	-	-	-	-
4.0980	418	0.3975	-	-	-	-	-	-
4.1078	419	1.8162	-	-	-	-	-	-
4.1176	420	0.9521	-	-	-	-	-	-
4.1275	421	1.7726	-	-	-	-	-	-
4.1373	422	4.1018	-	-	-	-	-	-
4.1471	423	0.4848	-	-	-	-	-	-
4.1569	424	0.3066	-	-	-	-	-	-
4.1667	425	1.3922	-	-	-	-	-	-
4.1765	426	1.3455	-	-	-	-	-	-
4.1863	427	2.5684	-	-	-	-	-	-
4.1961	428	2.7308	-	-	-	-	-	-
4.2059	429	6.1926	-	-	-	-	-	-
4.2157	430	0.4893	-	-	-	-	-	-
4.2255	431	0.8947	-	-	-	-	-	-
4.2353	432	3.9226	-	-	-	-	-	-
4.2451	433	0.4447	-	-	-	-	-	-
4.2549	434	1.5509	-	-	-	-	-	-
4.2647	435	0.1494	-	-	-	-	-	-
4.2745	436	0.6653	-	-	-	-	-	-
4.2843	437	1.2474	-	-	-	-	-	-
4.2941	438	1.003	-	-	-	-	-	-
4.3039	439	2.0581	-	-	-	-	-	-
4.3137	440	1.621	-	-	-	-	-	-
4.3235	441	0.354	-	-	-	-	-	-
4.3333	442	2.6752	-	-	-	-	-	-
4.3431	443	1.7156	-	-	-	-	-	-
4.3529	444	1.0886	-	-	-	-	-	-
4.3627	445	1.3605	-	-	-	-	-	-
4.3725	446	2.3739	-	-	-	-	-	-
4.3824	447	1.1467	-	-	-	-	-	-
4.3922	448	4.2469	-	-	-	-	-	-
4.4020	449	0.426	-	-	-	-	-	-
4.4118	450	0.697	-	-	-	-	-	-
4.4216	451	0.4556	-	-	-	-	-	-
4.4314	452	4.3441	-	-	-	-	-	-
4.4412	453	0.1128	-	-	-	-	-	-
4.4510	454	0.4467	-	-	-	-	-	-
4.4608	455	1.1093	-	-	-	-	-	-
4.4706	456	6.4229	-	-	-	-	-	-
4.4804	457	1.907	-	-	-	-	-	-
4.4902	458	2.1577	-	-	-	-	-	-
4.5	459	0.2015	-	-	-	-	-	-
4.5098	460	0.2782	-	-	-	-	-	-
4.5196	461	4.7503	-	-	-	-	-	-
4.5294	462	2.8296	-	-	-	-	-	-
4.5392	463	1.0601	-	-	-	-	-	-
4.5490	464	2.0974	-	-	-	-	-	-
4.5588	465	0.6219	-	-	-	-	-	-
4.5686	466	1.8882	-	-	-	-	-	-
4.5784	467	3.3879	-	-	-	-	-	-
4.5882	468	0.1579	-	-	-	-	-	-
4.5980	469	0.3244	-	-	-	-	-	-
4.6078	470	1.2288	-	-	-	-	-	-
4.6176	471	1.1257	-	-	-	-	-	-
4.6275	472	2.1462	-	-	-	-	-	-
4.6373	473	1.5341	-	-	-	-	-	-
4.6471	474	1.8499	-	-	-	-	-	-
4.6569	475	0.9148	-	-	-	-	-	-
4.6667	476	5.8209	-	-	-	-	-	-
4.6765	477	3.0442	-	-	-	-	-	-
4.6863	478	2.2646	-	-	-	-	-	-
4.6961	479	0.7703	-	-	-	-	-	-
4.7059	480	0.095	-	-	-	-	-	-
4.7157	481	3.467	-	-	-	-	-	-
4.7255	482	1.917	-	-	-	-	-	-
4.7353	483	1.854	-	-	-	-	-	-
4.7451	484	4.401	-	-	-	-	-	-
4.7549	485	0.8289	-	-	-	-	-	-
4.7647	486	1.4017	-	-	-	-	-	-
4.7745	487	3.4552	-	-	-	-	-	-
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4.8039	490	3.3605	-	-	-	-	-	-
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4.8333	493	1.2999	-	-	-	-	-	-
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4.9020	500	1.0877	-	-	-	-	-	-
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4.9216	502	1.6975	-	-	-	-	-	-
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4.9510	505	2.5208	-	-	-	-	-	-
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4.9706	507	1.9674	-	-	-	-	-	-
4.9804	508	2.0502	-	-	-	-	-	-
4.9902	509	3.2448	-	-	-	-	-	-
5.0	510	0.9598	0.5398	0.5410	0.5305	0.5149	0.4735	0.4376
5.0098	511	1.1768	-	-	-	-	-	-
5.0196	512	1.9281	-	-	-	-	-	-
5.0294	513	2.1877	-	-	-	-	-	-
5.0392	514	0.1436	-	-	-	-	-	-
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5.0588	516	0.7393	-	-	-	-	-	-
5.0686	517	0.1758	-	-	-	-	-	-
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5.1078	521	0.8052	-	-	-	-	-	-
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5.1471	525	1.6597	-	-	-	-	-	-
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5.1667	527	1.0337	-	-	-	-	-	-
5.1765	528	1.3722	-	-	-	-	-	-
5.1863	529	4.702	-	-	-	-	-	-
5.1961	530	1.9939	-	-	-	-	-	-
5.2059	531	1.8056	-	-	-	-	-	-
5.2157	532	0.84	-	-	-	-	-	-
5.2255	533	1.3357	-	-	-	-	-	-
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5.2451	535	1.9759	-	-	-	-	-	-
5.2549	536	3.5364	-	-	-	-	-	-
5.2647	537	2.4263	-	-	-	-	-	-
5.2745	538	0.9878	-	-	-	-	-	-
5.2843	539	0.6477	-	-	-	-	-	-
5.2941	540	0.3962	-	-	-	-	-	-
5.3039	541	1.6336	-	-	-	-	-	-
5.3137	542	0.5501	-	-	-	-	-	-
5.3235	543	0.3524	-	-	-	-	-	-
5.3333	544	2.2072	-	-	-	-	-	-
5.3431	545	0.3129	-	-	-	-	-	-
5.3529	546	0.2931	-	-	-	-	-	-
5.3627	547	3.0659	-	-	-	-	-	-
5.3725	548	4.3941	-	-	-	-	-	-
5.3824	549	0.9272	-	-	-	-	-	-
5.3922	550	0.3424	-	-	-	-	-	-
5.4020	551	0.3234	-	-	-	-	-	-
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5.4216	553	0.0854	-	-	-	-	-	-
5.4314	554	0.225	-	-	-	-	-	-
5.4412	555	2.3928	-	-	-	-	-	-
5.4510	556	0.1915	-	-	-	-	-	-
5.4608	557	0.8306	-	-	-	-	-	-
5.4706	558	0.3044	-	-	-	-	-	-
5.4804	559	2.031	-	-	-	-	-	-
5.4902	560	0.094	-	-	-	-	-	-
5.5	561	1.7682	-	-	-	-	-	-
5.5098	562	0.1988	-	-	-	-	-	-
5.5196	563	3.5462	-	-	-	-	-	-
5.5294	564	2.431	-	-	-	-	-	-
5.5392	565	0.6328	-	-	-	-	-	-
5.5490	566	1.3962	-	-	-	-	-	-
5.5588	567	2.0889	-	-	-	-	-	-
5.5686	568	0.5087	-	-	-	-	-	-
5.5784	569	1.6965	-	-	-	-	-	-
5.5882	570	0.4892	-	-	-	-	-	-
5.5980	571	0.7345	-	-	-	-	-	-
5.6078	572	1.6501	-	-	-	-	-	-
5.6176	573	0.8206	-	-	-	-	-	-
5.6275	574	0.1313	-	-	-	-	-	-
5.6373	575	0.7477	-	-	-	-	-	-
5.6471	576	0.3	-	-	-	-	-	-
5.6569	577	1.4371	-	-	-	-	-	-
5.6667	578	1.0097	-	-	-	-	-	-
5.6765	579	1.154	-	-	-	-	-	-
5.6863	580	2.3609	-	-	-	-	-	-
5.6961	581	1.4063	-	-	-	-	-	-
5.7059	582	1.2086	-	-	-	-	-	-
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5.7353	585	1.0212	-	-	-	-	-	-
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5.7549	587	1.7079	-	-	-	-	-	-
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5.9020	602	1.7777	-	-	-	-	-	-
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5.9510	607	1.0022	-	-	-	-	-	-
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5.9706	609	1.0927	-	-	-	-	-	-
5.9804	610	1.466	-	-	-	-	-	-
5.9902	611	1.6886	-	-	-	-	-	-
6.0	612	0.0075	0.5460	0.5573	0.5504	0.5377	0.5031	0.4622
6.0098	613	2.9338	-	-	-	-	-	-
6.0196	614	0.4003	-	-	-	-	-	-
6.0294	615	0.7203	-	-	-	-	-	-
6.0392	616	0.0941	-	-	-	-	-	-
6.0490	617	0.5725	-	-	-	-	-	-
6.0588	618	1.827	-	-	-	-	-	-
6.0686	619	1.7858	-	-	-	-	-	-
6.0784	620	0.1243	-	-	-	-	-	-
6.0882	621	3.4995	-	-	-	-	-	-
6.0980	622	2.243	-	-	-	-	-	-
6.1078	623	2.3023	-	-	-	-	-	-
6.1176	624	0.1504	-	-	-	-	-	-
6.1275	625	0.2739	-	-	-	-	-	-
6.1373	626	0.5756	-	-	-	-	-	-
6.1471	627	0.5515	-	-	-	-	-	-
6.1569	628	0.4772	-	-	-	-	-	-
6.1667	629	0.1287	-	-	-	-	-	-
6.1765	630	0.5132	-	-	-	-	-	-
6.1863	631	1.6294	-	-	-	-	-	-
6.1961	632	3.0269	-	-	-	-	-	-
6.2059	633	0.1832	-	-	-	-	-	-
6.2157	634	0.1013	-	-	-	-	-	-
6.2255	635	2.2236	-	-	-	-	-	-
6.2353	636	0.084	-	-	-	-	-	-
6.2451	637	0.4747	-	-	-	-	-	-
6.2549	638	0.4776	-	-	-	-	-	-
6.2647	639	0.3226	-	-	-	-	-	-
6.2745	640	1.3842	-	-	-	-	-	-
6.2843	641	0.1958	-	-	-	-	-	-
6.2941	642	2.6577	-	-	-	-	-	-
6.3039	643	1.8295	-	-	-	-	-	-
6.3137	644	0.6483	-	-	-	-	-	-
6.3235	645	0.671	-	-	-	-	-	-
6.3333	646	1.4637	-	-	-	-	-	-
6.3431	647	3.4489	-	-	-	-	-	-
6.3529	648	0.4837	-	-	-	-	-	-
6.3627	649	0.0927	-	-	-	-	-	-
6.3725	650	0.2907	-	-	-	-	-	-
6.3824	651	1.884	-	-	-	-	-	-
6.3922	652	3.2645	-	-	-	-	-	-
6.4020	653	0.7554	-	-	-	-	-	-
6.4118	654	1.1387	-	-	-	-	-	-
6.4216	655	0.2648	-	-	-	-	-	-
6.4314	656	0.2713	-	-	-	-	-	-
6.4412	657	0.9713	-	-	-	-	-	-
6.4510	658	2.1971	-	-	-	-	-	-
6.4608	659	0.687	-	-	-	-	-	-
6.4706	660	3.149	-	-	-	-	-	-
6.4804	661	0.7407	-	-	-	-	-	-
6.4902	662	0.3483	-	-	-	-	-	-
6.5	663	0.4235	-	-	-	-	-	-
6.5098	664	2.3466	-	-	-	-	-	-
6.5196	665	0.108	-	-	-	-	-	-
6.5294	666	0.1696	-	-	-	-	-	-
6.5392	667	1.1348	-	-	-	-	-	-
6.5490	668	0.1783	-	-	-	-	-	-
6.5588	669	0.0717	-	-	-	-	-	-
6.5686	670	0.2698	-	-	-	-	-	-
6.5784	671	0.7624	-	-	-	-	-	-
6.5882	672	1.9315	-	-	-	-	-	-
6.5980	673	1.4153	-	-	-	-	-	-
6.6078	674	0.0972	-	-	-	-	-	-
6.6176	675	0.6259	-	-	-	-	-	-
6.6275	676	0.1532	-	-	-	-	-	-
6.6373	677	1.5359	-	-	-	-	-	-
6.6471	678	3.6835	-	-	-	-	-	-
6.6569	679	1.4413	-	-	-	-	-	-
6.6667	680	0.1316	-	-	-	-	-	-
6.6765	681	0.98	-	-	-	-	-	-
6.6863	682	4.545	-	-	-	-	-	-
6.6961	683	4.13	-	-	-	-	-	-
6.7059	684	5.5336	-	-	-	-	-	-
6.7157	685	0.4967	-	-	-	-	-	-
6.7255	686	6.8419	-	-	-	-	-	-
6.7353	687	2.141	-	-	-	-	-	-
6.7451	688	0.4577	-	-	-	-	-	-
6.7549	689	1.3861	-	-	-	-	-	-
6.7647	690	0.202	-	-	-	-	-	-
6.7745	691	0.3602	-	-	-	-	-	-
6.7843	692	4.746	-	-	-	-	-	-
6.7941	693	0.3741	-	-	-	-	-	-
6.8039	694	0.7028	-	-	-	-	-	-
6.8137	695	0.2445	-	-	-	-	-	-
6.8235	696	4.9585	-	-	-	-	-	-
6.8333	697	1.8145	-	-	-	-	-	-
6.8431	698	0.5992	-	-	-	-	-	-
6.8529	699	0.3326	-	-	-	-	-	-
6.8627	700	4.4071	-	-	-	-	-	-
6.8725	701	1.2975	-	-	-	-	-	-
6.8824	702	0.0838	-	-	-	-	-	-
6.8922	703	0.6391	-	-	-	-	-	-
6.9020	704	0.4043	-	-	-	-	-	-
6.9118	705	3.0587	-	-	-	-	-	-
6.9216	706	3.1316	-	-	-	-	-	-
6.9314	707	0.2356	-	-	-	-	-	-
6.9412	708	0.7771	-	-	-	-	-	-
6.9510	709	2.7437	-	-	-	-	-	-
6.9608	710	0.3518	-	-	-	-	-	-
6.9706	711	1.7482	-	-	-	-	-	-
6.9804	712	3.4652	-	-	-	-	-	-
6.9902	713	1.0678	-	-	-	-	-	-
7.0	714	0.0745	0.5512	0.5564	0.5439	0.5374	0.5045	0.4774
7.0098	715	0.4542	-	-	-	-	-	-
7.0196	716	1.7377	-	-	-	-	-	-
7.0294	717	0.7405	-	-	-	-	-	-
7.0392	718	0.2791	-	-	-	-	-	-
7.0490	719	0.655	-	-	-	-	-	-
7.0588	720	0.6784	-	-	-	-	-	-
7.0686	721	2.269	-	-	-	-	-	-
7.0784	722	1.7147	-	-	-	-	-	-
7.0882	723	1.2439	-	-	-	-	-	-
7.0980	724	0.6956	-	-	-	-	-	-
7.1078	725	1.5403	-	-	-	-	-	-
7.1176	726	0.8351	-	-	-	-	-	-
7.1275	727	0.4354	-	-	-	-	-	-
7.1373	728	0.0799	-	-	-	-	-	-
7.1471	729	0.2014	-	-	-	-	-	-
7.1569	730	0.0683	-	-	-	-	-	-
7.1667	731	0.2685	-	-	-	-	-	-
7.1765	732	0.5861	-	-	-	-	-	-
7.1863	733	0.2006	-	-	-	-	-	-
7.1961	734	0.4644	-	-	-	-	-	-
7.2059	735	0.4079	-	-	-	-	-	-
7.2157	736	2.1752	-	-	-	-	-	-
7.2255	737	0.6598	-	-	-	-	-	-
7.2353	738	0.3968	-	-	-	-	-	-
7.2451	739	2.0319	-	-	-	-	-	-
7.2549	740	1.414	-	-	-	-	-	-
7.2647	741	0.972	-	-	-	-	-	-
7.2745	742	0.8023	-	-	-	-	-	-
7.2843	743	0.0865	-	-	-	-	-	-
7.2941	744	0.5504	-	-	-	-	-	-
7.3039	745	0.1786	-	-	-	-	-	-
7.3137	746	0.0577	-	-	-	-	-	-
7.3235	747	0.2733	-	-	-	-	-	-
7.3333	748	0.1569	-	-	-	-	-	-
7.3431	749	0.8364	-	-	-	-	-	-
7.3529	750	0.4199	-	-	-	-	-	-
7.3627	751	0.1372	-	-	-	-	-	-
7.3725	752	0.5961	-	-	-	-	-	-
7.3824	753	0.5606	-	-	-	-	-	-
7.3922	754	0.3691	-	-	-	-	-	-
7.4020	755	0.2112	-	-	-	-	-	-
7.4118	756	0.1481	-	-	-	-	-	-
7.4216	757	0.4519	-	-	-	-	-	-
7.4314	758	0.0891	-	-	-	-	-	-
7.4412	759	0.6373	-	-	-	-	-	-
7.4510	760	0.2603	-	-	-	-	-	-
7.4608	761	0.1376	-	-	-	-	-	-
7.4706	762	0.8313	-	-	-	-	-	-
7.4804	763	0.6816	-	-	-	-	-	-
7.4902	764	0.2357	-	-	-	-	-	-
7.5	765	1.0941	-	-	-	-	-	-
7.5098	766	0.1432	-	-	-	-	-	-
7.5196	767	1.3971	-	-	-	-	-	-
7.5294	768	0.085	-	-	-	-	-	-
7.5392	769	0.1028	-	-	-	-	-	-
7.5490	770	1.3943	-	-	-	-	-	-
7.5588	771	0.3034	-	-	-	-	-	-
7.5686	772	0.1066	-	-	-	-	-	-
7.5784	773	0.3826	-	-	-	-	-	-
7.5882	774	2.9294	-	-	-	-	-	-
7.5980	775	0.3147	-	-	-	-	-	-
7.6078	776	0.2727	-	-	-	-	-	-
7.6176	777	0.8388	-	-	-	-	-	-
7.6275	778	0.7989	-	-	-	-	-	-
7.6373	779	0.6952	-	-	-	-	-	-
7.6471	780	0.3426	-	-	-	-	-	-
7.6569	781	0.2356	-	-	-	-	-	-
7.6667	782	1.9497	-	-	-	-	-	-
7.6765	783	0.1705	-	-	-	-	-	-
7.6863	784	0.0647	-	-	-	-	-	-
7.6961	785	1.5847	-	-	-	-	-	-
7.7059	786	1.5402	-	-	-	-	-	-
7.7157	787	1.4899	-	-	-	-	-	-
7.7255	788	0.2314	-	-	-	-	-	-
7.7353	789	0.2176	-	-	-	-	-	-
7.7451	790	0.213	-	-	-	-	-	-
7.7549	791	0.1569	-	-	-	-	-	-
7.7647	792	1.2804	-	-	-	-	-	-
7.7745	793	0.5637	-	-	-	-	-	-
7.7843	794	1.8881	-	-	-	-	-	-
7.7941	795	0.2125	-	-	-	-	-	-
7.8039	796	2.1347	-	-	-	-	-	-
7.8137	797	1.0753	-	-	-	-	-	-
7.8235	798	0.7448	-	-	-	-	-	-
7.8333	799	0.4868	-	-	-	-	-	-
7.8431	800	0.4079	-	-	-	-	-	-
7.8529	801	0.4587	-	-	-	-	-	-
7.8627	802	1.2236	-	-	-	-	-	-
7.8725	803	0.71	-	-	-	-	-	-
7.8824	804	0.6339	-	-	-	-	-	-
7.8922	805	0.103	-	-	-	-	-	-
7.9020	806	1.4083	-	-	-	-	-	-
7.9118	807	0.3121	-	-	-	-	-	-
7.9216	808	1.7572	-	-	-	-	-	-
7.9314	809	0.8201	-	-	-	-	-	-
7.9412	810	1.0738	-	-	-	-	-	-
7.9510	811	4.2591	-	-	-	-	-	-
7.9608	812	0.2817	-	-	-	-	-	-
7.9706	813	4.4951	-	-	-	-	-	-
7.9804	814	0.2119	-	-	-	-	-	-
7.9902	815	0.6086	-	-	-	-	-	-
8.0	816	0.0476	0.5521	0.5556	0.5462	0.5374	0.5033	0.4851
8.0098	817	0.8563	-	-	-	-	-	-
8.0196	818	0.7961	-	-	-	-	-	-
8.0294	819	0.2469	-	-	-	-	-	-
8.0392	820	0.1805	-	-	-	-	-	-
8.0490	821	0.4718	-	-	-	-	-	-
8.0588	822	0.0907	-	-	-	-	-	-
8.0686	823	0.3712	-	-	-	-	-	-
8.0784	824	0.184	-	-	-	-	-	-
8.0882	825	0.5099	-	-	-	-	-	-
8.0980	826	0.9554	-	-	-	-	-	-
8.1078	827	0.036	-	-	-	-	-	-
8.1176	828	1.2681	-	-	-	-	-	-
8.1275	829	1.4157	-	-	-	-	-	-
8.1373	830	0.354	-	-	-	-	-	-
8.1471	831	0.4248	-	-	-	-	-	-
8.1569	832	0.6051	-	-	-	-	-	-
8.1667	833	0.5927	-	-	-	-	-	-
8.1765	834	0.8834	-	-	-	-	-	-
8.1863	835	0.7545	-	-	-	-	-	-
8.1961	836	0.5309	-	-	-	-	-	-
8.2059	837	0.6324	-	-	-	-	-	-
8.2157	838	0.1074	-	-	-	-	-	-
8.2255	839	1.5831	-	-	-	-	-	-
8.2353	840	0.0787	-	-	-	-	-	-
8.2451	841	0.1396	-	-	-	-	-	-
8.2549	842	2.757	-	-	-	-	-	-
8.2647	843	0.4178	-	-	-	-	-	-
8.2745	844	0.0328	-	-	-	-	-	-
8.2843	845	0.1078	-	-	-	-	-	-
8.2941	846	1.2134	-	-	-	-	-	-
8.3039	847	2.043	-	-	-	-	-	-
8.3137	848	1.2416	-	-	-	-	-	-
8.3235	849	0.3595	-	-	-	-	-	-
8.3333	850	0.203	-	-	-	-	-	-
8.3431	851	1.0074	-	-	-	-	-	-
8.3529	852	0.4635	-	-	-	-	-	-
8.3627	853	1.2737	-	-	-	-	-	-
8.3725	854	0.3156	-	-	-	-	-	-
8.3824	855	0.2234	-	-	-	-	-	-
8.3922	856	2.3354	-	-	-	-	-	-
8.4020	857	0.0932	-	-	-	-	-	-
8.4118	858	1.1709	-	-	-	-	-	-
8.4216	859	0.0524	-	-	-	-	-	-
8.4314	860	0.6069	-	-	-	-	-	-
8.4412	861	0.134	-	-	-	-	-	-
8.4510	862	0.103	-	-	-	-	-	-
8.4608	863	0.4228	-	-	-	-	-	-
8.4706	864	0.2694	-	-	-	-	-	-
8.4804	865	0.8675	-	-	-	-	-	-
8.4902	866	0.0473	-	-	-	-	-	-
8.5	867	1.5123	-	-	-	-	-	-
8.5098	868	0.1701	-	-	-	-	-	-
8.5196	869	0.2643	-	-	-	-	-	-
8.5294	870	0.3825	-	-	-	-	-	-
8.5392	871	0.8721	-	-	-	-	-	-
8.5490	872	0.4029	-	-	-	-	-	-
8.5588	873	0.6358	-	-	-	-	-	-
8.5686	874	1.4352	-	-	-	-	-	-
8.5784	875	3.0679	-	-	-	-	-	-
8.5882	876	0.3414	-	-	-	-	-	-
8.5980	877	0.0676	-	-	-	-	-	-
8.6078	878	0.2983	-	-	-	-	-	-
8.6176	879	0.661	-	-	-	-	-	-
8.6275	880	0.8606	-	-	-	-	-	-
8.6373	881	1.0992	-	-	-	-	-	-
8.6471	882	0.5299	-	-	-	-	-	-
8.6569	883	0.8567	-	-	-	-	-	-
8.6667	884	0.0464	-	-	-	-	-	-
8.6765	885	4.8367	-	-	-	-	-	-
8.6863	886	0.1096	-	-	-	-	-	-
8.6961	887	1.0106	-	-	-	-	-	-
8.7059	888	0.503	-	-	-	-	-	-
8.7157	889	0.5452	-	-	-	-	-	-
8.7255	890	0.0479	-	-	-	-	-	-
8.7353	891	0.1997	-	-	-	-	-	-
8.7451	892	1.6794	-	-	-	-	-	-
8.7549	893	0.3959	-	-	-	-	-	-
8.7647	894	0.124	-	-	-	-	-	-
8.7745	895	0.0321	-	-	-	-	-	-
8.7843	896	0.0465	-	-	-	-	-	-
8.7941	897	1.7601	-	-	-	-	-	-
8.8039	898	1.4143	-	-	-	-	-	-
8.8137	899	0.5717	-	-	-	-	-	-
8.8235	900	0.1885	-	-	-	-	-	-
8.8333	901	0.0329	-	-	-	-	-	-
8.8431	902	0.8594	-	-	-	-	-	-
8.8529	903	0.2483	-	-	-	-	-	-
8.8627	904	0.3048	-	-	-	-	-	-
8.8725	905	0.2954	-	-	-	-	-	-
8.8824	906	0.0401	-	-	-	-	-	-
8.8922	907	0.8687	-	-	-	-	-	-
8.9020	908	0.1259	-	-	-	-	-	-
8.9118	909	0.1995	-	-	-	-	-	-
8.9216	910	0.7339	-	-	-	-	-	-
8.9314	911	0.614	-	-	-	-	-	-
8.9412	912	0.7818	-	-	-	-	-	-
8.9510	913	0.1676	-	-	-	-	-	-
8.9608	914	0.2969	-	-	-	-	-	-
8.9706	915	0.2982	-	-	-	-	-	-
8.9804	916	0.1581	-	-	-	-	-	-
8.9902	917	1.218	-	-	-	-	-	-
9.0	918	1.4346	0.5584	0.5632	0.5544	0.5341	0.5061	0.4862
9.0098	919	1.3829	-	-	-	-	-	-
9.0196	920	0.66	-	-	-	-	-	-
9.0294	921	1.1933	-	-	-	-	-	-
9.0392	922	2.1405	-	-	-	-	-	-
9.0490	923	2.2798	-	-	-	-	-	-
9.0588	924	1.0026	-	-	-	-	-	-
9.0686	925	0.1313	-	-	-	-	-	-
9.0784	926	0.8387	-	-	-	-	-	-
9.0882	927	0.2706	-	-	-	-	-	-
9.0980	928	1.2225	-	-	-	-	-	-
9.1078	929	0.4177	-	-	-	-	-	-
9.1176	930	0.1102	-	-	-	-	-	-
9.1275	931	0.3428	-	-	-	-	-	-
9.1373	932	0.178	-	-	-	-	-	-
9.1471	933	1.0469	-	-	-	-	-	-
9.1569	934	1.0711	-	-	-	-	-	-
9.1667	935	1.2592	-	-	-	-	-	-
9.1765	936	0.4589	-	-	-	-	-	-
9.1863	937	0.0413	-	-	-	-	-	-
9.1961	938	0.119	-	-	-	-	-	-
9.2059	939	1.8376	-	-	-	-	-	-
9.2157	940	0.0359	-	-	-	-	-	-
9.2255	941	0.1707	-	-	-	-	-	-
9.2353	942	2.9861	-	-	-	-	-	-
9.2451	943	0.0806	-	-	-	-	-	-
9.2549	944	0.2299	-	-	-	-	-	-
9.2647	945	0.0742	-	-	-	-	-	-
9.2745	946	0.3245	-	-	-	-	-	-
9.2843	947	0.2226	-	-	-	-	-	-
9.2941	948	0.2429	-	-	-	-	-	-
9.3039	949	0.259	-	-	-	-	-	-
9.3137	950	0.0743	-	-	-	-	-	-
9.3235	951	0.1357	-	-	-	-	-	-
9.3333	952	0.5285	-	-	-	-	-	-
9.3431	953	0.154	-	-	-	-	-	-
9.3529	954	0.0586	-	-	-	-	-	-
9.3627	955	0.1569	-	-	-	-	-	-
9.3725	956	0.3171	-	-	-	-	-	-
9.3824	957	0.0161	-	-	-	-	-	-
9.3922	958	0.0415	-	-	-	-	-	-
9.4020	959	2.5649	-	-	-	-	-	-
9.4118	960	0.2358	-	-	-	-	-	-
9.4216	961	0.6078	-	-	-	-	-	-
9.4314	962	1.802	-	-	-	-	-	-
9.4412	963	0.0035	-	-	-	-	-	-
9.4510	964	0.3974	-	-	-	-	-	-
9.4608	965	1.8158	-	-	-	-	-	-
9.4706	966	0.8078	-	-	-	-	-	-
9.4804	967	0.0497	-	-	-	-	-	-
9.4902	968	0.8231	-	-	-	-	-	-
9.5	969	0.3436	-	-	-	-	-	-
9.5098	970	1.7315	-	-	-	-	-	-
9.5196	971	0.9816	-	-	-	-	-	-
9.5294	972	1.2535	-	-	-	-	-	-
9.5392	973	0.1249	-	-	-	-	-	-
9.5490	974	0.3484	-	-	-	-	-	-
9.5588	975	0.2927	-	-	-	-	-	-
9.5686	976	1.5592	-	-	-	-	-	-
9.5784	977	0.4511	-	-	-	-	-	-
9.5882	978	0.4105	-	-	-	-	-	-
9.5980	979	0.1007	-	-	-	-	-	-
9.6078	980	1.0741	-	-	-	-	-	-
9.6176	981	0.4839	-	-	-	-	-	-
9.6275	982	0.1092	-	-	-	-	-	-
9.6373	983	2.4432	-	-	-	-	-	-
9.6471	984	0.1773	-	-	-	-	-	-
9.6569	985	0.1584	-	-	-	-	-	-
9.6667	986	0.1581	-	-	-	-	-	-
9.6765	987	0.1331	-	-	-	-	-	-
9.6863	988	0.1053	-	-	-	-	-	-
9.6961	989	0.1884	-	-	-	-	-	-
9.7059	990	0.1585	-	-	-	-	-	-
9.7157	991	0.0562	-	-	-	-	-	-
9.7255	992	0.1197	-	-	-	-	-	-
9.7353	993	0.2471	-	-	-	-	-	-
9.7451	994	1.7135	-	-	-	-	-	-
9.7549	995	0.0801	-	-	-	-	-	-
9.7647	996	0.2973	-	-	-	-	-	-
9.7745	997	0.062	-	-	-	-	-	-
9.7843	998	2.3336	-	-	-	-	-	-
9.7941	999	1.7538	-	-	-	-	-	-
9.8039	1000	0.2638	-	-	-	-	-	-
9.8137	1001	0.7697	-	-	-	-	-	-
9.8235	1002	0.3561	-	-	-	-	-	-
9.8333	1003	0.1987	-	-	-	-	-	-
9.8431	1004	0.3988	-	-	-	-	-	-
9.8529	1005	0.0579	-	-	-	-	-	-
9.8627	1006	0.1217	-	-	-	-	-	-
9.8725	1007	1.1973	-	-	-	-	-	-
9.8824	1008	2.085	-	-	-	-	-	-
9.8922	1009	0.7251	-	-	-	-	-	-
9.9020	1010	0.1544	-	-	-	-	-	-
9.9118	1011	0.0993	-	-	-	-	-	-
9.9216	1012	0.5318	-	-	-	-	-	-
9.9314	1013	0.0847	-	-	-	-	-	-
9.9412	1014	2.1554	-	-	-	-	-	-
9.9510	1015	0.287	-	-	-	-	-	-
9.9608	1016	0.0654	-	-	-	-	-	-
9.9706	1017	4.5422	-	-	-	-	-	-
9.9804	1018	0.6639	-	-	-	-	-	-
9.9902	1019	0.0347	-	-	-	-	-	-
10.0	1020	0.1511	0.5529	0.5576	0.5521	0.5352	0.5041	0.4886

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.1.2
• Transformers: 4.51.3
• PyTorch: 2.8.0+cu126
• Accelerate: 1.11.0
• Datasets: 4.0.0
• Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}