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all_MiniLM_L6_v2 Finetuned on Data

Name: all-MiniLM-L6-v2-ft-new
Availability: InStock

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    "What is one action that may be included under 'mutual assistance' according to the text?",
    '1.Supervisory authorities shall provide each other with relevant information and mutual assistance in order to implement and apply this Regulation in a consistent manner, and shall put in place measures for effective cooperation with one another. Mutual assistance shall cover, in particular, information requests and supervisory measures, such as requests to carry out prior authorisations and consultations, inspections and investigations.\n2.Each supervisory authority shall take all appropriate measures required to reply to a request of another supervisory authority without undue delay and no later than one month after receiving the request. Such measures may include, in particular, the transmission of relevant information on the conduct of an investigation.\n3.Requests for assistance shall contain all the necessary information, including the purpose of and reasons for the request. Information exchanged shall be used only for the purpose for which it was requested.\n4.The requested supervisory authority shall not refuse to comply with the request unless: (a)  it is not competent for the subject-matter of the request or for the measures it is requested to execute; or (b)  compliance with the request would infringe this Regulation or Union or Member State law to which the supervisory authority receiving the request is subject.\n5.The requested supervisory authority shall inform the requesting supervisory authority of the results or, as the case may be, of the progress of the measures taken in order to respond to the request. The requested supervisory authority shall provide reasons for any refusal to comply with a request pursuant to paragraph 4\n6.Requested supervisory authorities shall, as a rule, supply the information requested by other supervisory authorities by electronic means, using a standardised format.\n7.Requested supervisory authorities shall not charge a fee for any action taken by them pursuant to a request for mutual assistance. Supervisory authorities may agree on rules to indemnify each other for specific expenditure arising from the provision of mutual assistance in exceptional circumstances.\n8.Where a supervisory authority does not provide the information referred to in paragraph 5 of this Article within one month of receiving the request of another supervisory authority, the requesting supervisory authority may adopt a provisional measure on the territory of its Member State in accordance with Article 55(1). In that case, the urgent need to act under Article 66(1) shall be presumed to be met and require an urgent binding decision from the Board pursuant to Article 66(2).\n9.The Commission may, by means of implementing acts, specify the format and procedures for mutual assistance referred to in this Article and the arrangements for the exchange of information by electronic means between supervisory authorities, and between supervisory authorities and the Board, in particular the standardised format referred to in paragraph 6 of this Article. Those implementing acts shall be adopted in accordance with the examination procedure referred to in Article 93(2).',
    "Any natural or legal person has the right to bring an action for annulment of decisions of the Board before the Court of Justice under the conditions provided for in Article 263 TFEU. As addressees of such decisions, the supervisory authorities concerned which wish to challenge them have to bring action within two months of being notified of them, in accordance with Article 263 TFEU. Where decisions of the Board are of direct and individual concern to a controller, processor or complainant, the latter may bring an action for annulment against those decisions within two months of their publication on the website of the Board, in accordance with Article 263 TFEU. Without prejudice to this right under Article 263 TFEU, each natural or legal person should have an effective judicial remedy before the competent national court against a decision of a supervisory authority which produces legal effects concerning that person. Such a decision concerns in particular the exercise of investigative, corrective and authorisation powers by the supervisory authority or the dismissal or rejection of complaints. However, the right to an effective judicial remedy does not encompass measures taken by supervisory authorities which are not legally binding, such as opinions issued by or advice provided by the supervisory authority. Proceedings against a supervisory authority should be brought before the courts of the Member State where the supervisory authority is established and should be conducted in accordance with that Member State's procedural law. Those courts should exercise full jurisdiction, which should include jurisdiction to examine all questions of fact and law relevant to the dispute before them. Where a complaint has been rejected or dismissed by a supervisory authority, the complainant may bring proceedings before the courts in the same Member State. In the context of judicial remedies relating to the application of this Regulation, national courts which consider a decision on the question necessary to enable them to give judgment, may, or in the case provided for in Article 267 TFEU, must, request the Court of Justice to give a preliminary ruling on the interpretation of Union law, including this Regulation. Furthermore, where a decision of a supervisory authority implementing a decision of the Board is challenged before a national court and the validity of the decision of the Board is at issue, that national court does not have the power to declare the Board's decision invalid but must refer the question of validity to the Court of Justice in accordance with Article 267 TFEU as interpreted by the Court of Justice, where it considers the decision invalid. However, a national court may not refer a question on the validity of the decision of the Board at the request of a natural or legal person which had the opportunity to bring an action for annulment of that decision, in particular if it was directly and individually concerned by that decision, but had not done so within the period laid down in Article 263 TFEU.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4105, 0.0135],
#         [0.4105, 1.0000, 0.2998],
#         [0.0135, 0.2998, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_384
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 384 3}

Metric	Value
cosine_accuracy@1	0.4447
cosine_accuracy@3	0.4668
cosine_accuracy@5	0.5012
cosine_accuracy@10	0.5528
cosine_precision@1	0.4447
cosine_precision@3	0.421
cosine_precision@5	0.3843
cosine_precision@10	0.327
cosine_recall@1	0.0961
cosine_recall@3	0.2403
cosine_recall@5	0.3143
cosine_recall@10	0.4251
cosine_ndcg@10	0.4912
cosine_mrr@10	0.4675
cosine_map@100	0.5401

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.4349
cosine_accuracy@3	0.457
cosine_accuracy@5	0.4791
cosine_accuracy@10	0.5233
cosine_precision@1	0.4349
cosine_precision@3	0.412
cosine_precision@5	0.3744
cosine_precision@10	0.314
cosine_recall@1	0.0917
cosine_recall@3	0.2336
cosine_recall@5	0.3043
cosine_recall@10	0.4083
cosine_ndcg@10	0.473
cosine_mrr@10	0.4538
cosine_map@100	0.5253

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.4079
cosine_accuracy@3	0.4324
cosine_accuracy@5	0.4668
cosine_accuracy@10	0.5086
cosine_precision@1	0.4079
cosine_precision@3	0.3866
cosine_precision@5	0.3543
cosine_precision@10	0.3012
cosine_recall@1	0.0881
cosine_recall@3	0.2217
cosine_recall@5	0.2895
cosine_recall@10	0.3905
cosine_ndcg@10	0.4526
cosine_mrr@10	0.4293
cosine_map@100	0.5005

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.3538
cosine_accuracy@3	0.3808
cosine_accuracy@5	0.4128
cosine_accuracy@10	0.457
cosine_precision@1	0.3538
cosine_precision@3	0.3407
cosine_precision@5	0.3135
cosine_precision@10	0.27
cosine_recall@1	0.0747
cosine_recall@3	0.192
cosine_recall@5	0.2533
cosine_recall@10	0.3556
cosine_ndcg@10	0.4019
cosine_mrr@10	0.3766
cosine_map@100	0.4618

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: 15.35 tokens
max: 34 tokens
min: 25 tokens
mean: 221.19 tokens
max: 256 tokens

Samples:

anchor	positive
`What was the plaintiff asked to input first on the clone website?`	[object Object] [object Object][object Object] [object Object][object Object] [object Object][object Object] [object Object][object Object] Partially accepts the lawsuit. [object Object][object Object] 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...
`At what time did the defendant send the first message to the plaintiff's mobile phone?`	**Court (Civil/Criminal): Civil [object Object]Provisions: [object Object]Time of commission of the act: [object Object]Outcome (not guilty, guilty): Rejects the lawsuit. [object Object]Reasoning: [object Object]Facts: The plaintiff holds account number ....................... at the defendant bank. Following the application/contract dated January 9, 2019, the plaintiff became a subscriber to the alternative service networks provided by the defendant bank through online banking (.........................). In the aforementioned application, the plaintiff stated that her mobile phone (number .................) would be used by the defendant to send additional security codes for the approval of her transactions via .......................... It is noted that the plaintiff received subscriber code .................., thus enabling her to conduct transactions without her physical presence at the branches of the defendant bank, from fixed or mobile devices (computers, smartphones, tablets), by entering her username and password to access her personal acc...
`How should the lead supervisory authority and other supervisory authorities exchange information under this Article?`	1.The lead supervisory authority shall cooperate with the other supervisory authorities concerned in accordance with this Article in an endeavour to reach consensus. The lead supervisory authority and the supervisory authorities concerned shall exchange all relevant information with each other.[object Object]2.The lead supervisory authority may request at any time other supervisory authorities concerned to provide mutual assistance pursuant to Article 61 and may conduct joint operations pursuant to Article 62, in particular for carrying out investigations or for monitoring the implementation of a measure concerning a controller or processor established in another Member State.[object Object]3.The lead supervisory authority shall, without delay, communicate the relevant information on the matter to the other supervisory authorities concerned. It shall without delay submit a draft decision to the other supervisory authorities concerned for their opinion and take due account of their views.[object Object]4.Where any of the other ...

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 384, 5 256, 6 128, 7 64 8 ], 9 "matryoshka_weights": [ 10 1, 11 1, 12 1, 13 1 14 ], 15 "n_dims_per_step": -1 16}

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_384_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.0098	1	9.314	-	-	-	-
0.0196	2	13.4156	-	-	-	-
0.0294	3	6.5901	-	-	-	-
0.0392	4	7.5108	-	-	-	-
0.0490	5	8.4592	-	-	-	-
0.0588	6	15.2418	-	-	-	-
0.0686	7	8.6375	-	-	-	-
0.0784	8	14.5092	-	-	-	-
0.0882	9	10.0459	-	-	-	-
0.0980	10	8.3529	-	-	-	-
0.1078	11	14.6619	-	-	-	-
0.1176	12	5.3518	-	-	-	-
0.1275	13	9.4316	-	-	-	-
0.1373	14	9.1804	-	-	-	-
0.1471	15	9.9546	-	-	-	-
0.1569	16	12.0305	-	-	-	-
0.1667	17	7.8328	-	-	-	-
0.1765	18	9.0889	-	-	-	-
0.1863	19	8.8536	-	-	-	-
0.1961	20	10.5517	-	-	-	-
0.2059	21	11.9371	-	-	-	-
0.2157	22	8.6637	-	-	-	-
0.2255	23	4.3503	-	-	-	-
0.2353	24	7.4951	-	-	-	-
0.2451	25	7.9368	-	-	-	-
0.2549	26	7.8018	-	-	-	-
0.2647	27	14.6783	-	-	-	-
0.2745	28	10.7873	-	-	-	-
0.2843	29	10.0755	-	-	-	-
0.2941	30	6.2568	-	-	-	-
0.3039	31	5.3363	-	-	-	-
0.3137	32	7.8691	-	-	-	-
0.3235	33	11.2728	-	-	-	-
0.3333	34	6.695	-	-	-	-
0.3431	35	12.1315	-	-	-	-
0.3529	36	7.2586	-	-	-	-
0.3627	37	8.7879	-	-	-	-
0.3725	38	7.639	-	-	-	-
0.3824	39	11.3883	-	-	-	-
0.3922	40	8.0624	-	-	-	-
0.4020	41	10.6244	-	-	-	-
0.4118	42	6.7805	-	-	-	-
0.4216	43	10.4424	-	-	-	-
0.4314	44	9.6474	-	-	-	-
0.4412	45	10.9693	-	-	-	-
0.4510	46	7.6602	-	-	-	-
0.4608	47	3.99	-	-	-	-
0.4706	48	5.6034	-	-	-	-
0.4804	49	7.1135	-	-	-	-
0.4902	50	5.6046	-	-	-	-
0.5	51	10.5781	-	-	-	-
0.5098	52	3.7839	-	-	-	-
0.5196	53	5.3129	-	-	-	-
0.5294	54	8.1674	-	-	-	-
0.5392	55	7.3051	-	-	-	-
0.5490	56	5.154	-	-	-	-
0.5588	57	5.4361	-	-	-	-
0.5686	58	6.0079	-	-	-	-
0.5784	59	7.9415	-	-	-	-
0.5882	60	9.1808	-	-	-	-
0.5980	61	8.0456	-	-	-	-
0.6078	62	12.8651	-	-	-	-
0.6176	63	6.3757	-	-	-	-
0.6275	64	7.2359	-	-	-	-
0.6373	65	7.1767	-	-	-	-
0.6471	66	9.7435	-	-	-	-
0.6569	67	8.1696	-	-	-	-
0.6667	68	4.6557	-	-	-	-
0.6765	69	7.3619	-	-	-	-
0.6863	70	6.937	-	-	-	-
0.6961	71	4.8277	-	-	-	-
0.7059	72	4.7581	-	-	-	-
0.7157	73	5.1204	-	-	-	-
0.7255	74	5.8539	-	-	-	-
0.7353	75	6.0988	-	-	-	-
0.7451	76	8.3001	-	-	-	-
0.7549	77	8.4049	-	-	-	-
0.7647	78	6.928	-	-	-	-
0.7745	79	5.3	-	-	-	-
0.7843	80	8.463	-	-	-	-
0.7941	81	5.7306	-	-	-	-
0.8039	82	7.8955	-	-	-	-
0.8137	83	9.6203	-	-	-	-
0.8235	84	8.6489	-	-	-	-
0.8333	85	3.8349	-	-	-	-
0.8431	86	6.7351	-	-	-	-
0.8529	87	9.5078	-	-	-	-
0.8627	88	5.1711	-	-	-	-
0.8725	89	7.4387	-	-	-	-
0.8824	90	4.5647	-	-	-	-
0.8922	91	4.8621	-	-	-	-
0.9020	92	3.7257	-	-	-	-
0.9118	93	4.6232	-	-	-	-
0.9216	94	7.8424	-	-	-	-
0.9314	95	11.4285	-	-	-	-
0.9412	96	8.2627	-	-	-	-
0.9510	97	8.5677	-	-	-	-
0.9608	98	4.8404	-	-	-	-
0.9706	99	5.5854	-	-	-	-
0.9804	100	3.6296	-	-	-	-
0.9902	101	5.6926	-	-	-	-
1.0	102	4.168	0.4201	0.3982	0.3559	0.3200
1.0098	103	7.2629	-	-	-	-
1.0196	104	2.1611	-	-	-	-
1.0294	105	5.4286	-	-	-	-
1.0392	106	6.2532	-	-	-	-
1.0490	107	5.3801	-	-	-	-
1.0588	108	5.9141	-	-	-	-
1.0686	109	6.121	-	-	-	-
1.0784	110	3.5102	-	-	-	-
1.0882	111	2.6456	-	-	-	-
1.0980	112	6.0685	-	-	-	-
1.1078	113	5.1762	-	-	-	-
1.1176	114	3.3374	-	-	-	-
1.1275	115	3.3591	-	-	-	-
1.1373	116	3.4516	-	-	-	-
1.1471	117	9.5469	-	-	-	-
1.1569	118	5.3393	-	-	-	-
1.1667	119	6.5951	-	-	-	-
1.1765	120	2.3823	-	-	-	-
1.1863	121	7.0711	-	-	-	-
1.1961	122	2.1941	-	-	-	-
1.2059	123	4.1914	-	-	-	-
1.2157	124	3.3929	-	-	-	-
1.2255	125	4.0328	-	-	-	-
1.2353	126	6.383	-	-	-	-
1.2451	127	4.9079	-	-	-	-
1.2549	128	4.7432	-	-	-	-
1.2647	129	3.0618	-	-	-	-
1.2745	130	4.468	-	-	-	-
1.2843	131	6.5799	-	-	-	-
1.2941	132	6.2514	-	-	-	-
1.3039	133	4.0947	-	-	-	-
1.3137	134	5.9042	-	-	-	-
1.3235	135	3.1853	-	-	-	-
1.3333	136	4.7338	-	-	-	-
1.3431	137	3.8172	-	-	-	-
1.3529	138	3.6581	-	-	-	-
1.3627	139	4.923	-	-	-	-
1.3725	140	4.5343	-	-	-	-
1.3824	141	3.7658	-	-	-	-
1.3922	142	11.1072	-	-	-	-
1.4020	143	3.7444	-	-	-	-
1.4118	144	8.2919	-	-	-	-
1.4216	145	6.0605	-	-	-	-
1.4314	146	3.6421	-	-	-	-
1.4412	147	4.8018	-	-	-	-
1.4510	148	6.0681	-	-	-	-
1.4608	149	3.7449	-	-	-	-
1.4706	150	5.372	-	-	-	-
1.4804	151	4.1343	-	-	-	-
1.4902	152	3.57	-	-	-	-
1.5	153	2.355	-	-	-	-
1.5098	154	5.0179	-	-	-	-
1.5196	155	4.5454	-	-	-	-
1.5294	156	4.3362	-	-	-	-
1.5392	157	4.7675	-	-	-	-
1.5490	158	3.4136	-	-	-	-
1.5588	159	3.5347	-	-	-	-
1.5686	160	4.7166	-	-	-	-
1.5784	161	5.3206	-	-	-	-
1.5882	162	5.2678	-	-	-	-
1.5980	163	5.0315	-	-	-	-
1.6078	164	7.7094	-	-	-	-
1.6176	165	2.2483	-	-	-	-
1.6275	166	3.6534	-	-	-	-
1.6373	167	4.6594	-	-	-	-
1.6471	168	4.5964	-	-	-	-
1.6569	169	9.873	-	-	-	-
1.6667	170	2.6889	-	-	-	-
1.6765	171	2.6431	-	-	-	-
1.6863	172	3.6038	-	-	-	-
1.6961	173	3.869	-	-	-	-
1.7059	174	2.8357	-	-	-	-
1.7157	175	4.0195	-	-	-	-
1.7255	176	5.8369	-	-	-	-
1.7353	177	1.4096	-	-	-	-
1.7451	178	8.4592	-	-	-	-
1.7549	179	5.934	-	-	-	-
1.7647	180	2.3132	-	-	-	-
1.7745	181	3.7131	-	-	-	-
1.7843	182	6.267	-	-	-	-
1.7941	183	7.9196	-	-	-	-
1.8039	184	7.2907	-	-	-	-
1.8137	185	3.8729	-	-	-	-
1.8235	186	4.265	-	-	-	-
1.8333	187	2.5177	-	-	-	-
1.8431	188	6.9559	-	-	-	-
1.8529	189	3.2725	-	-	-	-
1.8627	190	4.2916	-	-	-	-
1.8725	191	8.5079	-	-	-	-
1.8824	192	4.4141	-	-	-	-
1.8922	193	9.6196	-	-	-	-
1.9020	194	5.3813	-	-	-	-
1.9118	195	4.6901	-	-	-	-
1.9216	196	5.9262	-	-	-	-
1.9314	197	4.8516	-	-	-	-
1.9412	198	3.2107	-	-	-	-
1.9510	199	3.7887	-	-	-	-
1.9608	200	10.6244	-	-	-	-
1.9706	201	5.9944	-	-	-	-
1.9804	202	5.4088	-	-	-	-
1.9902	203	6.0135	-	-	-	-
2.0	204	9.4758	0.4590	0.4304	0.4099	0.3591
2.0098	205	5.3354	-	-	-	-
2.0196	206	5.217	-	-	-	-
2.0294	207	5.9712	-	-	-	-
2.0392	208	6.6628	-	-	-	-
2.0490	209	6.1048	-	-	-	-
2.0588	210	3.5396	-	-	-	-
2.0686	211	1.2557	-	-	-	-
2.0784	212	2.1268	-	-	-	-
2.0882	213	4.1976	-	-	-	-
2.0980	214	2.0743	-	-	-	-
2.1078	215	2.5679	-	-	-	-
2.1176	216	2.1892	-	-	-	-
2.1275	217	5.1347	-	-	-	-
2.1373	218	2.5574	-	-	-	-
2.1471	219	4.6373	-	-	-	-
2.1569	220	3.4945	-	-	-	-
2.1667	221	4.9608	-	-	-	-
2.1765	222	3.2493	-	-	-	-
2.1863	223	3.2011	-	-	-	-
2.1961	224	5.5386	-	-	-	-
2.2059	225	4.166	-	-	-	-
2.2157	226	2.7747	-	-	-	-
2.2255	227	2.4677	-	-	-	-
2.2353	228	1.9435	-	-	-	-
2.2451	229	3.3285	-	-	-	-
2.2549	230	4.082	-	-	-	-
2.2647	231	2.6793	-	-	-	-
2.2745	232	2.7412	-	-	-	-
2.2843	233	5.4343	-	-	-	-
2.2941	234	5.3182	-	-	-	-
2.3039	235	5.4431	-	-	-	-
2.3137	236	3.825	-	-	-	-
2.3235	237	5.9555	-	-	-	-
2.3333	238	4.0367	-	-	-	-
2.3431	239	0.874	-	-	-	-
2.3529	240	3.7652	-	-	-	-
2.3627	241	3.1397	-	-	-	-
2.3725	242	2.2095	-	-	-	-
2.3824	243	3.6528	-	-	-	-
2.3922	244	3.9126	-	-	-	-
2.4020	245	2.2959	-	-	-	-
2.4118	246	2.1179	-	-	-	-
2.4216	247	3.366	-	-	-	-
2.4314	248	3.7169	-	-	-	-
2.4412	249	2.2856	-	-	-	-
2.4510	250	2.9071	-	-	-	-
2.4608	251	2.7711	-	-	-	-
2.4706	252	3.3077	-	-	-	-
2.4804	253	3.5824	-	-	-	-
2.4902	254	2.2269	-	-	-	-
2.5	255	5.7617	-	-	-	-
2.5098	256	1.7614	-	-	-	-
2.5196	257	4.6898	-	-	-	-
2.5294	258	1.9208	-	-	-	-
2.5392	259	3.2963	-	-	-	-
2.5490	260	6.7511	-	-	-	-
2.5588	261	2.9878	-	-	-	-
2.5686	262	1.3882	-	-	-	-
2.5784	263	2.6977	-	-	-	-
2.5882	264	4.0032	-	-	-	-
2.5980	265	3.5722	-	-	-	-
2.6078	266	3.1433	-	-	-	-
2.6176	267	2.2979	-	-	-	-
2.6275	268	4.1767	-	-	-	-
2.6373	269	2.0087	-	-	-	-
2.6471	270	2.1034	-	-	-	-
2.6569	271	3.381	-	-	-	-
2.6667	272	4.51	-	-	-	-
2.6765	273	2.9127	-	-	-	-
2.6863	274	4.2389	-	-	-	-
2.6961	275	3.2239	-	-	-	-
2.7059	276	6.8586	-	-	-	-
2.7157	277	4.9489	-	-	-	-
2.7255	278	7.1983	-	-	-	-
2.7353	279	2.9171	-	-	-	-
2.7451	280	5.4655	-	-	-	-
2.7549	281	3.5527	-	-	-	-
2.7647	282	2.4092	-	-	-	-
2.7745	283	3.1819	-	-	-	-
2.7843	284	3.2839	-	-	-	-
2.7941	285	2.8816	-	-	-	-
2.8039	286	3.4632	-	-	-	-
2.8137	287	6.4261	-	-	-	-
2.8235	288	5.2636	-	-	-	-
2.8333	289	3.7697	-	-	-	-
2.8431	290	2.7751	-	-	-	-
2.8529	291	2.7938	-	-	-	-
2.8627	292	3.2083	-	-	-	-
2.8725	293	3.2028	-	-	-	-
2.8824	294	4.1932	-	-	-	-
2.8922	295	3.3445	-	-	-	-
2.9020	296	3.4374	-	-	-	-
2.9118	297	4.893	-	-	-	-
2.9216	298	5.9708	-	-	-	-
2.9314	299	1.6077	-	-	-	-
2.9412	300	2.5645	-	-	-	-
2.9510	301	1.5729	-	-	-	-
2.9608	302	1.3197	-	-	-	-
2.9706	303	5.934	-	-	-	-
2.9804	304	1.8081	-	-	-	-
2.9902	305	3.7014	-	-	-	-
3.0	306	1.3901	0.4526	0.4435	0.4243	0.3765
3.0098	307	1.2279	-	-	-	-
3.0196	308	3.0015	-	-	-	-
3.0294	309	2.0001	-	-	-	-
3.0392	310	2.3177	-	-	-	-
3.0490	311	2.0772	-	-	-	-
3.0588	312	1.9896	-	-	-	-
3.0686	313	1.5398	-	-	-	-
3.0784	314	2.7205	-	-	-	-
3.0882	315	4.2203	-	-	-	-
3.0980	316	1.7044	-	-	-	-
3.1078	317	0.4191	-	-	-	-
3.1176	318	5.7572	-	-	-	-
3.1275	319	2.9936	-	-	-	-
3.1373	320	1.4732	-	-	-	-
3.1471	321	0.7598	-	-	-	-
3.1569	322	1.8146	-	-	-	-
3.1667	323	2.211	-	-	-	-
3.1765	324	3.1902	-	-	-	-
3.1863	325	2.1343	-	-	-	-
3.1961	326	3.5396	-	-	-	-
3.2059	327	1.5265	-	-	-	-
3.2157	328	2.4405	-	-	-	-
3.2255	329	2.5522	-	-	-	-
3.2353	330	2.2863	-	-	-	-
3.2451	331	3.5981	-	-	-	-
3.2549	332	4.4424	-	-	-	-
3.2647	333	4.5853	-	-	-	-
3.2745	334	3.5618	-	-	-	-
3.2843	335	2.0108	-	-	-	-
3.2941	336	3.1284	-	-	-	-
3.3039	337	1.3107	-	-	-	-
3.3137	338	6.0269	-	-	-	-
3.3235	339	0.4078	-	-	-	-
3.3333	340	1.4205	-	-	-	-
3.3431	341	2.9532	-	-	-	-
3.3529	342	5.1152	-	-	-	-
3.3627	343	1.4727	-	-	-	-
3.3725	344	1.1699	-	-	-	-
3.3824	345	1.1651	-	-	-	-
3.3922	346	2.7933	-	-	-	-
3.4020	347	3.1528	-	-	-	-
3.4118	348	1.5391	-	-	-	-
3.4216	349	3.0983	-	-	-	-
3.4314	350	2.1153	-	-	-	-
3.4412	351	5.2886	-	-	-	-
3.4510	352	3.3398	-	-	-	-
3.4608	353	2.7488	-	-	-	-
3.4706	354	1.1624	-	-	-	-
3.4804	355	1.502	-	-	-	-
3.4902	356	3.0659	-	-	-	-
3.5	357	2.2818	-	-	-	-
3.5098	358	2.4482	-	-	-	-
3.5196	359	2.1802	-	-	-	-
3.5294	360	5.9712	-	-	-	-
3.5392	361	2.0519	-	-	-	-
3.5490	362	4.983	-	-	-	-
3.5588	363	6.4049	-	-	-	-
3.5686	364	2.3832	-	-	-	-
3.5784	365	4.6559	-	-	-	-
3.5882	366	1.836	-	-	-	-
3.5980	367	1.7374	-	-	-	-
3.6078	368	2.701	-	-	-	-
3.6176	369	4.4824	-	-	-	-
3.6275	370	5.4828	-	-	-	-
3.6373	371	2.5578	-	-	-	-
3.6471	372	1.2774	-	-	-	-
3.6569	373	0.9595	-	-	-	-
3.6667	374	3.0434	-	-	-	-
3.6765	375	6.3731	-	-	-	-
3.6863	376	6.8533	-	-	-	-
3.6961	377	2.7222	-	-	-	-
3.7059	378	1.0102	-	-	-	-
3.7157	379	7.4713	-	-	-	-
3.7255	380	0.3445	-	-	-	-
3.7353	381	3.6325	-	-	-	-
3.7451	382	3.025	-	-	-	-
3.7549	383	1.1259	-	-	-	-
3.7647	384	3.0086	-	-	-	-
3.7745	385	3.1928	-	-	-	-
3.7843	386	6.2071	-	-	-	-
3.7941	387	1.7223	-	-	-	-
3.8039	388	4.6953	-	-	-	-
3.8137	389	2.097	-	-	-	-
3.8235	390	1.2704	-	-	-	-
3.8333	391	1.1491	-	-	-	-
3.8431	392	2.2606	-	-	-	-
3.8529	393	1.4562	-	-	-	-
3.8627	394	2.6823	-	-	-	-
3.8725	395	0.7801	-	-	-	-
3.8824	396	3.1714	-	-	-	-
3.8922	397	2.6189	-	-	-	-
3.9020	398	1.168	-	-	-	-
3.9118	399	2.3271	-	-	-	-
3.9216	400	4.8405	-	-	-	-
3.9314	401	3.9107	-	-	-	-
3.9412	402	2.8518	-	-	-	-
3.9510	403	4.1657	-	-	-	-
3.9608	404	2.2372	-	-	-	-
3.9706	405	1.0614	-	-	-	-
3.9804	406	3.8297	-	-	-	-
3.9902	407	1.9999	-	-	-	-
4.0	408	1.3233	0.4710	0.4510	0.4281	0.4148
4.0098	409	1.0468	-	-	-	-
4.0196	410	2.1942	-	-	-	-
4.0294	411	2.0797	-	-	-	-
4.0392	412	2.8829	-	-	-	-
4.0490	413	0.7442	-	-	-	-
4.0588	414	2.4341	-	-	-	-
4.0686	415	3.232	-	-	-	-
4.0784	416	1.4055	-	-	-	-
4.0882	417	0.724	-	-	-	-
4.0980	418	3.1341	-	-	-	-
4.1078	419	1.786	-	-	-	-
4.1176	420	1.4026	-	-	-	-
4.1275	421	4.7106	-	-	-	-
4.1373	422	5.0459	-	-	-	-
4.1471	423	3.257	-	-	-	-
4.1569	424	2.2771	-	-	-	-
4.1667	425	1.6407	-	-	-	-
4.1765	426	1.953	-	-	-	-
4.1863	427	2.7483	-	-	-	-
4.1961	428	2.284	-	-	-	-
4.2059	429	1.3397	-	-	-	-
4.2157	430	3.0712	-	-	-	-
4.2255	431	4.0505	-	-	-	-
4.2353	432	0.5806	-	-	-	-
4.2451	433	0.8523	-	-	-	-
4.2549	434	5.1672	-	-	-	-
4.2647	435	1.0583	-	-	-	-
4.2745	436	3.5743	-	-	-	-
4.2843	437	2.1655	-	-	-	-
4.2941	438	1.017	-	-	-	-
4.3039	439	1.4406	-	-	-	-
4.3137	440	2.8878	-	-	-	-
4.3235	441	2.9334	-	-	-	-
4.3333	442	2.8944	-	-	-	-
4.3431	443	2.9257	-	-	-	-
4.3529	444	1.2912	-	-	-	-
4.3627	445	4.3522	-	-	-	-
4.3725	446	1.7422	-	-	-	-
4.3824	447	3.6445	-	-	-	-
4.3922	448	4.282	-	-	-	-
4.4020	449	3.7589	-	-	-	-
4.4118	450	0.6328	-	-	-	-
4.4216	451	2.7343	-	-	-	-
4.4314	452	2.7081	-	-	-	-
4.4412	453	2.1833	-	-	-	-
4.4510	454	2.4024	-	-	-	-
4.4608	455	1.8104	-	-	-	-
4.4706	456	4.498	-	-	-	-
4.4804	457	3.3512	-	-	-	-
4.4902	458	2.6532	-	-	-	-
4.5	459	1.2743	-	-	-	-
4.5098	460	4.3366	-	-	-	-
4.5196	461	0.9041	-	-	-	-
4.5294	462	1.7273	-	-	-	-
4.5392	463	2.228	-	-	-	-
4.5490	464	1.9863	-	-	-	-
4.5588	465	1.3791	-	-	-	-
4.5686	466	0.6	-	-	-	-
4.5784	467	0.9613	-	-	-	-
4.5882	468	0.8215	-	-	-	-
4.5980	469	2.0666	-	-	-	-
4.6078	470	3.4636	-	-	-	-
4.6176	471	3.3794	-	-	-	-
4.6275	472	1.5988	-	-	-	-
4.6373	473	0.9649	-	-	-	-
4.6471	474	1.7106	-	-	-	-
4.6569	475	2.9365	-	-	-	-
4.6667	476	1.261	-	-	-	-
4.6765	477	2.7977	-	-	-	-
4.6863	478	3.0166	-	-	-	-
4.6961	479	1.871	-	-	-	-
4.7059	480	3.1311	-	-	-	-
4.7157	481	1.4456	-	-	-	-
4.7255	482	1.7153	-	-	-	-
4.7353	483	2.283	-	-	-	-
4.7451	484	3.1011	-	-	-	-
4.7549	485	1.0986	-	-	-	-
4.7647	486	2.2503	-	-	-	-
4.7745	487	0.3953	-	-	-	-
4.7843	488	4.141	-	-	-	-
4.7941	489	3.4797	-	-	-	-
4.8039	490	4.1368	-	-	-	-
4.8137	491	0.565	-	-	-	-
4.8235	492	2.1161	-	-	-	-
4.8333	493	3.2724	-	-	-	-
4.8431	494	1.0777	-	-	-	-
4.8529	495	2.3217	-	-	-	-
4.8627	496	2.4613	-	-	-	-
4.8725	497	2.6271	-	-	-	-
4.8824	498	3.5758	-	-	-	-
4.8922	499	0.7174	-	-	-	-
4.9020	500	1.3738	-	-	-	-
4.9118	501	4.1496	-	-	-	-
4.9216	502	0.4534	-	-	-	-
4.9314	503	2.1279	-	-	-	-
4.9412	504	3.3264	-	-	-	-
4.9510	505	1.4833	-	-	-	-
4.9608	506	2.6725	-	-	-	-
4.9706	507	2.2114	-	-	-	-
4.9804	508	0.6644	-	-	-	-
4.9902	509	3.18	-	-	-	-
5.0	510	0.6032	0.4756	0.4734	0.4388	0.3863
5.0098	511	2.8545	-	-	-	-
5.0196	512	3.287	-	-	-	-
5.0294	513	2.972	-	-	-	-
5.0392	514	3.6356	-	-	-	-
5.0490	515	0.8827	-	-	-	-
5.0588	516	1.8925	-	-	-	-
5.0686	517	5.0565	-	-	-	-
5.0784	518	2.1866	-	-	-	-
5.0882	519	1.2702	-	-	-	-
5.0980	520	2.3826	-	-	-	-
5.1078	521	2.024	-	-	-	-
5.1176	522	1.606	-	-	-	-
5.1275	523	1.766	-	-	-	-
5.1373	524	2.4618	-	-	-	-
5.1471	525	1.1541	-	-	-	-
5.1569	526	0.607	-	-	-	-
5.1667	527	1.1553	-	-	-	-
5.1765	528	1.5372	-	-	-	-
5.1863	529	4.0782	-	-	-	-
5.1961	530	1.7296	-	-	-	-
5.2059	531	2.2709	-	-	-	-
5.2157	532	1.953	-	-	-	-
5.2255	533	3.8818	-	-	-	-
5.2353	534	2.0291	-	-	-	-
5.2451	535	0.9016	-	-	-	-
5.2549	536	0.8501	-	-	-	-
5.2647	537	1.84	-	-	-	-
5.2745	538	1.3482	-	-	-	-
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5.3039	541	0.9188	-	-	-	-
5.3137	542	0.6407	-	-	-	-
5.3235	543	2.075	-	-	-	-
5.3333	544	1.3842	-	-	-	-
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5.3529	546	2.0425	-	-	-	-
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5.3725	548	1.3228	-	-	-	-
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5.3922	550	0.5431	-	-	-	-
5.4020	551	0.9359	-	-	-	-
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5.4510	556	1.6772	-	-	-	-
5.4608	557	1.7166	-	-	-	-
5.4706	558	4.0858	-	-	-	-
5.4804	559	1.5822	-	-	-	-
5.4902	560	2.3036	-	-	-	-
5.5	561	1.445	-	-	-	-
5.5098	562	2.8063	-	-	-	-
5.5196	563	2.8822	-	-	-	-
5.5294	564	6.7016	-	-	-	-
5.5392	565	1.8359	-	-	-	-
5.5490	566	3.0261	-	-	-	-
5.5588	567	1.4881	-	-	-	-
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5.5784	569	2.7369	-	-	-	-
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5.6078	572	0.8245	-	-	-	-
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5.9902	611	1.3203	-	-	-	-
6.0	612	2.0452	0.4682	0.4634	0.4284	0.4043
6.0098	613	2.1156	-	-	-	-
6.0196	614	1.1371	-	-	-	-
6.0294	615	2.8463	-	-	-	-
6.0392	616	1.4252	-	-	-	-
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6.0588	618	2.1618	-	-	-	-
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6.0784	620	2.7691	-	-	-	-
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6.0980	622	2.4046	-	-	-	-
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6.1176	624	1.9515	-	-	-	-
6.1275	625	3.0264	-	-	-	-
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6.1471	627	1.0579	-	-	-	-
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6.2255	635	1.0471	-	-	-	-
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6.2451	637	2.0447	-	-	-	-
6.2549	638	1.7709	-	-	-	-
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6.2745	640	0.6764	-	-	-	-
6.2843	641	0.5081	-	-	-	-
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6.3039	643	2.0049	-	-	-	-
6.3137	644	1.9641	-	-	-	-
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6.4020	653	2.3882	-	-	-	-
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6.4510	658	5.2133	-	-	-	-
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6.4804	661	0.6605	-	-	-	-
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6.5	663	3.408	-	-	-	-
6.5098	664	0.1852	-	-	-	-
6.5196	665	3.2213	-	-	-	-
6.5294	666	2.4864	-	-	-	-
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6.5490	668	1.4647	-	-	-	-
6.5588	669	4.6957	-	-	-	-
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6.9902	713	3.427	-	-	-	-
7.0	714	2.2931	0.4822	0.4740	0.4423	0.3975
7.0098	715	2.3939	-	-	-	-
7.0196	716	1.5321	-	-	-	-
7.0294	717	2.577	-	-	-	-
7.0392	718	1.0859	-	-	-	-
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7.0588	720	3.4612	-	-	-	-
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7.0784	722	1.2549	-	-	-	-
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7.0980	724	5.1588	-	-	-	-
7.1078	725	1.751	-	-	-	-
7.1176	726	2.0296	-	-	-	-
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7.2549	740	1.1732	-	-	-	-
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7.2745	742	1.2033	-	-	-	-
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7.2941	744	1.6815	-	-	-	-
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7.3235	747	1.4083	-	-	-	-
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7.3529	750	1.831	-	-	-	-
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7.4510	760	1.5175	-	-	-	-
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7.4804	763	0.9924	-	-	-	-
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7.5	765	0.2891	-	-	-	-
7.5098	766	2.1621	-	-	-	-
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7.5294	768	1.977	-	-	-	-
7.5392	769	1.852	-	-	-	-
7.5490	770	1.5613	-	-	-	-
7.5588	771	0.9339	-	-	-	-
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7.5784	773	2.1131	-	-	-	-
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7.5980	775	1.5074	-	-	-	-
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7.6275	778	0.7083	-	-	-	-
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7.6471	780	0.9997	-	-	-	-
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7.6667	782	0.5454	-	-	-	-
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7.6961	785	0.5691	-	-	-	-
7.7059	786	2.4681	-	-	-	-
7.7157	787	0.8033	-	-	-	-
7.7255	788	0.8835	-	-	-	-
7.7353	789	1.5139	-	-	-	-
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7.7549	791	1.081	-	-	-	-
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7.7745	793	2.7415	-	-	-	-
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7.7941	795	1.5433	-	-	-	-
7.8039	796	2.4771	-	-	-	-
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7.8431	800	1.863	-	-	-	-
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7.9020	806	1.5693	-	-	-	-
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7.9510	811	1.5963	-	-	-	-
7.9608	812	0.7807	-	-	-	-
7.9706	813	1.7183	-	-	-	-
7.9804	814	1.5457	-	-	-	-
7.9902	815	0.1146	-	-	-	-
8.0	816	0.5686	0.4841	0.4732	0.4474	0.3997
8.0098	817	0.2666	-	-	-	-
8.0196	818	2.6931	-	-	-	-
8.0294	819	1.6724	-	-	-	-
8.0392	820	1.9833	-	-	-	-
8.0490	821	1.0162	-	-	-	-
8.0588	822	0.9381	-	-	-	-
8.0686	823	3.3628	-	-	-	-
8.0784	824	2.9051	-	-	-	-
8.0882	825	1.2757	-	-	-	-
8.0980	826	0.6009	-	-	-	-
8.1078	827	0.8206	-	-	-	-
8.1176	828	1.056	-	-	-	-
8.1275	829	0.6114	-	-	-	-
8.1373	830	3.1316	-	-	-	-
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8.1569	832	0.6706	-	-	-	-
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8.1863	835	5.7306	-	-	-	-
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8.2157	838	1.329	-	-	-	-
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8.2941	846	1.7959	-	-	-	-
8.3039	847	2.3565	-	-	-	-
8.3137	848	0.8934	-	-	-	-
8.3235	849	1.0446	-	-	-	-
8.3333	850	1.3397	-	-	-	-
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8.3529	852	1.4928	-	-	-	-
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8.3725	854	0.9378	-	-	-	-
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8.3922	856	1.1886	-	-	-	-
8.4020	857	3.6991	-	-	-	-
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8.4216	859	2.2705	-	-	-	-
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8.4510	862	0.6003	-	-	-	-
8.4608	863	0.7857	-	-	-	-
8.4706	864	3.0006	-	-	-	-
8.4804	865	1.9955	-	-	-	-
8.4902	866	0.1742	-	-	-	-
8.5	867	0.7204	-	-	-	-
8.5098	868	2.0722	-	-	-	-
8.5196	869	0.9973	-	-	-	-
8.5294	870	0.6607	-	-	-	-
8.5392	871	0.9087	-	-	-	-
8.5490	872	1.0388	-	-	-	-
8.5588	873	1.5166	-	-	-	-
8.5686	874	2.1062	-	-	-	-
8.5784	875	1.357	-	-	-	-
8.5882	876	0.8239	-	-	-	-
8.5980	877	1.0221	-	-	-	-
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8.6275	880	2.1688	-	-	-	-
8.6373	881	1.5953	-	-	-	-
8.6471	882	0.9909	-	-	-	-
8.6569	883	0.7163	-	-	-	-
8.6667	884	2.9778	-	-	-	-
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8.6961	887	1.9843	-	-	-	-
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8.7157	889	1.7284	-	-	-	-
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8.7353	891	1.0349	-	-	-	-
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8.7549	893	0.7346	-	-	-	-
8.7647	894	0.896	-	-	-	-
8.7745	895	0.3089	-	-	-	-
8.7843	896	1.0116	-	-	-	-
8.7941	897	0.782	-	-	-	-
8.8039	898	1.3643	-	-	-	-
8.8137	899	0.8717	-	-	-	-
8.8235	900	1.0883	-	-	-	-
8.8333	901	2.4553	-	-	-	-
8.8431	902	0.8967	-	-	-	-
8.8529	903	1.4815	-	-	-	-
8.8627	904	2.0851	-	-	-	-
8.8725	905	0.8294	-	-	-	-
8.8824	906	1.4176	-	-	-	-
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8.9020	908	0.8526	-	-	-	-
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8.9216	910	1.6507	-	-	-	-
8.9314	911	0.8236	-	-	-	-
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8.9510	913	0.1605	-	-	-	-
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8.9706	915	0.8835	-	-	-	-
8.9804	916	2.8156	-	-	-	-
8.9902	917	1.193	-	-	-	-
9.0	918	0.4156	0.4914	0.4730	0.4484	0.3994
9.0098	919	0.6944	-	-	-	-
9.0196	920	1.3762	-	-	-	-
9.0294	921	1.4685	-	-	-	-
9.0392	922	1.985	-	-	-	-
9.0490	923	1.532	-	-	-	-
9.0588	924	0.9062	-	-	-	-
9.0686	925	0.5014	-	-	-	-
9.0784	926	2.4734	-	-	-	-
9.0882	927	1.235	-	-	-	-
9.0980	928	2.0355	-	-	-	-
9.1078	929	1.8362	-	-	-	-
9.1176	930	0.2716	-	-	-	-
9.1275	931	2.4027	-	-	-	-
9.1373	932	1.987	-	-	-	-
9.1471	933	0.866	-	-	-	-
9.1569	934	0.5206	-	-	-	-
9.1667	935	1.1732	-	-	-	-
9.1765	936	0.5978	-	-	-	-
9.1863	937	1.4445	-	-	-	-
9.1961	938	1.563	-	-	-	-
9.2059	939	3.7164	-	-	-	-
9.2157	940	0.6754	-	-	-	-
9.2255	941	0.9696	-	-	-	-
9.2353	942	1.9793	-	-	-	-
9.2451	943	0.5348	-	-	-	-
9.2549	944	1.8555	-	-	-	-
9.2647	945	3.1624	-	-	-	-
9.2745	946	1.3702	-	-	-	-
9.2843	947	1.0587	-	-	-	-
9.2941	948	2.3264	-	-	-	-
9.3039	949	0.3401	-	-	-	-
9.3137	950	3.5269	-	-	-	-
9.3235	951	0.8789	-	-	-	-
9.3333	952	0.1871	-	-	-	-
9.3431	953	0.9429	-	-	-	-
9.3529	954	1.2789	-	-	-	-
9.3627	955	1.3322	-	-	-	-
9.3725	956	0.7246	-	-	-	-
9.3824	957	1.335	-	-	-	-
9.3922	958	0.4446	-	-	-	-
9.4020	959	0.7804	-	-	-	-
9.4118	960	3.2016	-	-	-	-
9.4216	961	2.9954	-	-	-	-
9.4314	962	0.4036	-	-	-	-
9.4412	963	1.893	-	-	-	-
9.4510	964	2.0831	-	-	-	-
9.4608	965	0.4715	-	-	-	-
9.4706	966	1.2693	-	-	-	-
9.4804	967	1.2715	-	-	-	-
9.4902	968	2.7172	-	-	-	-
9.5	969	0.6332	-	-	-	-
9.5098	970	1.9209	-	-	-	-
9.5196	971	1.7342	-	-	-	-
9.5294	972	0.6174	-	-	-	-
9.5392	973	1.0254	-	-	-	-
9.5490	974	1.6523	-	-	-	-
9.5588	975	1.0256	-	-	-	-
9.5686	976	1.7213	-	-	-	-
9.5784	977	0.6547	-	-	-	-
9.5882	978	1.0245	-	-	-	-
9.5980	979	1.0071	-	-	-	-
9.6078	980	1.8322	-	-	-	-
9.6176	981	1.1578	-	-	-	-
9.6275	982	0.3978	-	-	-	-
9.6373	983	1.6247	-	-	-	-
9.6471	984	1.4199	-	-	-	-
9.6569	985	1.0348	-	-	-	-
9.6667	986	1.7685	-	-	-	-
9.6765	987	1.9384	-	-	-	-
9.6863	988	1.0472	-	-	-	-
9.6961	989	1.5427	-	-	-	-
9.7059	990	2.572	-	-	-	-
9.7157	991	2.0624	-	-	-	-
9.7255	992	2.6882	-	-	-	-
9.7353	993	1.4118	-	-	-	-
9.7451	994	3.1801	-	-	-	-
9.7549	995	0.2857	-	-	-	-
9.7647	996	0.5164	-	-	-	-
9.7745	997	1.3223	-	-	-	-
9.7843	998	1.1611	-	-	-	-
9.7941	999	2.4415	-	-	-	-
9.8039	1000	1.4446	-	-	-	-
9.8137	1001	1.094	-	-	-	-
9.8235	1002	1.8717	-	-	-	-
9.8333	1003	1.6525	-	-	-	-
9.8431	1004	0.5387	-	-	-	-
9.8529	1005	0.2567	-	-	-	-
9.8627	1006	1.4519	-	-	-	-
9.8725	1007	0.567	-	-	-	-
9.8824	1008	0.6905	-	-	-	-
9.8922	1009	0.8775	-	-	-	-
9.9020	1010	1.9415	-	-	-	-
9.9118	1011	1.538	-	-	-	-
9.9216	1012	1.0921	-	-	-	-
9.9314	1013	2.3688	-	-	-	-
9.9412	1014	1.3336	-	-	-	-
9.9510	1015	2.078	-	-	-	-
9.9608	1016	0.9699	-	-	-	-
9.9706	1017	0.338	-	-	-	-
9.9804	1018	0.4844	-	-	-	-
9.9902	1019	0.8789	-	-	-	-
10.0	1020	0.6449	0.4912	0.473	0.4526	0.4019

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

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 15.35 tokens max: 34 tokens	min: 25 tokens mean: 221.19 tokens max: 256 tokens

IoannisKat1/all-MiniLM-L6-v2-ft-new

all_MiniLM_L6_v2 Finetuned on Data

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

IoannisKat1/
all-MiniLM-L6-v2-ft-new