IoannisKat1/all-mpnet-base-v2-ft-new

all_mpnet_base_v2 Finetuned on Data

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
• Maximum Sequence Length: 384 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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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})
  (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 = [
    'For which purposes should data concerning health not be processed by third parties?',
    'The processing of special categories of personal data may be necessary for reasons of public interest in the areas of public health without consent of the data subject. Such processing should be subject to suitable and specific measures so as to protect the rights and freedoms of natural persons. In that context, ‘public health’ should be interpreted as defined in Regulation (EC) No 1338/2008 of the European Parliament and of the Council (1), namely all elements related to health, namely health status, including morbidity and disability, the determinants having an effect on that health status, health care needs, resources allocated to health care, the provision of, and universal access to, health care as well as health care expenditure and financing, and the causes of mortality. Such processing of data concerning health for reasons of public interest should not result in personal data being processed for other purposes by third parties such as employers or insurance and banking companies.',
    '**Court (Civil/Criminal): Criminal**  \n**Provisions:**  \n**Time of commission of the act:**  \n**Result (not guilty, guilty):** Guilty  \n**Rationale:** Declares the defendant guilty of the following: In Athens, between the period from July 25, 2005, to July 26, 2005, for profit and with the intent to conceal the true origin and provide assistance to a person involved in criminal activity, he intentionally concealed any assets and became, in any case, the beneficiary of those assets, which originated from the aforementioned activity, namely fraud through the manipulation of computer data, from which the damage caused is particularly significant, the perpetrator committing professional fraud, and the circumstances under which this act was carried out indicating that he is particularly dangerous. He engages in activities related to money laundering from criminal activity as a profession and is particularly dangerous. Specifically: An individual whose identity is unknown had violated the communication codes of .... and ... with Bank ..... (password, passport details via the Internet) within the framework of electronic banking transactions provided by the aforementioned bank and on June 20, 2005, July 12, 2005, and July 25, 2005, knowingly falsely presented himself to employees of Bank ..... over the phone as the ..... beneficiary of the ..... account at this bank, knowing the password and account number. Thus, on June 20, 2005, he succeeded, through the appropriate employees, in changing the phone number at the Bank to his own .... On July 12, 2005, he verified the change of the phone number, and on July 25, 2005, he read a transfer request to Greece for the amount of $427,620 with account number .... to a bank employee, and by changing the password for online banking transactions and knowing the user identity ....., he managed to convince the relevant employee to disclose the account balance of ..... which was $629,473.04. In this way, he convinced the relevant employee of Bank ..... to proceed with the transfer of the amount of $427,620 from the account of ..... to Bank ...... in Greece, specifically to the ...... account, with the transfer request made via email from the online banking system.  \n**Actual incidents:**',
]
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,  0.6007, -0.1042],
#         [ 0.6007,  1.0000, -0.0270],
#         [-0.1042, -0.0270,  1.0000]])

Evaluation

Metrics

Information Retrieval

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

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.43
cosine_accuracy@3	0.4595
cosine_accuracy@5	0.5086
cosine_accuracy@10	0.5528
cosine_precision@1	0.43
cosine_precision@3	0.4136
cosine_precision@5	0.3936
cosine_precision@10	0.3511
cosine_recall@1	0.0835
cosine_recall@3	0.2094
cosine_recall@5	0.2886
cosine_recall@10	0.3988
cosine_ndcg@10	0.4868
cosine_mrr@10	0.4574
cosine_map@100	0.547

Information Retrieval

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

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.43
cosine_accuracy@3	0.457
cosine_accuracy@5	0.4963
cosine_accuracy@10	0.543
cosine_precision@1	0.43
cosine_precision@3	0.4136
cosine_precision@5	0.3902
cosine_precision@10	0.3491
cosine_recall@1	0.0816
cosine_recall@3	0.2063
cosine_recall@5	0.279
cosine_recall@10	0.3912
cosine_ndcg@10	0.4823
cosine_mrr@10	0.4548
cosine_map@100	0.5405

Information Retrieval

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

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.4324
cosine_accuracy@3	0.4545
cosine_accuracy@5	0.4939
cosine_accuracy@10	0.5332
cosine_precision@1	0.4324
cosine_precision@3	0.4161
cosine_precision@5	0.3946
cosine_precision@10	0.3482
cosine_recall@1	0.0805
cosine_recall@3	0.2001
cosine_recall@5	0.2755
cosine_recall@10	0.3883
cosine_ndcg@10	0.4792
cosine_mrr@10	0.4546
cosine_map@100	0.5365

Information Retrieval

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

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.4054
cosine_accuracy@3	0.4373
cosine_accuracy@5	0.4767
cosine_accuracy@10	0.5209
cosine_precision@1	0.4054
cosine_precision@3	0.3956
cosine_precision@5	0.3789
cosine_precision@10	0.3403
cosine_recall@1	0.0735
cosine_recall@3	0.1879
cosine_recall@5	0.2618
cosine_recall@10	0.3803
cosine_ndcg@10	0.46
cosine_mrr@10	0.4312
cosine_map@100	0.5151

Information Retrieval

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

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.3636
cosine_accuracy@3	0.3882
cosine_accuracy@5	0.4275
cosine_accuracy@10	0.4693
cosine_precision@1	0.3636
cosine_precision@3	0.3538
cosine_precision@5	0.3391
cosine_precision@10	0.3029
cosine_recall@1	0.0653
cosine_recall@3	0.1706
cosine_recall@5	0.2407
cosine_recall@10	0.3419
cosine_ndcg@10	0.4123
cosine_mrr@10	0.387
cosine_map@100	0.4714

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: 15.62 tokens max: 34 tokens	min: 25 tokens mean: 295.23 tokens max: 384 tokens

• Samples:

  anchor	positive
  Which safeguard is recommended when processing data for specific purposes?	The processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes should be subject to appropriate safeguards for the rights and freedoms of the data subject pursuant to this Regulation. Those safeguards should ensure that technical and organisational measures are in place in order to ensure, in particular, the principle of data minimisation. The further processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes is to be carried out when the controller has assessed the feasibility to fulfil those purposes by processing data which do not permit or no longer permit the identification of data subjects, provided that appropriate safeguards exist (such as, for instance, pseudonymisation of the data). Member States should provide for appropriate safeguards for the processing of personal data for archiving purposes in the public int...
  Under what circumstances should processing be lawful?	Processing should be lawful where it is necessary in the context of a contract or the intention to enter into a contract.
  How long can personal data be stored for archiving purposes?	1.Personal data shall be: (a) processed lawfully, fairly and in a transparent manner in relation to the data subject (‘lawfulness, fairness and transparency’); (b) collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes; further processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes shall, in accordance with Article 89(1), not be considered to be incompatible with the initial purposes (‘purpose limitation’); (c) adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed (‘data minimisation’); (d) accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purposes for which they are processed, are erased or rectified without delay (‘accuracy’); 4.5.2016 L 119/35 (1) Directive (EU) 2015/153...

• 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	10.5062	-	-	-	-	-
0.0196	2	15.1252	-	-	-	-	-
0.0294	3	12.1261	-	-	-	-	-
0.0392	4	17.0626	-	-	-	-	-
0.0490	5	11.8792	-	-	-	-	-
0.0588	6	14.7209	-	-	-	-	-
0.0686	7	7.5974	-	-	-	-	-
0.0784	8	14.7475	-	-	-	-	-
0.0882	9	11.0845	-	-	-	-	-
0.0980	10	21.555	-	-	-	-	-
0.1078	11	8.9107	-	-	-	-	-
0.1176	12	10.4711	-	-	-	-	-
0.1275	13	12.3044	-	-	-	-	-
0.1373	14	16.7416	-	-	-	-	-
0.1471	15	10.9581	-	-	-	-	-
0.1569	16	10.2429	-	-	-	-	-
0.1667	17	10.337	-	-	-	-	-
0.1765	18	12.6871	-	-	-	-	-
0.1863	19	12.0037	-	-	-	-	-
0.1961	20	8.7042	-	-	-	-	-
0.2059	21	6.2856	-	-	-	-	-
0.2157	22	12.2504	-	-	-	-	-
0.2255	23	6.5664	-	-	-	-	-
0.2353	24	6.9022	-	-	-	-	-
0.2451	25	13.2137	-	-	-	-	-
0.2549	26	8.649	-	-	-	-	-
0.2647	27	10.2548	-	-	-	-	-
0.2745	28	8.937	-	-	-	-	-
0.2843	29	6.863	-	-	-	-	-
0.2941	30	11.6154	-	-	-	-	-
0.3039	31	3.0374	-	-	-	-	-
0.3137	32	5.6845	-	-	-	-	-
0.3235	33	9.6348	-	-	-	-	-
0.3333	34	10.1713	-	-	-	-	-
0.3431	35	17.1706	-	-	-	-	-
0.3529	36	8.8354	-	-	-	-	-
0.3627	37	9.5873	-	-	-	-	-
0.3725	38	6.0446	-	-	-	-	-
0.3824	39	8.9629	-	-	-	-	-
0.3922	40	11.5594	-	-	-	-	-
0.4020	41	14.5652	-	-	-	-	-
0.4118	42	8.6806	-	-	-	-	-
0.4216	43	12.5203	-	-	-	-	-
0.4314	44	15.8328	-	-	-	-	-
0.4412	45	7.9913	-	-	-	-	-
0.4510	46	9.9815	-	-	-	-	-
0.4608	47	7.6775	-	-	-	-	-
0.4706	48	8.6495	-	-	-	-	-
0.4804	49	7.4536	-	-	-	-	-
0.4902	50	3.8248	-	-	-	-	-
0.5	51	2.9394	-	-	-	-	-
0.5098	52	13.4557	-	-	-	-	-
0.5196	53	4.4811	-	-	-	-	-
0.5294	54	3.6653	-	-	-	-	-
0.5392	55	7.9698	-	-	-	-	-
0.5490	56	8.8599	-	-	-	-	-
0.5588	57	9.6441	-	-	-	-	-
0.5686	58	6.4397	-	-	-	-	-
0.5784	59	4.8391	-	-	-	-	-
0.5882	60	8.1203	-	-	-	-	-
0.5980	61	5.685	-	-	-	-	-
0.6078	62	9.6878	-	-	-	-	-
0.6176	63	10.0697	-	-	-	-	-
0.6275	64	4.4702	-	-	-	-	-
0.6373	65	7.2568	-	-	-	-	-
0.6471	66	5.8591	-	-	-	-	-
0.6569	67	9.7748	-	-	-	-	-
0.6667	68	11.7127	-	-	-	-	-
0.6765	69	16.1343	-	-	-	-	-
0.6863	70	4.5088	-	-	-	-	-
0.6961	71	7.5293	-	-	-	-	-
0.7059	72	10.6862	-	-	-	-	-
0.7157	73	6.1222	-	-	-	-	-
0.7255	74	9.2219	-	-	-	-	-
0.7353	75	3.6964	-	-	-	-	-
0.7451	76	10.8735	-	-	-	-	-
0.7549	77	5.1895	-	-	-	-	-
0.7647	78	7.5445	-	-	-	-	-
0.7745	79	5.855	-	-	-	-	-
0.7843	80	9.5987	-	-	-	-	-
0.7941	81	4.6386	-	-	-	-	-
0.8039	82	6.251	-	-	-	-	-
0.8137	83	6.8899	-	-	-	-	-
0.8235	84	9.238	-	-	-	-	-
0.8333	85	7.2555	-	-	-	-	-
0.8431	86	6.436	-	-	-	-	-
0.8529	87	3.2206	-	-	-	-	-
0.8627	88	7.0912	-	-	-	-	-
0.8725	89	5.0378	-	-	-	-	-
0.8824	90	5.2964	-	-	-	-	-
0.8922	91	6.1904	-	-	-	-	-
0.9020	92	8.9955	-	-	-	-	-
0.9118	93	7.0209	-	-	-	-	-
0.9216	94	2.8927	-	-	-	-	-
0.9314	95	4.0125	-	-	-	-	-
0.9412	96	10.5064	-	-	-	-	-
0.9510	97	3.8146	-	-	-	-	-
0.9608	98	3.8792	-	-	-	-	-
0.9706	99	2.945	-	-	-	-	-
0.9804	100	2.4559	-	-	-	-	-
0.9902	101	6.0063	-	-	-	-	-
1.0	102	3.8832	0.4076	0.3996	0.3868	0.3644	0.3114
1.0098	103	6.5085	-	-	-	-	-
1.0196	104	6.4525	-	-	-	-	-
1.0294	105	4.0882	-	-	-	-	-
1.0392	106	5.4989	-	-	-	-	-
1.0490	107	10.2571	-	-	-	-	-
1.0588	108	4.3575	-	-	-	-	-
1.0686	109	3.02	-	-	-	-	-
1.0784	110	9.8081	-	-	-	-	-
1.0882	111	2.9395	-	-	-	-	-
1.0980	112	4.8773	-	-	-	-	-
1.1078	113	4.2096	-	-	-	-	-
1.1176	114	3.0304	-	-	-	-	-
1.1275	115	0.9558	-	-	-	-	-
1.1373	116	4.212	-	-	-	-	-
1.1471	117	2.8502	-	-	-	-	-
1.1569	118	3.9223	-	-	-	-	-
1.1667	119	6.6211	-	-	-	-	-
1.1765	120	7.9361	-	-	-	-	-
1.1863	121	3.6789	-	-	-	-	-
1.1961	122	3.881	-	-	-	-	-
1.2059	123	6.4585	-	-	-	-	-
1.2157	124	2.6327	-	-	-	-	-
1.2255	125	3.6137	-	-	-	-	-
1.2353	126	5.5995	-	-	-	-	-
1.2451	127	3.4795	-	-	-	-	-
1.2549	128	4.0016	-	-	-	-	-
1.2647	129	3.9866	-	-	-	-	-
1.2745	130	3.2067	-	-	-	-	-
1.2843	131	3.0401	-	-	-	-	-
1.2941	132	1.4515	-	-	-	-	-
1.3039	133	3.3002	-	-	-	-	-
1.3137	134	1.9871	-	-	-	-	-
1.3235	135	2.9048	-	-	-	-	-
1.3333	136	7.5579	-	-	-	-	-
1.3431	137	3.1444	-	-	-	-	-
1.3529	138	3.946	-	-	-	-	-
1.3627	139	4.425	-	-	-	-	-
1.3725	140	3.3572	-	-	-	-	-
1.3824	141	4.284	-	-	-	-	-
1.3922	142	2.0	-	-	-	-	-
1.4020	143	1.6336	-	-	-	-	-
1.4118	144	4.8296	-	-	-	-	-
1.4216	145	4.1713	-	-	-	-	-
1.4314	146	7.2047	-	-	-	-	-
1.4412	147	3.2369	-	-	-	-	-
1.4510	148	5.0459	-	-	-	-	-
1.4608	149	4.3548	-	-	-	-	-
1.4706	150	2.1277	-	-	-	-	-
1.4804	151	7.0513	-	-	-	-	-
1.4902	152	1.5996	-	-	-	-	-
1.5	153	3.6716	-	-	-	-	-
1.5098	154	7.5217	-	-	-	-	-
1.5196	155	4.5551	-	-	-	-	-
1.5294	156	8.9013	-	-	-	-	-
1.5392	157	3.7938	-	-	-	-	-
1.5490	158	6.5032	-	-	-	-	-
1.5588	159	4.3208	-	-	-	-	-
1.5686	160	2.4914	-	-	-	-	-
1.5784	161	8.1273	-	-	-	-	-
1.5882	162	7.925	-	-	-	-	-
1.5980	163	3.5399	-	-	-	-	-
1.6078	164	4.3323	-	-	-	-	-
1.6176	165	4.8124	-	-	-	-	-
1.6275	166	8.3933	-	-	-	-	-
1.6373	167	5.1423	-	-	-	-	-
1.6471	168	6.426	-	-	-	-	-
1.6569	169	3.863	-	-	-	-	-
1.6667	170	5.3783	-	-	-	-	-
1.6765	171	7.1191	-	-	-	-	-
1.6863	172	4.0557	-	-	-	-	-
1.6961	173	2.4756	-	-	-	-	-
1.7059	174	7.0071	-	-	-	-	-
1.7157	175	7.8456	-	-	-	-	-
1.7255	176	5.7382	-	-	-	-	-
1.7353	177	8.2471	-	-	-	-	-
1.7451	178	2.7506	-	-	-	-	-
1.7549	179	3.6095	-	-	-	-	-
1.7647	180	4.4643	-	-	-	-	-
1.7745	181	2.1244	-	-	-	-	-
1.7843	182	7.962	-	-	-	-	-
1.7941	183	5.668	-	-	-	-	-
1.8039	184	8.8238	-	-	-	-	-
1.8137	185	4.0232	-	-	-	-	-
1.8235	186	5.0952	-	-	-	-	-
1.8333	187	3.1376	-	-	-	-	-
1.8431	188	6.6129	-	-	-	-	-
1.8529	189	1.166	-	-	-	-	-
1.8627	190	3.6496	-	-	-	-	-
1.8725	191	4.4133	-	-	-	-	-
1.8824	192	4.6314	-	-	-	-	-
1.8922	193	5.7002	-	-	-	-	-
1.9020	194	3.7733	-	-	-	-	-
1.9118	195	4.792	-	-	-	-	-
1.9216	196	7.8003	-	-	-	-	-
1.9314	197	3.4749	-	-	-	-	-
1.9412	198	5.7873	-	-	-	-	-
1.9510	199	4.0242	-	-	-	-	-
1.9608	200	2.7828	-	-	-	-	-
1.9706	201	2.2991	-	-	-	-	-
1.9804	202	4.1092	-	-	-	-	-
1.9902	203	6.9827	-	-	-	-	-
2.0	204	4.1257	0.4289	0.4273	0.4215	0.4160	0.3520
2.0098	205	2.4646	-	-	-	-	-
2.0196	206	3.3283	-	-	-	-	-
2.0294	207	0.2036	-	-	-	-	-
2.0392	208	3.4185	-	-	-	-	-
2.0490	209	1.3486	-	-	-	-	-
2.0588	210	6.3235	-	-	-	-	-
2.0686	211	3.6837	-	-	-	-	-
2.0784	212	2.4766	-	-	-	-	-
2.0882	213	1.7008	-	-	-	-	-
2.0980	214	1.1425	-	-	-	-	-
2.1078	215	3.1866	-	-	-	-	-
2.1176	216	1.5131	-	-	-	-	-
2.1275	217	1.1845	-	-	-	-	-
2.1373	218	1.4116	-	-	-	-	-
2.1471	219	1.1628	-	-	-	-	-
2.1569	220	2.3009	-	-	-	-	-
2.1667	221	3.6178	-	-	-	-	-
2.1765	222	2.9634	-	-	-	-	-
2.1863	223	2.6099	-	-	-	-	-
2.1961	224	2.3901	-	-	-	-	-
2.2059	225	1.8718	-	-	-	-	-
2.2157	226	0.8301	-	-	-	-	-
2.2255	227	2.2337	-	-	-	-	-
2.2353	228	1.763	-	-	-	-	-
2.2451	229	1.6571	-	-	-	-	-
2.2549	230	1.5607	-	-	-	-	-
2.2647	231	1.5189	-	-	-	-	-
2.2745	232	1.3395	-	-	-	-	-
2.2843	233	2.2397	-	-	-	-	-
2.2941	234	8.3358	-	-	-	-	-
2.3039	235	5.4402	-	-	-	-	-
2.3137	236	2.3583	-	-	-	-	-
2.3235	237	2.9728	-	-	-	-	-
2.3333	238	3.2368	-	-	-	-	-
2.3431	239	5.3349	-	-	-	-	-
2.3529	240	5.1133	-	-	-	-	-
2.3627	241	5.432	-	-	-	-	-
2.3725	242	2.4747	-	-	-	-	-
2.3824	243	2.5203	-	-	-	-	-
2.3922	244	1.6055	-	-	-	-	-
2.4020	245	2.0632	-	-	-	-	-
2.4118	246	3.3429	-	-	-	-	-
2.4216	247	3.3352	-	-	-	-	-
2.4314	248	1.468	-	-	-	-	-
2.4412	249	1.4636	-	-	-	-	-
2.4510	250	6.2413	-	-	-	-	-
2.4608	251	0.986	-	-	-	-	-
2.4706	252	5.1781	-	-	-	-	-
2.4804	253	5.57	-	-	-	-	-
2.4902	254	1.8424	-	-	-	-	-
2.5	255	0.7476	-	-	-	-	-
2.5098	256	4.7886	-	-	-	-	-
2.5196	257	7.4943	-	-	-	-	-
2.5294	258	4.9764	-	-	-	-	-
2.5392	259	2.5229	-	-	-	-	-
2.5490	260	1.2107	-	-	-	-	-
2.5588	261	3.7841	-	-	-	-	-
2.5686	262	4.2538	-	-	-	-	-
2.5784	263	5.0431	-	-	-	-	-
2.5882	264	4.0782	-	-	-	-	-
2.5980	265	2.6999	-	-	-	-	-
2.6078	266	2.0595	-	-	-	-	-
2.6176	267	2.8177	-	-	-	-	-
2.6275	268	1.4411	-	-	-	-	-
2.6373	269	3.2591	-	-	-	-	-
2.6471	270	5.6571	-	-	-	-	-
2.6569	271	6.8458	-	-	-	-	-
2.6667	272	4.455	-	-	-	-	-
2.6765	273	3.7889	-	-	-	-	-
2.6863	274	2.0797	-	-	-	-	-
2.6961	275	3.2366	-	-	-	-	-
2.7059	276	5.5029	-	-	-	-	-
2.7157	277	2.4614	-	-	-	-	-
2.7255	278	1.5427	-	-	-	-	-
2.7353	279	1.0222	-	-	-	-	-
2.7451	280	0.7948	-	-	-	-	-
2.7549	281	5.1751	-	-	-	-	-
2.7647	282	3.6797	-	-	-	-	-
2.7745	283	2.1421	-	-	-	-	-
2.7843	284	5.18	-	-	-	-	-
2.7941	285	1.2921	-	-	-	-	-
2.8039	286	2.4567	-	-	-	-	-
2.8137	287	3.4246	-	-	-	-	-
2.8235	288	3.7379	-	-	-	-	-
2.8333	289	2.637	-	-	-	-	-
2.8431	290	1.9286	-	-	-	-	-
2.8529	291	3.7831	-	-	-	-	-
2.8627	292	1.959	-	-	-	-	-
2.8725	293	4.8515	-	-	-	-	-
2.8824	294	3.3372	-	-	-	-	-
2.8922	295	2.2748	-	-	-	-	-
2.9020	296	2.3427	-	-	-	-	-
2.9118	297	3.3426	-	-	-	-	-
2.9216	298	3.7352	-	-	-	-	-
2.9314	299	1.6633	-	-	-	-	-
2.9412	300	1.6348	-	-	-	-	-
2.9510	301	2.9441	-	-	-	-	-
2.9608	302	1.9479	-	-	-	-	-
2.9706	303	2.1411	-	-	-	-	-
2.9804	304	4.0641	-	-	-	-	-
2.9902	305	2.8836	-	-	-	-	-
3.0	306	3.1764	0.4565	0.4562	0.4552	0.4166	0.3926
3.0098	307	1.8779	-	-	-	-	-
3.0196	308	1.3571	-	-	-	-	-
3.0294	309	1.2313	-	-	-	-	-
3.0392	310	2.886	-	-	-	-	-
3.0490	311	1.017	-	-	-	-	-
3.0588	312	1.5891	-	-	-	-	-
3.0686	313	2.8195	-	-	-	-	-
3.0784	314	1.0608	-	-	-	-	-
3.0882	315	1.5186	-	-	-	-	-
3.0980	316	1.9838	-	-	-	-	-
3.1078	317	0.9633	-	-	-	-	-
3.1176	318	1.2909	-	-	-	-	-
3.1275	319	0.341	-	-	-	-	-
3.1373	320	1.6501	-	-	-	-	-
3.1471	321	1.957	-	-	-	-	-
3.1569	322	1.3453	-	-	-	-	-
3.1667	323	0.7173	-	-	-	-	-
3.1765	324	0.7919	-	-	-	-	-
3.1863	325	0.9661	-	-	-	-	-
3.1961	326	1.9469	-	-	-	-	-
3.2059	327	1.1932	-	-	-	-	-
3.2157	328	1.374	-	-	-	-	-
3.2255	329	1.1796	-	-	-	-	-
3.2353	330	1.9485	-	-	-	-	-
3.2451	331	2.7915	-	-	-	-	-
3.2549	332	1.8929	-	-	-	-	-
3.2647	333	0.5911	-	-	-	-	-
3.2745	334	0.6787	-	-	-	-	-
3.2843	335	5.4034	-	-	-	-	-
3.2941	336	1.7276	-	-	-	-	-
3.3039	337	3.9991	-	-	-	-	-
3.3137	338	0.5633	-	-	-	-	-
3.3235	339	0.917	-	-	-	-	-
3.3333	340	1.2998	-	-	-	-	-
3.3431	341	2.4185	-	-	-	-	-
3.3529	342	1.2228	-	-	-	-	-
3.3627	343	2.8731	-	-	-	-	-
3.3725	344	1.2905	-	-	-	-	-
3.3824	345	1.1197	-	-	-	-	-
3.3922	346	2.4175	-	-	-	-	-
3.4020	347	1.6228	-	-	-	-	-
3.4118	348	1.3037	-	-	-	-	-
3.4216	349	1.0706	-	-	-	-	-
3.4314	350	2.1008	-	-	-	-	-
3.4412	351	1.1668	-	-	-	-	-
3.4510	352	1.2096	-	-	-	-	-
3.4608	353	0.8392	-	-	-	-	-
3.4706	354	3.2651	-	-	-	-	-
3.4804	355	1.6766	-	-	-	-	-
3.4902	356	0.3395	-	-	-	-	-
3.5	357	1.2441	-	-	-	-	-
3.5098	358	1.4444	-	-	-	-	-
3.5196	359	10.1679	-	-	-	-	-
3.5294	360	1.9789	-	-	-	-	-
3.5392	361	1.2966	-	-	-	-	-
3.5490	362	2.5762	-	-	-	-	-
3.5588	363	1.933	-	-	-	-	-
3.5686	364	3.0901	-	-	-	-	-
3.5784	365	2.5007	-	-	-	-	-
3.5882	366	2.7827	-	-	-	-	-
3.5980	367	1.7199	-	-	-	-	-
3.6078	368	1.1736	-	-	-	-	-
3.6176	369	3.3356	-	-	-	-	-
3.6275	370	0.7246	-	-	-	-	-
3.6373	371	2.4905	-	-	-	-	-
3.6471	372	1.5726	-	-	-	-	-
3.6569	373	1.3818	-	-	-	-	-
3.6667	374	0.2448	-	-	-	-	-
3.6765	375	0.4391	-	-	-	-	-
3.6863	376	1.1748	-	-	-	-	-
3.6961	377	7.2923	-	-	-	-	-
3.7059	378	0.3368	-	-	-	-	-
3.7157	379	0.43	-	-	-	-	-
3.7255	380	3.3452	-	-	-	-	-
3.7353	381	2.918	-	-	-	-	-
3.7451	382	1.801	-	-	-	-	-
3.7549	383	2.8407	-	-	-	-	-
3.7647	384	2.4272	-	-	-	-	-
3.7745	385	0.9349	-	-	-	-	-
3.7843	386	2.4791	-	-	-	-	-
3.7941	387	2.1666	-	-	-	-	-
3.8039	388	1.7717	-	-	-	-	-
3.8137	389	1.5936	-	-	-	-	-
3.8235	390	0.8187	-	-	-	-	-
3.8333	391	4.0269	-	-	-	-	-
3.8431	392	3.6645	-	-	-	-	-
3.8529	393	2.3922	-	-	-	-	-
3.8627	394	0.1943	-	-	-	-	-
3.8725	395	0.7069	-	-	-	-	-
3.8824	396	1.0205	-	-	-	-	-
3.8922	397	3.2399	-	-	-	-	-
3.9020	398	3.8463	-	-	-	-	-
3.9118	399	0.5511	-	-	-	-	-
3.9216	400	1.0245	-	-	-	-	-
3.9314	401	1.4659	-	-	-	-	-
3.9412	402	4.1376	-	-	-	-	-
3.9510	403	0.2403	-	-	-	-	-
3.9608	404	3.6992	-	-	-	-	-
3.9706	405	3.7554	-	-	-	-	-
3.9804	406	1.2881	-	-	-	-	-
3.9902	407	3.4056	-	-	-	-	-
4.0	408	0.7261	0.4582	0.4675	0.4607	0.4289	0.3945
4.0098	409	0.2358	-	-	-	-	-
4.0196	410	0.9315	-	-	-	-	-
4.0294	411	0.1774	-	-	-	-	-
4.0392	412	1.7475	-	-	-	-	-
4.0490	413	3.6127	-	-	-	-	-
4.0588	414	3.0253	-	-	-	-	-
4.0686	415	0.3536	-	-	-	-	-
4.0784	416	3.6735	-	-	-	-	-
4.0882	417	1.4067	-	-	-	-	-
4.0980	418	1.8275	-	-	-	-	-
4.1078	419	1.2235	-	-	-	-	-
4.1176	420	1.8925	-	-	-	-	-
4.1275	421	0.8721	-	-	-	-	-
4.1373	422	0.1328	-	-	-	-	-
4.1471	423	1.9969	-	-	-	-	-
4.1569	424	0.8856	-	-	-	-	-
4.1667	425	0.5358	-	-	-	-	-
4.1765	426	2.5099	-	-	-	-	-
4.1863	427	0.5634	-	-	-	-	-
4.1961	428	0.1712	-	-	-	-	-
4.2059	429	3.9052	-	-	-	-	-
4.2157	430	0.6288	-	-	-	-	-
4.2255	431	1.0672	-	-	-	-	-
4.2353	432	1.1448	-	-	-	-	-
4.2451	433	1.1989	-	-	-	-	-
4.2549	434	1.7888	-	-	-	-	-
4.2647	435	1.3741	-	-	-	-	-
4.2745	436	1.4119	-	-	-	-	-
4.2843	437	0.8146	-	-	-	-	-
4.2941	438	0.6838	-	-	-	-	-
4.3039	439	0.8096	-	-	-	-	-
4.3137	440	1.7432	-	-	-	-	-
4.3235	441	1.4242	-	-	-	-	-
4.3333	442	4.039	-	-	-	-	-
4.3431	443	0.2364	-	-	-	-	-
4.3529	444	0.6241	-	-	-	-	-
4.3627	445	2.3781	-	-	-	-	-
4.3725	446	1.1928	-	-	-	-	-
4.3824	447	1.2822	-	-	-	-	-
4.3922	448	1.445	-	-	-	-	-
4.4020	449	0.8603	-	-	-	-	-
4.4118	450	2.7241	-	-	-	-	-
4.4216	451	1.9318	-	-	-	-	-
4.4314	452	0.6398	-	-	-	-	-
4.4412	453	1.0932	-	-	-	-	-
4.4510	454	1.3003	-	-	-	-	-
4.4608	455	3.0352	-	-	-	-	-
4.4706	456	0.4492	-	-	-	-	-
4.4804	457	4.665	-	-	-	-	-
4.4902	458	1.7073	-	-	-	-	-
4.5	459	0.3467	-	-	-	-	-
4.5098	460	0.8215	-	-	-	-	-
4.5196	461	3.1267	-	-	-	-	-
4.5294	462	0.2262	-	-	-	-	-
4.5392	463	0.5577	-	-	-	-	-
4.5490	464	1.521	-	-	-	-	-
4.5588	465	1.792	-	-	-	-	-
4.5686	466	0.2396	-	-	-	-	-
4.5784	467	0.6672	-	-	-	-	-
4.5882	468	1.5579	-	-	-	-	-
4.5980	469	0.4134	-	-	-	-	-
4.6078	470	1.8965	-	-	-	-	-
4.6176	471	1.6115	-	-	-	-	-
4.6275	472	1.0135	-	-	-	-	-
4.6373	473	4.3298	-	-	-	-	-
4.6471	474	0.8401	-	-	-	-	-
4.6569	475	1.1574	-	-	-	-	-
4.6667	476	1.2617	-	-	-	-	-
4.6765	477	0.0673	-	-	-	-	-
4.6863	478	1.1267	-	-	-	-	-
4.6961	479	0.9391	-	-	-	-	-
4.7059	480	2.2109	-	-	-	-	-
4.7157	481	1.7302	-	-	-	-	-
4.7255	482	1.0692	-	-	-	-	-
4.7353	483	3.0713	-	-	-	-	-
4.7451	484	2.207	-	-	-	-	-
4.7549	485	1.7978	-	-	-	-	-
4.7647	486	0.7705	-	-	-	-	-
4.7745	487	2.9775	-	-	-	-	-
4.7843	488	1.2694	-	-	-	-	-
4.7941	489	0.8635	-	-	-	-	-
4.8039	490	2.2139	-	-	-	-	-
4.8137	491	1.3139	-	-	-	-	-
4.8235	492	1.4027	-	-	-	-	-
4.8333	493	0.4055	-	-	-	-	-
4.8431	494	2.4345	-	-	-	-	-
4.8529	495	1.6135	-	-	-	-	-
4.8627	496	0.7421	-	-	-	-	-
4.8725	497	1.9173	-	-	-	-	-
4.8824	498	0.2355	-	-	-	-	-
4.8922	499	0.8762	-	-	-	-	-
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4.9902	509	1.5688	-	-	-	-	-
5.0	510	0.5072	0.4776	0.4827	0.4794	0.4486	0.3946
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5.5098	562	3.1024	-	-	-	-	-
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6.0	612	0.7863	0.4622	0.4662	0.4587	0.4345	0.3890
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6.0294	615	0.3443	-	-	-	-	-
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6.0784	620	2.0298	-	-	-	-	-
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6.0980	622	0.0383	-	-	-	-	-
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6.1569	628	2.2865	-	-	-	-	-
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6.4510	658	2.0283	-	-	-	-	-
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6.5	663	2.4094	-	-	-	-	-
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6.6078	674	2.1726	-	-	-	-	-
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6.9510	709	1.1276	-	-	-	-	-
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6.9804	712	0.8258	-	-	-	-	-
6.9902	713	0.2178	-	-	-	-	-
7.0	714	0.1586	0.4842	0.4773	0.4746	0.4532	0.4014
7.0098	715	1.3612	-	-	-	-	-
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7.0294	717	0.1313	-	-	-	-	-
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7.0490	719	1.448	-	-	-	-	-
7.0588	720	0.6645	-	-	-	-	-
7.0686	721	2.2201	-	-	-	-	-
7.0784	722	0.1926	-	-	-	-	-
7.0882	723	0.1263	-	-	-	-	-
7.0980	724	3.7967	-	-	-	-	-
7.1078	725	2.3927	-	-	-	-	-
7.1176	726	6.3334	-	-	-	-	-
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7.1373	728	0.137	-	-	-	-	-
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7.1569	730	0.2917	-	-	-	-	-
7.1667	731	1.7204	-	-	-	-	-
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7.1863	733	0.1121	-	-	-	-	-
7.1961	734	1.6957	-	-	-	-	-
7.2059	735	0.2025	-	-	-	-	-
7.2157	736	0.7124	-	-	-	-	-
7.2255	737	0.1843	-	-	-	-	-
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7.2451	739	0.1229	-	-	-	-	-
7.2549	740	0.8085	-	-	-	-	-
7.2647	741	1.4072	-	-	-	-	-
7.2745	742	0.3219	-	-	-	-	-
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7.2941	744	1.0444	-	-	-	-	-
7.3039	745	0.3287	-	-	-	-	-
7.3137	746	0.9557	-	-	-	-	-
7.3235	747	1.4513	-	-	-	-	-
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7.3431	749	0.1202	-	-	-	-	-
7.3529	750	0.5519	-	-	-	-	-
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7.3922	754	0.2854	-	-	-	-	-
7.4020	755	0.4645	-	-	-	-	-
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7.4412	759	1.6786	-	-	-	-	-
7.4510	760	0.1831	-	-	-	-	-
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7.4804	763	0.0992	-	-	-	-	-
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7.5	765	0.3628	-	-	-	-	-
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7.5196	767	0.8091	-	-	-	-	-
7.5294	768	0.2497	-	-	-	-	-
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7.5490	770	0.5576	-	-	-	-	-
7.5588	771	0.2881	-	-	-	-	-
7.5686	772	1.1749	-	-	-	-	-
7.5784	773	0.8155	-	-	-	-	-
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7.5980	775	1.4786	-	-	-	-	-
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7.6275	778	0.9981	-	-	-	-	-
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7.6471	780	0.6307	-	-	-	-	-
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7.6667	782	0.0685	-	-	-	-	-
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7.6961	785	1.0463	-	-	-	-	-
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7.7157	787	1.3589	-	-	-	-	-
7.7255	788	2.5954	-	-	-	-	-
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7.7549	791	0.6342	-	-	-	-	-
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7.7941	795	1.3731	-	-	-	-	-
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7.8431	800	1.559	-	-	-	-	-
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7.9902	815	1.0448	-	-	-	-	-
8.0	816	1.1644	0.4888	0.4873	0.4753	0.4484	0.4195
8.0098	817	1.5271	-	-	-	-	-
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8.0294	819	0.5901	-	-	-	-	-
8.0392	820	0.6715	-	-	-	-	-
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8.3137	848	0.6536	-	-	-	-	-
8.3235	849	0.6141	-	-	-	-	-
8.3333	850	0.2558	-	-	-	-	-
8.3431	851	0.3183	-	-	-	-	-
8.3529	852	0.0475	-	-	-	-	-
8.3627	853	0.9415	-	-	-	-	-
8.3725	854	0.5416	-	-	-	-	-
8.3824	855	0.4004	-	-	-	-	-
8.3922	856	1.0537	-	-	-	-	-
8.4020	857	0.4692	-	-	-	-	-
8.4118	858	1.0613	-	-	-	-	-
8.4216	859	0.7172	-	-	-	-	-
8.4314	860	0.9497	-	-	-	-	-
8.4412	861	1.6799	-	-	-	-	-
8.4510	862	0.2936	-	-	-	-	-
8.4608	863	0.476	-	-	-	-	-
8.4706	864	0.2543	-	-	-	-	-
8.4804	865	1.3147	-	-	-	-	-
8.4902	866	0.0484	-	-	-	-	-
8.5	867	0.1529	-	-	-	-	-
8.5098	868	0.2154	-	-	-	-	-
8.5196	869	1.5366	-	-	-	-	-
8.5294	870	1.168	-	-	-	-	-
8.5392	871	0.0876	-	-	-	-	-
8.5490	872	0.5811	-	-	-	-	-
8.5588	873	2.2808	-	-	-	-	-
8.5686	874	1.7974	-	-	-	-	-
8.5784	875	0.2878	-	-	-	-	-
8.5882	876	0.7999	-	-	-	-	-
8.5980	877	0.4579	-	-	-	-	-
8.6078	878	1.843	-	-	-	-	-
8.6176	879	1.1973	-	-	-	-	-
8.6275	880	1.5788	-	-	-	-	-
8.6373	881	0.2493	-	-	-	-	-
8.6471	882	0.1847	-	-	-	-	-
8.6569	883	0.1465	-	-	-	-	-
8.6667	884	0.2215	-	-	-	-	-
8.6765	885	0.3329	-	-	-	-	-
8.6863	886	1.3447	-	-	-	-	-
8.6961	887	2.6495	-	-	-	-	-
8.7059	888	0.0285	-	-	-	-	-
8.7157	889	0.1793	-	-	-	-	-
8.7255	890	0.5292	-	-	-	-	-
8.7353	891	0.3564	-	-	-	-	-
8.7451	892	0.2517	-	-	-	-	-
8.7549	893	0.1496	-	-	-	-	-
8.7647	894	1.9259	-	-	-	-	-
8.7745	895	1.0521	-	-	-	-	-
8.7843	896	0.6744	-	-	-	-	-
8.7941	897	0.423	-	-	-	-	-
8.8039	898	0.1497	-	-	-	-	-
8.8137	899	0.1196	-	-	-	-	-
8.8235	900	0.4274	-	-	-	-	-
8.8333	901	0.0996	-	-	-	-	-
8.8431	902	2.8575	-	-	-	-	-
8.8529	903	1.8501	-	-	-	-	-
8.8627	904	0.06	-	-	-	-	-
8.8725	905	0.9051	-	-	-	-	-
8.8824	906	0.2576	-	-	-	-	-
8.8922	907	1.0302	-	-	-	-	-
8.9020	908	0.1147	-	-	-	-	-
8.9118	909	1.0332	-	-	-	-	-
8.9216	910	0.2305	-	-	-	-	-
8.9314	911	0.7005	-	-	-	-	-
8.9412	912	0.263	-	-	-	-	-
8.9510	913	1.8709	-	-	-	-	-
8.9608	914	0.11	-	-	-	-	-
8.9706	915	1.1326	-	-	-	-	-
8.9804	916	0.4268	-	-	-	-	-
8.9902	917	0.135	-	-	-	-	-
9.0	918	0.0505	0.4897	0.4788	0.4805	0.4570	0.4143
9.0098	919	0.5024	-	-	-	-	-
9.0196	920	0.0824	-	-	-	-	-
9.0294	921	1.2406	-	-	-	-	-
9.0392	922	0.8058	-	-	-	-	-
9.0490	923	1.4736	-	-	-	-	-
9.0588	924	1.136	-	-	-	-	-
9.0686	925	0.8272	-	-	-	-	-
9.0784	926	0.1542	-	-	-	-	-
9.0882	927	0.5059	-	-	-	-	-
9.0980	928	0.0862	-	-	-	-	-
9.1078	929	0.0455	-	-	-	-	-
9.1176	930	0.5071	-	-	-	-	-
9.1275	931	0.6212	-	-	-	-	-
9.1373	932	0.1289	-	-	-	-	-
9.1471	933	1.7777	-	-	-	-	-
9.1569	934	0.686	-	-	-	-	-
9.1667	935	0.1327	-	-	-	-	-
9.1765	936	0.6337	-	-	-	-	-
9.1863	937	0.3141	-	-	-	-	-
9.1961	938	0.3251	-	-	-	-	-
9.2059	939	0.2824	-	-	-	-	-
9.2157	940	0.7855	-	-	-	-	-
9.2255	941	1.4569	-	-	-	-	-
9.2353	942	1.9269	-	-	-	-	-
9.2451	943	0.0791	-	-	-	-	-
9.2549	944	0.1144	-	-	-	-	-
9.2647	945	0.3128	-	-	-	-	-
9.2745	946	0.4973	-	-	-	-	-
9.2843	947	1.1329	-	-	-	-	-
9.2941	948	0.3658	-	-	-	-	-
9.3039	949	0.3045	-	-	-	-	-
9.3137	950	1.107	-	-	-	-	-
9.3235	951	0.3437	-	-	-	-	-
9.3333	952	0.1056	-	-	-	-	-
9.3431	953	0.6303	-	-	-	-	-
9.3529	954	0.4203	-	-	-	-	-
9.3627	955	0.0878	-	-	-	-	-
9.3725	956	1.0469	-	-	-	-	-
9.3824	957	0.7847	-	-	-	-	-
9.3922	958	0.3565	-	-	-	-	-
9.4020	959	0.5553	-	-	-	-	-
9.4118	960	0.4059	-	-	-	-	-
9.4216	961	0.019	-	-	-	-	-
9.4314	962	0.1547	-	-	-	-	-
9.4412	963	0.527	-	-	-	-	-
9.4510	964	0.69	-	-	-	-	-
9.4608	965	0.0371	-	-	-	-	-
9.4706	966	0.1762	-	-	-	-	-
9.4804	967	0.5291	-	-	-	-	-
9.4902	968	0.1081	-	-	-	-	-
9.5	969	0.0859	-	-	-	-	-
9.5098	970	0.118	-	-	-	-	-
9.5196	971	2.4267	-	-	-	-	-
9.5294	972	0.6871	-	-	-	-	-
9.5392	973	0.3477	-	-	-	-	-
9.5490	974	0.2141	-	-	-	-	-
9.5588	975	0.0792	-	-	-	-	-
9.5686	976	0.8325	-	-	-	-	-
9.5784	977	0.6683	-	-	-	-	-
9.5882	978	0.369	-	-	-	-	-
9.5980	979	0.1902	-	-	-	-	-
9.6078	980	0.0755	-	-	-	-	-
9.6176	981	0.292	-	-	-	-	-
9.6275	982	0.7333	-	-	-	-	-
9.6373	983	0.4299	-	-	-	-	-
9.6471	984	1.4757	-	-	-	-	-
9.6569	985	1.229	-	-	-	-	-
9.6667	986	0.5431	-	-	-	-	-
9.6765	987	1.2087	-	-	-	-	-
9.6863	988	2.3625	-	-	-	-	-
9.6961	989	0.4824	-	-	-	-	-
9.7059	990	0.0348	-	-	-	-	-
9.7157	991	1.3008	-	-	-	-	-
9.7255	992	0.1918	-	-	-	-	-
9.7353	993	0.088	-	-	-	-	-
9.7451	994	0.9295	-	-	-	-	-
9.7549	995	0.6183	-	-	-	-	-
9.7647	996	0.0707	-	-	-	-	-
9.7745	997	0.3144	-	-	-	-	-
9.7843	998	0.2091	-	-	-	-	-
9.7941	999	0.0325	-	-	-	-	-
9.8039	1000	2.6885	-	-	-	-	-
9.8137	1001	0.3574	-	-	-	-	-
9.8235	1002	0.7325	-	-	-	-	-
9.8333	1003	3.0029	-	-	-	-	-
9.8431	1004	0.6402	-	-	-	-	-
9.8529	1005	0.2019	-	-	-	-	-
9.8627	1006	0.1228	-	-	-	-	-
9.8725	1007	0.361	-	-	-	-	-
9.8824	1008	1.3336	-	-	-	-	-
9.8922	1009	0.0413	-	-	-	-	-
9.9020	1010	0.7923	-	-	-	-	-
9.9118	1011	2.1246	-	-	-	-	-
9.9216	1012	0.5226	-	-	-	-	-
9.9314	1013	0.187	-	-	-	-	-
9.9412	1014	0.5866	-	-	-	-	-
9.9510	1015	0.099	-	-	-	-	-
9.9608	1016	0.9884	-	-	-	-	-
9.9706	1017	0.109	-	-	-	-	-
9.9804	1018	0.5118	-	-	-	-	-
9.9902	1019	1.3323	-	-	-	-	-
10.0	1020	0.386	0.4868	0.4823	0.4792	0.46	0.4123

• 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}
}