IoannisKat1/bert-base-uncased-ft-new

bert_base_uncased Finetuned on Data

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. 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: google-bert/bert-base-uncased
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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 = [
    'How should the Board act when performing its tasks?',
    "In order to promote the consistent application of this Regulation, the Board should be set up as an independent body of the Union. To fulfil its objectives, the Board should have legal personality. The Board should be represented by its Chair. It should replace the Working Party on the Protection of Individuals with Regard to the Processing of Personal Data established by Directive 95/46/EC. It should consist of the head of a supervisory authority of each Member State and the European Data Protection Supervisor or their respective representatives. The Commission should participate in the Board's activities without voting rights and the European Data Protection Supervisor should have specific voting rights. The Board should contribute to the consistent application of this Regulation throughout the Union, including by advising the Commission, in particular on the level of protection in third countries or international organisations, and promoting cooperation of the supervisory authorities throughout the Union. The Board should act independently when performing its tasks.",
    "1.Processing shall be lawful only if and to the extent that at least one of the following applies: (a)  the data subject has given consent to the processing of his or her personal data for one or more specific purposes; (b)  processing is necessary for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract; (c)  processing is necessary for compliance with a legal obligation to which the controller is subject; (d)  processing is necessary in order to protect the vital interests of the data subject or of another natural person; (e)  processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller; (f)  processing is necessary for the purposes of the legitimate interests pursued by the controller or by a third party, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. Point (f) of the first subparagraph shall not apply to processing carried out by public authorities in the performance of their tasks.\n2.Member States may maintain or introduce more specific provisions to adapt the application of the rules of this Regulation with regard to processing for compliance with points (c) and (e) of paragraph 1 by determining more precisely specific requirements for the processing and other measures to ensure lawful and fair processing including for other specific processing situations as provided for in Chapter IX.\n3.The basis for the processing referred to in point (c) and (e) of paragraph 1 shall be laid down by: (a)  Union law; or (b)  Member State law to which the controller is subject. The purpose of the processing shall be determined in that legal basis or, as regards the processing referred to in point (e) of paragraph 1, shall be necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. That legal basis may contain specific provisions to adapt the application of rules of this Regulation, inter alia: the general conditions governing the lawfulness of processing by the controller; the types of data which are subject to the processing; the data subjects concerned; the entities to, and the purposes for which, the personal data may be disclosed; the purpose limitation; storage periods; and processing operations and processing procedures, including measures to ensure lawful and fair processing such as those for other specific 4.5.2016 L 119/36   processing situations as provided for in Chapter IX. The Union or the Member State law shall meet an objective of public interest and be proportionate to the legitimate aim pursued.\n4.Where the processing for a purpose other than that for which the personal data have been collected is not based on the data subject's consent or on a Union or Member State law which constitutes a necessary and proportionate measure in a democratic society to safeguard the objectives referred to in Article 23(1), the controller shall, in order to ascertain whether processing for another purpose is compatible with the purpose for which the personal data are initially collected, take into account, inter alia: (a)  any link between the purposes for which the personal data have been collected and the purposes of the intended further processing; (b)  the context in which the personal data have been collected, in particular regarding the relationship between data subjects and the controller; (c)  the nature of the personal data, in particular whether special categories of personal data are processed, pursuant to Article 9, or whether personal data related to criminal convictions and offences are processed, pursuant to Article 10; (d)  the possible consequences of the intended further processing for data subjects; (e)  the existence of appropriate safeguards, which may include encryption or pseudonymisation.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.6448,  0.0439],
#         [ 0.6448,  1.0000, -0.0616],
#         [ 0.0439, -0.0616,  1.0000]])

Evaluation

Metrics

Information Retrieval

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

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.3907
cosine_accuracy@3	0.4226
cosine_accuracy@5	0.4595
cosine_accuracy@10	0.5258
cosine_precision@1	0.3907
cosine_precision@3	0.38
cosine_precision@5	0.3602
cosine_precision@10	0.3349
cosine_recall@1	0.0697
cosine_recall@3	0.1843
cosine_recall@5	0.2485
cosine_recall@10	0.3735
cosine_ndcg@10	0.4507
cosine_mrr@10	0.4188
cosine_map@100	0.5062

Information Retrieval

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

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.3882
cosine_accuracy@3	0.4201
cosine_accuracy@5	0.4545
cosine_accuracy@10	0.5086
cosine_precision@1	0.3882
cosine_precision@3	0.3776
cosine_precision@5	0.3587
cosine_precision@10	0.3283
cosine_recall@1	0.0698
cosine_recall@3	0.1826
cosine_recall@5	0.2468
cosine_recall@10	0.3673
cosine_ndcg@10	0.444
cosine_mrr@10	0.4147
cosine_map@100	0.5034

Information Retrieval

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

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.3931
cosine_accuracy@3	0.4177
cosine_accuracy@5	0.4496
cosine_accuracy@10	0.5184
cosine_precision@1	0.3931
cosine_precision@3	0.3792
cosine_precision@5	0.3597
cosine_precision@10	0.3354
cosine_recall@1	0.0693
cosine_recall@3	0.1766
cosine_recall@5	0.2385
cosine_recall@10	0.3696
cosine_ndcg@10	0.4482
cosine_mrr@10	0.418
cosine_map@100	0.5008

Information Retrieval

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

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.3956
cosine_accuracy@3	0.4349
cosine_accuracy@5	0.4717
cosine_accuracy@10	0.5135
cosine_precision@1	0.3956
cosine_precision@3	0.389
cosine_precision@5	0.3749
cosine_precision@10	0.3445
cosine_recall@1	0.0667
cosine_recall@3	0.1763
cosine_recall@5	0.2437
cosine_recall@10	0.36
cosine_ndcg@10	0.4543
cosine_mrr@10	0.4236
cosine_map@100	0.503

Information Retrieval

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

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.371
cosine_accuracy@3	0.4054
cosine_accuracy@5	0.4496
cosine_accuracy@10	0.5012
cosine_precision@1	0.371
cosine_precision@3	0.3595
cosine_precision@5	0.345
cosine_precision@10	0.3226
cosine_recall@1	0.0678
cosine_recall@3	0.1725
cosine_recall@5	0.2313
cosine_recall@10	0.3468
cosine_ndcg@10	0.4304
cosine_mrr@10	0.3994
cosine_map@100	0.4816

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: 15.47 tokens max: 34 tokens	min: 25 tokens mean: 363.61 tokens max: 512 tokens

• Samples:

  anchor	positive
  What actions did the defendant fail to implement?	Court (Civil/Criminal): Civil Provisions: Time of commission of the act: Outcome (not guilty, guilty): Reasoning: Partially accepts the lawsuit. Facts: The plaintiff, who works as a lawyer, maintains a savings account with the defendant banking corporation under account number GR.............. Pursuant to a contract dated June 11, 2010, established in Thessaloniki between the defendant and the plaintiff, the plaintiff was granted access to the electronic banking system (e-banking) to conduct banking transactions remotely. On October 10, 2020, the plaintiff fell victim to electronic fraud through the "phishing" method, whereby an unknown perpetrator managed to extract and transfer €3,000.00 from the plaintiff’s account to another account of the same bank. Specifically, on that day at 6:51 a.m., the plaintiff received an email from the sender ".........", with the address ..........., informing him that his debit card had been suspended and that online p...
  What can the data subject do in the context of using information society services?	1.The data subject shall have the right to object, on grounds relating to his or her particular situation, at any time to processing of personal data concerning him or her which is based on point (e) or (f) of Article 6(1), including profiling based on those provisions. The controller shall no longer process the personal data unless the controller demonstrates compelling legitimate grounds for the processing which override the interests, rights and freedoms of the data subject or for the establishment, exercise or defence of legal claims. 2.Where personal data are processed for direct marketing purposes, the data subject shall have the right to object at any time to processing of personal data concerning him or her for such marketing, which includes profiling to the extent that it is related to such direct marketing. 3.Where the data subject objects to processing for direct marketing purposes, the personal data shall no longer be processed for such purposes. 4.5.2016 L 119/45 4.At th...
  How should the Commission inform third countries or international organisations of data protection inadequacies?	The Commission may recognise that a third country, a territory or a specified sector within a third country, or an international organisation no longer ensures an adequate level of data protection. Consequently the transfer of personal data to that third country or international organisation should be prohibited, unless the requirements in this Regulation relating to transfers subject to appropriate safeguards, including binding corporate rules, and derogations for specific situations are fulfilled. In that case, provision should be made for consultations between the Commission and such third countries or international organisations. The Commission should, in a timely manner, inform the third country or international organisation of the reasons and enter into consultations with it in order to remedy the situation.

• Loss: MatryoshkaLoss with these parameters:

  {
      "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: 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_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	15.7141	-	-	-	-	-
0.0196	2	19.9298	-	-	-	-	-
0.0294	3	16.3113	-	-	-	-	-
0.0392	4	16.4252	-	-	-	-	-
0.0490	5	15.8192	-	-	-	-	-
0.0588	6	16.0262	-	-	-	-	-
0.0686	7	14.6899	-	-	-	-	-
0.0784	8	15.0818	-	-	-	-	-
0.0882	9	18.663	-	-	-	-	-
0.0980	10	15.2615	-	-	-	-	-
0.1078	11	14.7447	-	-	-	-	-
0.1176	12	15.7102	-	-	-	-	-
0.1275	13	16.7967	-	-	-	-	-
0.1373	14	13.3178	-	-	-	-	-
0.1471	15	15.7202	-	-	-	-	-
0.1569	16	13.6993	-	-	-	-	-
0.1667	17	13.9843	-	-	-	-	-
0.1765	18	14.3489	-	-	-	-	-
0.1863	19	13.9907	-	-	-	-	-
0.1961	20	13.7404	-	-	-	-	-
0.2059	21	10.2427	-	-	-	-	-
0.2157	22	14.7446	-	-	-	-	-
0.2255	23	15.7778	-	-	-	-	-
0.2353	24	16.7369	-	-	-	-	-
0.2451	25	13.6189	-	-	-	-	-
0.2549	26	18.6547	-	-	-	-	-
0.2647	27	14.1386	-	-	-	-	-
0.2745	28	13.5343	-	-	-	-	-
0.2843	29	12.5569	-	-	-	-	-
0.2941	30	12.0759	-	-	-	-	-
0.3039	31	13.5691	-	-	-	-	-
0.3137	32	9.2042	-	-	-	-	-
0.3235	33	13.9038	-	-	-	-	-
0.3333	34	13.0435	-	-	-	-	-
0.3431	35	12.652	-	-	-	-	-
0.3529	36	12.0144	-	-	-	-	-
0.3627	37	12.1732	-	-	-	-	-
0.3725	38	9.6029	-	-	-	-	-
0.3824	39	11.088	-	-	-	-	-
0.3922	40	9.6017	-	-	-	-	-
0.4020	41	14.1734	-	-	-	-	-
0.4118	42	14.5926	-	-	-	-	-
0.4216	43	7.6713	-	-	-	-	-
0.4314	44	12.013	-	-	-	-	-
0.4412	45	12.7818	-	-	-	-	-
0.4510	46	11.7701	-	-	-	-	-
0.4608	47	11.6981	-	-	-	-	-
0.4706	48	7.0184	-	-	-	-	-
0.4804	49	11.1502	-	-	-	-	-
0.4902	50	7.1393	-	-	-	-	-
0.5	51	13.5702	-	-	-	-	-
0.5098	52	9.8015	-	-	-	-	-
0.5196	53	9.8888	-	-	-	-	-
0.5294	54	9.2609	-	-	-	-	-
0.5392	55	9.5653	-	-	-	-	-
0.5490	56	10.1831	-	-	-	-	-
0.5588	57	5.8571	-	-	-	-	-
0.5686	58	11.6202	-	-	-	-	-
0.5784	59	8.9687	-	-	-	-	-
0.5882	60	8.3242	-	-	-	-	-
0.5980	61	9.139	-	-	-	-	-
0.6078	62	9.8182	-	-	-	-	-
0.6176	63	10.4709	-	-	-	-	-
0.6275	64	9.1636	-	-	-	-	-
0.6373	65	6.8375	-	-	-	-	-
0.6471	66	10.5625	-	-	-	-	-
0.6569	67	7.8726	-	-	-	-	-
0.6667	68	10.8442	-	-	-	-	-
0.6765	69	12.7397	-	-	-	-	-
0.6863	70	8.2273	-	-	-	-	-
0.6961	71	7.5165	-	-	-	-	-
0.7059	72	7.6787	-	-	-	-	-
0.7157	73	6.9246	-	-	-	-	-
0.7255	74	9.1875	-	-	-	-	-
0.7353	75	7.827	-	-	-	-	-
0.7451	76	9.2136	-	-	-	-	-
0.7549	77	7.8857	-	-	-	-	-
0.7647	78	7.865	-	-	-	-	-
0.7745	79	6.9468	-	-	-	-	-
0.7843	80	12.6114	-	-	-	-	-
0.7941	81	5.9008	-	-	-	-	-
0.8039	82	6.4489	-	-	-	-	-
0.8137	83	5.6792	-	-	-	-	-
0.8235	84	10.424	-	-	-	-	-
0.8333	85	10.4203	-	-	-	-	-
0.8431	86	5.551	-	-	-	-	-
0.8529	87	7.1461	-	-	-	-	-
0.8627	88	10.0891	-	-	-	-	-
0.8725	89	7.5677	-	-	-	-	-
0.8824	90	4.5324	-	-	-	-	-
0.8922	91	9.475	-	-	-	-	-
0.9020	92	8.2073	-	-	-	-	-
0.9118	93	8.3825	-	-	-	-	-
0.9216	94	9.2301	-	-	-	-	-
0.9314	95	8.4193	-	-	-	-	-
0.9412	96	10.4013	-	-	-	-	-
0.9510	97	9.4639	-	-	-	-	-
0.9608	98	7.9491	-	-	-	-	-
0.9706	99	6.8399	-	-	-	-	-
0.9804	100	11.285	-	-	-	-	-
0.9902	101	5.4246	-	-	-	-	-
1.0	102	2.2306	0.3205	0.3198	0.3224	0.3080	0.2512
1.0098	103	6.9273	-	-	-	-	-
1.0196	104	5.4814	-	-	-	-	-
1.0294	105	4.4908	-	-	-	-	-
1.0392	106	4.7609	-	-	-	-	-
1.0490	107	5.4881	-	-	-	-	-
1.0588	108	6.9669	-	-	-	-	-
1.0686	109	5.2229	-	-	-	-	-
1.0784	110	6.2044	-	-	-	-	-
1.0882	111	3.8223	-	-	-	-	-
1.0980	112	3.2096	-	-	-	-	-
1.1078	113	5.1949	-	-	-	-	-
1.1176	114	5.7645	-	-	-	-	-
1.1275	115	6.9909	-	-	-	-	-
1.1373	116	3.136	-	-	-	-	-
1.1471	117	4.3039	-	-	-	-	-
1.1569	118	5.2367	-	-	-	-	-
1.1667	119	5.5356	-	-	-	-	-
1.1765	120	7.5846	-	-	-	-	-
1.1863	121	4.8824	-	-	-	-	-
1.1961	122	3.4201	-	-	-	-	-
1.2059	123	11.4679	-	-	-	-	-
1.2157	124	6.8034	-	-	-	-	-
1.2255	125	9.4692	-	-	-	-	-
1.2353	126	8.3458	-	-	-	-	-
1.2451	127	4.8878	-	-	-	-	-
1.2549	128	8.559	-	-	-	-	-
1.2647	129	6.9098	-	-	-	-	-
1.2745	130	1.8911	-	-	-	-	-
1.2843	131	4.0066	-	-	-	-	-
1.2941	132	7.9379	-	-	-	-	-
1.3039	133	7.6913	-	-	-	-	-
1.3137	134	11.0871	-	-	-	-	-
1.3235	135	6.8814	-	-	-	-	-
1.3333	136	3.3988	-	-	-	-	-
1.3431	137	6.1393	-	-	-	-	-
1.3529	138	2.6741	-	-	-	-	-
1.3627	139	6.1331	-	-	-	-	-
1.3725	140	8.4481	-	-	-	-	-
1.3824	141	5.2652	-	-	-	-	-
1.3922	142	5.593	-	-	-	-	-
1.4020	143	10.81	-	-	-	-	-
1.4118	144	5.7611	-	-	-	-	-
1.4216	145	6.8042	-	-	-	-	-
1.4314	146	6.7177	-	-	-	-	-
1.4412	147	3.7151	-	-	-	-	-
1.4510	148	7.0377	-	-	-	-	-
1.4608	149	2.0383	-	-	-	-	-
1.4706	150	3.3445	-	-	-	-	-
1.4804	151	3.8735	-	-	-	-	-
1.4902	152	7.003	-	-	-	-	-
1.5	153	4.8621	-	-	-	-	-
1.5098	154	6.4851	-	-	-	-	-
1.5196	155	5.5665	-	-	-	-	-
1.5294	156	2.8644	-	-	-	-	-
1.5392	157	6.6778	-	-	-	-	-
1.5490	158	4.9415	-	-	-	-	-
1.5588	159	8.8981	-	-	-	-	-
1.5686	160	4.6822	-	-	-	-	-
1.5784	161	2.043	-	-	-	-	-
1.5882	162	5.2752	-	-	-	-	-
1.5980	163	6.3728	-	-	-	-	-
1.6078	164	4.1213	-	-	-	-	-
1.6176	165	3.0021	-	-	-	-	-
1.6275	166	5.067	-	-	-	-	-
1.6373	167	6.7529	-	-	-	-	-
1.6471	168	6.6704	-	-	-	-	-
1.6569	169	7.1276	-	-	-	-	-
1.6667	170	6.285	-	-	-	-	-
1.6765	171	5.7332	-	-	-	-	-
1.6863	172	6.2255	-	-	-	-	-
1.6961	173	6.1692	-	-	-	-	-
1.7059	174	4.8534	-	-	-	-	-
1.7157	175	11.4246	-	-	-	-	-
1.7255	176	9.1455	-	-	-	-	-
1.7353	177	7.2099	-	-	-	-	-
1.7451	178	8.3639	-	-	-	-	-
1.7549	179	4.8122	-	-	-	-	-
1.7647	180	5.6348	-	-	-	-	-
1.7745	181	6.0456	-	-	-	-	-
1.7843	182	3.878	-	-	-	-	-
1.7941	183	9.4243	-	-	-	-	-
1.8039	184	6.3067	-	-	-	-	-
1.8137	185	7.6652	-	-	-	-	-
1.8235	186	4.9735	-	-	-	-	-
1.8333	187	6.9195	-	-	-	-	-
1.8431	188	3.7862	-	-	-	-	-
1.8529	189	3.1527	-	-	-	-	-
1.8627	190	3.0304	-	-	-	-	-
1.8725	191	4.2305	-	-	-	-	-
1.8824	192	4.1313	-	-	-	-	-
1.8922	193	4.0905	-	-	-	-	-
1.9020	194	3.8162	-	-	-	-	-
1.9118	195	9.0048	-	-	-	-	-
1.9216	196	5.7165	-	-	-	-	-
1.9314	197	2.4262	-	-	-	-	-
1.9412	198	5.5227	-	-	-	-	-
1.9510	199	3.1251	-	-	-	-	-
1.9608	200	4.6023	-	-	-	-	-
1.9706	201	2.174	-	-	-	-	-
1.9804	202	8.1218	-	-	-	-	-
1.9902	203	3.6265	-	-	-	-	-
2.0	204	9.0022	0.4215	0.4111	0.4054	0.3943	0.3516
2.0098	205	4.5839	-	-	-	-	-
2.0196	206	3.2195	-	-	-	-	-
2.0294	207	4.9547	-	-	-	-	-
2.0392	208	2.7177	-	-	-	-	-
2.0490	209	3.9209	-	-	-	-	-
2.0588	210	2.2717	-	-	-	-	-
2.0686	211	1.072	-	-	-	-	-
2.0784	212	3.5728	-	-	-	-	-
2.0882	213	2.4756	-	-	-	-	-
2.0980	214	3.0092	-	-	-	-	-
2.1078	215	0.8999	-	-	-	-	-
2.1176	216	4.5233	-	-	-	-	-
2.1275	217	4.1223	-	-	-	-	-
2.1373	218	1.8551	-	-	-	-	-
2.1471	219	5.0426	-	-	-	-	-
2.1569	220	2.3534	-	-	-	-	-
2.1667	221	4.0579	-	-	-	-	-
2.1765	222	1.8646	-	-	-	-	-
2.1863	223	4.8616	-	-	-	-	-
2.1961	224	3.038	-	-	-	-	-
2.2059	225	1.5944	-	-	-	-	-
2.2157	226	1.1801	-	-	-	-	-
2.2255	227	3.0152	-	-	-	-	-
2.2353	228	10.8283	-	-	-	-	-
2.2451	229	4.8659	-	-	-	-	-
2.2549	230	0.3958	-	-	-	-	-
2.2647	231	3.6975	-	-	-	-	-
2.2745	232	4.9146	-	-	-	-	-
2.2843	233	3.2711	-	-	-	-	-
2.2941	234	0.8335	-	-	-	-	-
2.3039	235	2.6175	-	-	-	-	-
2.3137	236	1.3882	-	-	-	-	-
2.3235	237	0.6938	-	-	-	-	-
2.3333	238	2.7426	-	-	-	-	-
2.3431	239	6.179	-	-	-	-	-
2.3529	240	2.5322	-	-	-	-	-
2.3627	241	3.8656	-	-	-	-	-
2.3725	242	3.4385	-	-	-	-	-
2.3824	243	3.116	-	-	-	-	-
2.3922	244	0.8342	-	-	-	-	-
2.4020	245	2.7266	-	-	-	-	-
2.4118	246	2.1479	-	-	-	-	-
2.4216	247	0.5676	-	-	-	-	-
2.4314	248	2.8609	-	-	-	-	-
2.4412	249	3.9221	-	-	-	-	-
2.4510	250	3.998	-	-	-	-	-
2.4608	251	6.2543	-	-	-	-	-
2.4706	252	3.1022	-	-	-	-	-
2.4804	253	3.1827	-	-	-	-	-
2.4902	254	2.9459	-	-	-	-	-
2.5	255	6.5891	-	-	-	-	-
2.5098	256	4.941	-	-	-	-	-
2.5196	257	1.2576	-	-	-	-	-
2.5294	258	1.037	-	-	-	-	-
2.5392	259	5.221	-	-	-	-	-
2.5490	260	3.5002	-	-	-	-	-
2.5588	261	2.1365	-	-	-	-	-
2.5686	262	3.7367	-	-	-	-	-
2.5784	263	5.9917	-	-	-	-	-
2.5882	264	5.0322	-	-	-	-	-
2.5980	265	1.331	-	-	-	-	-
2.6078	266	2.3862	-	-	-	-	-
2.6176	267	2.3224	-	-	-	-	-
2.6275	268	3.3178	-	-	-	-	-
2.6373	269	5.1318	-	-	-	-	-
2.6471	270	3.5232	-	-	-	-	-
2.6569	271	4.3459	-	-	-	-	-
2.6667	272	3.9521	-	-	-	-	-
2.6765	273	2.4277	-	-	-	-	-
2.6863	274	1.3987	-	-	-	-	-
2.6961	275	1.3355	-	-	-	-	-
2.7059	276	6.8146	-	-	-	-	-
2.7157	277	2.7484	-	-	-	-	-
2.7255	278	1.8571	-	-	-	-	-
2.7353	279	1.2208	-	-	-	-	-
2.7451	280	1.8873	-	-	-	-	-
2.7549	281	5.5332	-	-	-	-	-
2.7647	282	1.469	-	-	-	-	-
2.7745	283	2.6817	-	-	-	-	-
2.7843	284	6.3603	-	-	-	-	-
2.7941	285	0.8347	-	-	-	-	-
2.8039	286	3.7871	-	-	-	-	-
2.8137	287	3.2206	-	-	-	-	-
2.8235	288	2.7951	-	-	-	-	-
2.8333	289	1.3966	-	-	-	-	-
2.8431	290	1.6591	-	-	-	-	-
2.8529	291	3.4087	-	-	-	-	-
2.8627	292	5.133	-	-	-	-	-
2.8725	293	0.6866	-	-	-	-	-
2.8824	294	1.3529	-	-	-	-	-
2.8922	295	5.4178	-	-	-	-	-
2.9020	296	3.327	-	-	-	-	-
2.9118	297	3.4192	-	-	-	-	-
2.9216	298	1.2658	-	-	-	-	-
2.9314	299	1.0931	-	-	-	-	-
2.9412	300	3.3242	-	-	-	-	-
2.9510	301	1.4626	-	-	-	-	-
2.9608	302	4.4721	-	-	-	-	-
2.9706	303	4.5404	-	-	-	-	-
2.9804	304	2.4667	-	-	-	-	-
2.9902	305	1.8945	-	-	-	-	-
3.0	306	0.5133	0.4141	0.4210	0.4201	0.4205	0.3695
3.0098	307	0.8378	-	-	-	-	-
3.0196	308	1.5729	-	-	-	-	-
3.0294	309	0.5079	-	-	-	-	-
3.0392	310	1.5766	-	-	-	-	-
3.0490	311	3.9715	-	-	-	-	-
3.0588	312	2.3516	-	-	-	-	-
3.0686	313	4.2686	-	-	-	-	-
3.0784	314	1.6472	-	-	-	-	-
3.0882	315	2.372	-	-	-	-	-
3.0980	316	2.3549	-	-	-	-	-
3.1078	317	1.1143	-	-	-	-	-
3.1176	318	1.0626	-	-	-	-	-
3.1275	319	1.0482	-	-	-	-	-
3.1373	320	1.9146	-	-	-	-	-
3.1471	321	0.6047	-	-	-	-	-
3.1569	322	1.4464	-	-	-	-	-
3.1667	323	1.0272	-	-	-	-	-
3.1765	324	2.6977	-	-	-	-	-
3.1863	325	2.9464	-	-	-	-	-
3.1961	326	1.1477	-	-	-	-	-
3.2059	327	2.3362	-	-	-	-	-
3.2157	328	0.8658	-	-	-	-	-
3.2255	329	0.5602	-	-	-	-	-
3.2353	330	1.2728	-	-	-	-	-
3.2451	331	1.8408	-	-	-	-	-
3.2549	332	0.7387	-	-	-	-	-
3.2647	333	0.2165	-	-	-	-	-
3.2745	334	1.1435	-	-	-	-	-
3.2843	335	2.5381	-	-	-	-	-
3.2941	336	0.5402	-	-	-	-	-
3.3039	337	1.1352	-	-	-	-	-
3.3137	338	2.0289	-	-	-	-	-
3.3235	339	1.9294	-	-	-	-	-
3.3333	340	2.2742	-	-	-	-	-
3.3431	341	2.8841	-	-	-	-	-
3.3529	342	2.0572	-	-	-	-	-
3.3627	343	1.4641	-	-	-	-	-
3.3725	344	3.9854	-	-	-	-	-
3.3824	345	2.6084	-	-	-	-	-
3.3922	346	1.1946	-	-	-	-	-
3.4020	347	3.0805	-	-	-	-	-
3.4118	348	3.4866	-	-	-	-	-
3.4216	349	1.814	-	-	-	-	-
3.4314	350	4.866	-	-	-	-	-
3.4412	351	1.7529	-	-	-	-	-
3.4510	352	4.7152	-	-	-	-	-
3.4608	353	2.2526	-	-	-	-	-
3.4706	354	1.8223	-	-	-	-	-
3.4804	355	1.7654	-	-	-	-	-
3.4902	356	1.0013	-	-	-	-	-
3.5	357	0.9441	-	-	-	-	-
3.5098	358	1.2359	-	-	-	-	-
3.5196	359	0.8625	-	-	-	-	-
3.5294	360	2.7402	-	-	-	-	-
3.5392	361	0.9782	-	-	-	-	-
3.5490	362	3.2514	-	-	-	-	-
3.5588	363	0.3758	-	-	-	-	-
3.5686	364	1.6266	-	-	-	-	-
3.5784	365	1.7738	-	-	-	-	-
3.5882	366	2.2321	-	-	-	-	-
3.5980	367	3.6657	-	-	-	-	-
3.6078	368	0.9546	-	-	-	-	-
3.6176	369	0.9882	-	-	-	-	-
3.6275	370	2.7033	-	-	-	-	-
3.6373	371	1.8896	-	-	-	-	-
3.6471	372	2.14	-	-	-	-	-
3.6569	373	1.1367	-	-	-	-	-
3.6667	374	0.9034	-	-	-	-	-
3.6765	375	4.1989	-	-	-	-	-
3.6863	376	1.0531	-	-	-	-	-
3.6961	377	1.2001	-	-	-	-	-
3.7059	378	0.5419	-	-	-	-	-
3.7157	379	2.8246	-	-	-	-	-
3.7255	380	1.9894	-	-	-	-	-
3.7353	381	7.2872	-	-	-	-	-
3.7451	382	1.165	-	-	-	-	-
3.7549	383	3.2534	-	-	-	-	-
3.7647	384	1.4657	-	-	-	-	-
3.7745	385	3.5551	-	-	-	-	-
3.7843	386	0.3342	-	-	-	-	-
3.7941	387	0.684	-	-	-	-	-
3.8039	388	2.6067	-	-	-	-	-
3.8137	389	0.6356	-	-	-	-	-
3.8235	390	4.1628	-	-	-	-	-
3.8333	391	1.2829	-	-	-	-	-
3.8431	392	0.2132	-	-	-	-	-
3.8529	393	3.1881	-	-	-	-	-
3.8627	394	1.1507	-	-	-	-	-
3.8725	395	6.0361	-	-	-	-	-
3.8824	396	1.1641	-	-	-	-	-
3.8922	397	5.4936	-	-	-	-	-
3.9020	398	2.9277	-	-	-	-	-
3.9118	399	3.0353	-	-	-	-	-
3.9216	400	0.7986	-	-	-	-	-
3.9314	401	1.7391	-	-	-	-	-
3.9412	402	0.5909	-	-	-	-	-
3.9510	403	1.2669	-	-	-	-	-
3.9608	404	1.8321	-	-	-	-	-
3.9706	405	2.9635	-	-	-	-	-
3.9804	406	1.6435	-	-	-	-	-
3.9902	407	2.9212	-	-	-	-	-
4.0	408	1.423	0.4087	0.4103	0.3978	0.3957	0.3638
4.0098	409	2.6238	-	-	-	-	-
4.0196	410	1.903	-	-	-	-	-
4.0294	411	0.3648	-	-	-	-	-
4.0392	412	1.4451	-	-	-	-	-
4.0490	413	0.4473	-	-	-	-	-
4.0588	414	2.7152	-	-	-	-	-
4.0686	415	3.1068	-	-	-	-	-
4.0784	416	0.5772	-	-	-	-	-
4.0882	417	5.783	-	-	-	-	-
4.0980	418	2.1852	-	-	-	-	-
4.1078	419	0.316	-	-	-	-	-
4.1176	420	1.8778	-	-	-	-	-
4.1275	421	1.1323	-	-	-	-	-
4.1373	422	1.321	-	-	-	-	-
4.1471	423	2.4721	-	-	-	-	-
4.1569	424	1.726	-	-	-	-	-
4.1667	425	1.2905	-	-	-	-	-
4.1765	426	2.9871	-	-	-	-	-
4.1863	427	0.0724	-	-	-	-	-
4.1961	428	1.1808	-	-	-	-	-
4.2059	429	1.7136	-	-	-	-	-
4.2157	430	1.831	-	-	-	-	-
4.2255	431	1.18	-	-	-	-	-
4.2353	432	0.6387	-	-	-	-	-
4.2451	433	0.8915	-	-	-	-	-
4.2549	434	1.2173	-	-	-	-	-
4.2647	435	3.1688	-	-	-	-	-
4.2745	436	2.5525	-	-	-	-	-
4.2843	437	3.9881	-	-	-	-	-
4.2941	438	2.8312	-	-	-	-	-
4.3039	439	1.638	-	-	-	-	-
4.3137	440	0.3774	-	-	-	-	-
4.3235	441	1.389	-	-	-	-	-
4.3333	442	1.3137	-	-	-	-	-
4.3431	443	1.5514	-	-	-	-	-
4.3529	444	6.8409	-	-	-	-	-
4.3627	445	8.0112	-	-	-	-	-
4.3725	446	0.377	-	-	-	-	-
4.3824	447	1.0928	-	-	-	-	-
4.3922	448	0.2113	-	-	-	-	-
4.4020	449	2.068	-	-	-	-	-
4.4118	450	1.0895	-	-	-	-	-
4.4216	451	1.0891	-	-	-	-	-
4.4314	452	1.706	-	-	-	-	-
4.4412	453	2.2784	-	-	-	-	-
4.4510	454	0.6901	-	-	-	-	-
4.4608	455	4.7342	-	-	-	-	-
4.4706	456	1.6835	-	-	-	-	-
4.4804	457	1.9608	-	-	-	-	-
4.4902	458	2.36	-	-	-	-	-
4.5	459	0.5788	-	-	-	-	-
4.5098	460	3.0938	-	-	-	-	-
4.5196	461	1.9388	-	-	-	-	-
4.5294	462	1.8407	-	-	-	-	-
4.5392	463	0.2539	-	-	-	-	-
4.5490	464	0.8653	-	-	-	-	-
4.5588	465	1.0415	-	-	-	-	-
4.5686	466	0.2581	-	-	-	-	-
4.5784	467	3.2178	-	-	-	-	-
4.5882	468	1.7602	-	-	-	-	-
4.5980	469	3.5085	-	-	-	-	-
4.6078	470	1.4228	-	-	-	-	-
4.6176	471	1.6385	-	-	-	-	-
4.6275	472	1.6302	-	-	-	-	-
4.6373	473	0.2816	-	-	-	-	-
4.6471	474	1.0022	-	-	-	-	-
4.6569	475	3.1141	-	-	-	-	-
4.6667	476	0.2854	-	-	-	-	-
4.6765	477	4.6917	-	-	-	-	-
4.6863	478	0.3969	-	-	-	-	-
4.6961	479	2.4925	-	-	-	-	-
4.7059	480	0.4133	-	-	-	-	-
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4.9902	509	0.8882	-	-	-	-	-
5.0	510	0.7819	0.4393	0.4338	0.4416	0.4295	0.3889
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6.0	612	0.8569	0.4473	0.4482	0.4504	0.4515	0.4138
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6.1765	630	3.311	-	-	-	-	-
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6.4804	661	0.725	-	-	-	-	-
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6.5	663	2.231	-	-	-	-	-
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6.5588	669	1.6513	-	-	-	-	-
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6.9510	709	0.725	-	-	-	-	-
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6.9804	712	3.9793	-	-	-	-	-
6.9902	713	0.3452	-	-	-	-	-
7.0	714	0.1112	0.4442	0.4357	0.4430	0.4392	0.4201
7.0098	715	0.1986	-	-	-	-	-
7.0196	716	0.1309	-	-	-	-	-
7.0294	717	0.302	-	-	-	-	-
7.0392	718	0.387	-	-	-	-	-
7.0490	719	0.6811	-	-	-	-	-
7.0588	720	0.1184	-	-	-	-	-
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7.0784	722	0.7094	-	-	-	-	-
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7.0980	724	0.8515	-	-	-	-	-
7.1078	725	0.1928	-	-	-	-	-
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7.1569	730	0.7258	-	-	-	-	-
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7.1765	732	0.5043	-	-	-	-	-
7.1863	733	0.3594	-	-	-	-	-
7.1961	734	0.3433	-	-	-	-	-
7.2059	735	1.247	-	-	-	-	-
7.2157	736	0.2146	-	-	-	-	-
7.2255	737	3.5853	-	-	-	-	-
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7.2549	740	2.8213	-	-	-	-	-
7.2647	741	0.5844	-	-	-	-	-
7.2745	742	0.7471	-	-	-	-	-
7.2843	743	0.4646	-	-	-	-	-
7.2941	744	0.1847	-	-	-	-	-
7.3039	745	0.1304	-	-	-	-	-
7.3137	746	2.071	-	-	-	-	-
7.3235	747	0.4216	-	-	-	-	-
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7.3529	750	0.0541	-	-	-	-	-
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7.4510	760	0.1901	-	-	-	-	-
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7.4804	763	0.263	-	-	-	-	-
7.4902	764	1.2797	-	-	-	-	-
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7.5098	766	3.6521	-	-	-	-	-
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7.5588	771	0.1919	-	-	-	-	-
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7.5784	773	0.3336	-	-	-	-	-
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7.5980	775	1.0271	-	-	-	-	-
7.6078	776	0.0699	-	-	-	-	-
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7.6275	778	1.5631	-	-	-	-	-
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7.7255	788	0.472	-	-	-	-	-
7.7353	789	0.3201	-	-	-	-	-
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7.8235	798	1.3512	-	-	-	-	-
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7.8431	800	2.3537	-	-	-	-	-
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7.9804	814	1.0919	-	-	-	-	-
7.9902	815	0.5894	-	-	-	-	-
8.0	816	0.5687	0.4367	0.4527	0.4446	0.4549	0.4209
8.0098	817	0.9448	-	-	-	-	-
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8.0294	819	2.0971	-	-	-	-	-
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8.1373	830	0.0229	-	-	-	-	-
8.1471	831	0.022	-	-	-	-	-
8.1569	832	0.2386	-	-	-	-	-
8.1667	833	0.2888	-	-	-	-	-
8.1765	834	1.4573	-	-	-	-	-
8.1863	835	0.246	-	-	-	-	-
8.1961	836	0.4084	-	-	-	-	-
8.2059	837	0.9884	-	-	-	-	-
8.2157	838	0.1843	-	-	-	-	-
8.2255	839	0.5776	-	-	-	-	-
8.2353	840	0.663	-	-	-	-	-
8.2451	841	0.1248	-	-	-	-	-
8.2549	842	0.3924	-	-	-	-	-
8.2647	843	0.4227	-	-	-	-	-
8.2745	844	0.0517	-	-	-	-	-
8.2843	845	1.421	-	-	-	-	-
8.2941	846	0.3923	-	-	-	-	-
8.3039	847	0.0566	-	-	-	-	-
8.3137	848	1.674	-	-	-	-	-
8.3235	849	1.7599	-	-	-	-	-
8.3333	850	0.236	-	-	-	-	-
8.3431	851	1.5853	-	-	-	-	-
8.3529	852	1.0122	-	-	-	-	-
8.3627	853	2.7049	-	-	-	-	-
8.3725	854	0.5979	-	-	-	-	-
8.3824	855	0.3934	-	-	-	-	-
8.3922	856	0.155	-	-	-	-	-
8.4020	857	0.0404	-	-	-	-	-
8.4118	858	0.1053	-	-	-	-	-
8.4216	859	0.7973	-	-	-	-	-
8.4314	860	1.0623	-	-	-	-	-
8.4412	861	0.2224	-	-	-	-	-
8.4510	862	0.498	-	-	-	-	-
8.4608	863	0.0506	-	-	-	-	-
8.4706	864	0.193	-	-	-	-	-
8.4804	865	1.1289	-	-	-	-	-
8.4902	866	1.8809	-	-	-	-	-
8.5	867	0.2647	-	-	-	-	-
8.5098	868	0.9137	-	-	-	-	-
8.5196	869	1.1054	-	-	-	-	-
8.5294	870	0.3431	-	-	-	-	-
8.5392	871	1.2004	-	-	-	-	-
8.5490	872	0.8382	-	-	-	-	-
8.5588	873	0.3734	-	-	-	-	-
8.5686	874	0.2123	-	-	-	-	-
8.5784	875	0.7461	-	-	-	-	-
8.5882	876	0.0573	-	-	-	-	-
8.5980	877	1.7312	-	-	-	-	-
8.6078	878	0.6657	-	-	-	-	-
8.6176	879	1.8817	-	-	-	-	-
8.6275	880	0.0719	-	-	-	-	-
8.6373	881	0.582	-	-	-	-	-
8.6471	882	0.1383	-	-	-	-	-
8.6569	883	1.1228	-	-	-	-	-
8.6667	884	1.4588	-	-	-	-	-
8.6765	885	0.1121	-	-	-	-	-
8.6863	886	0.08	-	-	-	-	-
8.6961	887	0.9132	-	-	-	-	-
8.7059	888	2.0485	-	-	-	-	-
8.7157	889	0.0602	-	-	-	-	-
8.7255	890	0.372	-	-	-	-	-
8.7353	891	2.6196	-	-	-	-	-
8.7451	892	1.088	-	-	-	-	-
8.7549	893	0.1426	-	-	-	-	-
8.7647	894	0.5937	-	-	-	-	-
8.7745	895	0.1557	-	-	-	-	-
8.7843	896	1.6383	-	-	-	-	-
8.7941	897	0.315	-	-	-	-	-
8.8039	898	0.6838	-	-	-	-	-
8.8137	899	0.3439	-	-	-	-	-
8.8235	900	2.3383	-	-	-	-	-
8.8333	901	0.1489	-	-	-	-	-
8.8431	902	1.9066	-	-	-	-	-
8.8529	903	0.6731	-	-	-	-	-
8.8627	904	1.4289	-	-	-	-	-
8.8725	905	0.1097	-	-	-	-	-
8.8824	906	0.2366	-	-	-	-	-
8.8922	907	0.5073	-	-	-	-	-
8.9020	908	1.0773	-	-	-	-	-
8.9118	909	0.0663	-	-	-	-	-
8.9216	910	0.0808	-	-	-	-	-
8.9314	911	0.1299	-	-	-	-	-
8.9412	912	0.2324	-	-	-	-	-
8.9510	913	1.4257	-	-	-	-	-
8.9608	914	0.1796	-	-	-	-	-
8.9706	915	1.702	-	-	-	-	-
8.9804	916	0.1128	-	-	-	-	-
8.9902	917	0.2039	-	-	-	-	-
9.0	918	0.0208	0.4497	0.4427	0.4490	0.4524	0.4320
9.0098	919	1.5254	-	-	-	-	-
9.0196	920	1.3278	-	-	-	-	-
9.0294	921	0.1509	-	-	-	-	-
9.0392	922	0.0821	-	-	-	-	-
9.0490	923	0.4457	-	-	-	-	-
9.0588	924	0.1536	-	-	-	-	-
9.0686	925	0.1446	-	-	-	-	-
9.0784	926	0.627	-	-	-	-	-
9.0882	927	0.4614	-	-	-	-	-
9.0980	928	0.658	-	-	-	-	-
9.1078	929	1.2732	-	-	-	-	-
9.1176	930	1.7738	-	-	-	-	-
9.1275	931	0.0963	-	-	-	-	-
9.1373	932	1.8892	-	-	-	-	-
9.1471	933	0.7154	-	-	-	-	-
9.1569	934	0.0751	-	-	-	-	-
9.1667	935	0.2999	-	-	-	-	-
9.1765	936	0.5683	-	-	-	-	-
9.1863	937	0.7485	-	-	-	-	-
9.1961	938	0.3099	-	-	-	-	-
9.2059	939	0.7234	-	-	-	-	-
9.2157	940	0.3434	-	-	-	-	-
9.2255	941	1.1842	-	-	-	-	-
9.2353	942	1.3713	-	-	-	-	-
9.2451	943	0.1505	-	-	-	-	-
9.2549	944	1.3096	-	-	-	-	-
9.2647	945	2.1745	-	-	-	-	-
9.2745	946	0.0571	-	-	-	-	-
9.2843	947	0.4994	-	-	-	-	-
9.2941	948	0.2938	-	-	-	-	-
9.3039	949	1.3117	-	-	-	-	-
9.3137	950	1.3904	-	-	-	-	-
9.3235	951	0.2781	-	-	-	-	-
9.3333	952	0.073	-	-	-	-	-
9.3431	953	0.4679	-	-	-	-	-
9.3529	954	0.0644	-	-	-	-	-
9.3627	955	0.0382	-	-	-	-	-
9.3725	956	0.4062	-	-	-	-	-
9.3824	957	0.1543	-	-	-	-	-
9.3922	958	1.5961	-	-	-	-	-
9.4020	959	0.887	-	-	-	-	-
9.4118	960	0.345	-	-	-	-	-
9.4216	961	0.4368	-	-	-	-	-
9.4314	962	0.485	-	-	-	-	-
9.4412	963	0.0532	-	-	-	-	-
9.4510	964	0.0596	-	-	-	-	-
9.4608	965	0.5338	-	-	-	-	-
9.4706	966	4.2175	-	-	-	-	-
9.4804	967	0.1782	-	-	-	-	-
9.4902	968	0.2971	-	-	-	-	-
9.5	969	0.0347	-	-	-	-	-
9.5098	970	0.1772	-	-	-	-	-
9.5196	971	0.3312	-	-	-	-	-
9.5294	972	1.0914	-	-	-	-	-
9.5392	973	0.5447	-	-	-	-	-
9.5490	974	0.6363	-	-	-	-	-
9.5588	975	0.1738	-	-	-	-	-
9.5686	976	0.6424	-	-	-	-	-
9.5784	977	0.0752	-	-	-	-	-
9.5882	978	0.7333	-	-	-	-	-
9.5980	979	0.5427	-	-	-	-	-
9.6078	980	0.1689	-	-	-	-	-
9.6176	981	0.6654	-	-	-	-	-
9.6275	982	0.5247	-	-	-	-	-
9.6373	983	0.4657	-	-	-	-	-
9.6471	984	0.8176	-	-	-	-	-
9.6569	985	0.1689	-	-	-	-	-
9.6667	986	0.5954	-	-	-	-	-
9.6765	987	0.832	-	-	-	-	-
9.6863	988	1.0878	-	-	-	-	-
9.6961	989	0.1918	-	-	-	-	-
9.7059	990	1.0797	-	-	-	-	-
9.7157	991	0.7171	-	-	-	-	-
9.7255	992	0.0529	-	-	-	-	-
9.7353	993	0.717	-	-	-	-	-
9.7451	994	1.3604	-	-	-	-	-
9.7549	995	0.1234	-	-	-	-	-
9.7647	996	0.4606	-	-	-	-	-
9.7745	997	0.148	-	-	-	-	-
9.7843	998	0.58	-	-	-	-	-
9.7941	999	0.9918	-	-	-	-	-
9.8039	1000	0.4089	-	-	-	-	-
9.8137	1001	0.3936	-	-	-	-	-
9.8235	1002	0.0753	-	-	-	-	-
9.8333	1003	0.1202	-	-	-	-	-
9.8431	1004	0.191	-	-	-	-	-
9.8529	1005	1.8437	-	-	-	-	-
9.8627	1006	1.2283	-	-	-	-	-
9.8725	1007	0.526	-	-	-	-	-
9.8824	1008	0.0851	-	-	-	-	-
9.8922	1009	0.9696	-	-	-	-	-
9.9020	1010	0.0735	-	-	-	-	-
9.9118	1011	0.4334	-	-	-	-	-
9.9216	1012	1.8229	-	-	-	-	-
9.9314	1013	0.4248	-	-	-	-	-
9.9412	1014	0.4024	-	-	-	-	-
9.9510	1015	0.6838	-	-	-	-	-
9.9608	1016	0.1111	-	-	-	-	-
9.9706	1017	0.1799	-	-	-	-	-
9.9804	1018	0.3433	-	-	-	-	-
9.9902	1019	0.1694	-	-	-	-	-
10.0	1020	0.1874	0.4507	0.4440	0.4482	0.4543	0.4304

• 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}
}