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legal_bert_base_uncased Finetuned on Data

Name: legal-bert-base-uncased-ft-new
Availability: InStock

This is a sentence-transformers model finetuned from nlpaueb/legal-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: nlpaueb/legal-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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    "In what context does the operation of the democratic system in a Member State require political parties to compile personal data on people's political opinions?",
    "Where in the course of electoral activities, the operation of the democratic system in a Member State requires that political parties compile personal data on people's political opinions, the processing of such data may be permitted for reasons of public interest, provided that appropriate safeguards are established.",
    '1.The data subject shall have the right to obtain from the controller confirmation as to whether or not personal data concerning him or her are being processed, and, where that is the case, access to the personal data and the following information: (a)  the purposes of the processing; (b)  the categories of personal data concerned; (c)  the recipients or categories of recipient to whom the personal data have been or will be disclosed, in particular recipients in third countries or international organisations; (d)  where possible, the envisaged period for which the personal data will be stored, or, if not possible, the criteria used to determine that period; (e)  the existence of the right to request from the controller rectification or erasure of personal data or restriction of processing of personal data concerning the data subject or to object to such processing; (f)  the right to lodge a complaint with a supervisory authority; (g)  where the personal data are not collected from the data subject, any available information as to their source; (h)  the existence of automated decision-making, including profiling, referred to in Article 22(1) and (4) and, at least in those cases, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.\n2.Where personal data are transferred to a third country or to an international organisation, the data subject shall have the right to be informed of the appropriate safeguards pursuant to Article 46 relating to the transfer.\n3.The controller shall provide a copy of the personal data undergoing processing. For any further copies requested by the data subject, the controller may charge a reasonable fee based on administrative costs. Where the data subject makes the request by electronic means, and unless otherwise requested by the data subject, the information shall be provided in a commonly used electronic form.\n4.The right to obtain a copy referred to in paragraph 3 shall not adversely affect the rights and freedoms of others. Section 3 Rectification and erasure',
]
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, 0.8307, 0.1443],
#         [0.8307, 1.0000, 0.1468],
#         [0.1443, 0.1468, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.4079
cosine_accuracy@3	0.4521
cosine_accuracy@5	0.4988
cosine_accuracy@10	0.5627
cosine_precision@1	0.4079
cosine_precision@3	0.407
cosine_precision@5	0.398
cosine_precision@10	0.3681
cosine_recall@1	0.0629
cosine_recall@3	0.1767
cosine_recall@5	0.2581
cosine_recall@10	0.397
cosine_ndcg@10	0.4802
cosine_mrr@10	0.4422
cosine_map@100	0.5349

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.4201
cosine_accuracy@3	0.4595
cosine_accuracy@5	0.5037
cosine_accuracy@10	0.5725
cosine_precision@1	0.4201
cosine_precision@3	0.4185
cosine_precision@5	0.4079
cosine_precision@10	0.3737
cosine_recall@1	0.0639
cosine_recall@3	0.178
cosine_recall@5	0.2597
cosine_recall@10	0.3948
cosine_ndcg@10	0.4862
cosine_mrr@10	0.4528
cosine_map@100	0.5415

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.4029
cosine_accuracy@3	0.4423
cosine_accuracy@5	0.4717
cosine_accuracy@10	0.5479
cosine_precision@1	0.4029
cosine_precision@3	0.4013
cosine_precision@5	0.3877
cosine_precision@10	0.3587
cosine_recall@1	0.0619
cosine_recall@3	0.1719
cosine_recall@5	0.2476
cosine_recall@10	0.3811
cosine_ndcg@10	0.4679
cosine_mrr@10	0.434
cosine_map@100	0.5272

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.4054
cosine_accuracy@3	0.4496
cosine_accuracy@5	0.4742
cosine_accuracy@10	0.5258
cosine_precision@1	0.4054
cosine_precision@3	0.3997
cosine_precision@5	0.3838
cosine_precision@10	0.3511
cosine_recall@1	0.0673
cosine_recall@3	0.1777
cosine_recall@5	0.2456
cosine_recall@10	0.3598
cosine_ndcg@10	0.4621
cosine_mrr@10	0.4332
cosine_map@100	0.5189

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.3833
cosine_accuracy@3	0.4251
cosine_accuracy@5	0.4521
cosine_accuracy@10	0.5135
cosine_precision@1	0.3833
cosine_precision@3	0.3833
cosine_precision@5	0.373
cosine_precision@10	0.343
cosine_recall@1	0.0568
cosine_recall@3	0.1575
cosine_recall@5	0.2312
cosine_recall@10	0.3402
cosine_ndcg@10	0.4408
cosine_mrr@10	0.4121
cosine_map@100	0.5014

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: 15.21 tokens
max: 36 tokens
min: 25 tokens
mean: 369.39 tokens
max: 512 tokens

Samples:

anchor	positive
`What is the purpose of the publicly accessible electronic register maintained by the authority?`	1.The Board shall ensure the consistent application of this Regulation. To that end, the Board shall, on its own initiative or, where relevant, at the request of the Commission, in particular: (a) monitor and ensure the correct application of this Regulation in the cases provided for in Articles 64 and 65 without prejudice to the tasks of national supervisory authorities; 4.5.2016 L 119/76 (b) advise the Commission on any issue related to the protection of personal data in the Union, including on any proposed amendment of this Regulation; (c) advise the Commission on the format and procedures for the exchange of information between controllers, processors and supervisory authorities for binding corporate rules; (d) issue guidelines, recommendations, and best practices on procedures for erasing links, copies or replications of personal data from publicly available communication services as referred to in Article 17(2); (e) examine, on its own initiative, on request of one of its ...
`How should the request for consent be presented?`	1.Where processing is based on consent, the controller shall be able to demonstrate that the data subject has consented to processing of his or her personal data.[object Object]2.If the data subject's consent is given in the context of a written declaration which also concerns other matters, the request for consent shall be presented in a manner which is clearly distinguishable from the other matters, in an intelligible and easily accessible form, using clear and plain language. Any part of such a declaration which constitutes an infringement of this Regulation shall not be binding.[object Object]3.The data subject shall have the right to withdraw his or her consent at any time. The withdrawal of consent shall not affect the lawfulness of processing based on consent before its withdrawal. Prior to giving consent, the data subject shall be informed thereof. It shall be as easy to withdraw as to give consent.[object Object]4.When assessing whether consent is freely given, utmost account shall be taken of whether, inter alia, the...
`What type of clauses is the European Data Protection Committee encouraged to adopt under point (j)?`	1.Without prejudice to other tasks set out under this Regulation, each supervisory authority shall on its territory: (a) monitor and enforce the application of this Regulation; (b) promote public awareness and understanding of the risks, rules, safeguards and rights in relation to processing. Activities addressed specifically to children shall receive specific attention; (c) advise, in accordance with Member State law, the national parliament, the government, and other institutions and bodies on legislative and administrative measures relating to the protection of natural persons' rights and freedoms with regard to processing; (d) promote the awareness of controllers and processors of their obligations under this Regulation; (e) upon request, provide information to any data subject concerning the exercise of their rights under this Regulation and, if appropriate, cooperate with the supervisory authorities in other Member States to that end; (f) handle complaints lodged by a data ...

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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	16.142	-	-	-	-	-
0.0196	2	13.6979	-	-	-	-	-
0.0294	3	17.5356	-	-	-	-	-
0.0392	4	14.932	-	-	-	-	-
0.0490	5	19.2064	-	-	-	-	-
0.0588	6	16.981	-	-	-	-	-
0.0686	7	17.1026	-	-	-	-	-
0.0784	8	15.6905	-	-	-	-	-
0.0882	9	18.223	-	-	-	-	-
0.0980	10	15.6634	-	-	-	-	-
0.1078	11	21.4638	-	-	-	-	-
0.1176	12	18.1468	-	-	-	-	-
0.1275	13	17.1178	-	-	-	-	-
0.1373	14	15.877	-	-	-	-	-
0.1471	15	15.1227	-	-	-	-	-
0.1569	16	14.0964	-	-	-	-	-
0.1667	17	17.4772	-	-	-	-	-
0.1765	18	19.1766	-	-	-	-	-
0.1863	19	14.6675	-	-	-	-	-
0.1961	20	13.2606	-	-	-	-	-
0.2059	21	18.2742	-	-	-	-	-
0.2157	22	18.0096	-	-	-	-	-
0.2255	23	12.3101	-	-	-	-	-
0.2353	24	14.0233	-	-	-	-	-
0.2451	25	12.9841	-	-	-	-	-
0.2549	26	13.7307	-	-	-	-	-
0.2647	27	16.5663	-	-	-	-	-
0.2745	28	13.9413	-	-	-	-	-
0.2843	29	15.4937	-	-	-	-	-
0.2941	30	12.2452	-	-	-	-	-
0.3039	31	11.8533	-	-	-	-	-
0.3137	32	12.9059	-	-	-	-	-
0.3235	33	13.6171	-	-	-	-	-
0.3333	34	15.7267	-	-	-	-	-
0.3431	35	11.0184	-	-	-	-	-
0.3529	36	13.0709	-	-	-	-	-
0.3627	37	13.0111	-	-	-	-	-
0.3725	38	12.1237	-	-	-	-	-
0.3824	39	12.1466	-	-	-	-	-
0.3922	40	13.1446	-	-	-	-	-
0.4020	41	14.607	-	-	-	-	-
0.4118	42	13.0565	-	-	-	-	-
0.4216	43	11.0872	-	-	-	-	-
0.4314	44	13.9679	-	-	-	-	-
0.4412	45	11.4318	-	-	-	-	-
0.4510	46	13.5983	-	-	-	-	-
0.4608	47	13.3464	-	-	-	-	-
0.4706	48	10.2001	-	-	-	-	-
0.4804	49	12.6368	-	-	-	-	-
0.4902	50	14.6145	-	-	-	-	-
0.5	51	10.4465	-	-	-	-	-
0.5098	52	11.4625	-	-	-	-	-
0.5196	53	13.0207	-	-	-	-	-
0.5294	54	14.5234	-	-	-	-	-
0.5392	55	13.5372	-	-	-	-	-
0.5490	56	9.3016	-	-	-	-	-
0.5588	57	10.8238	-	-	-	-	-
0.5686	58	13.8616	-	-	-	-	-
0.5784	59	11.3745	-	-	-	-	-
0.5882	60	9.5075	-	-	-	-	-
0.5980	61	10.5469	-	-	-	-	-
0.6078	62	10.9505	-	-	-	-	-
0.6176	63	9.7053	-	-	-	-	-
0.6275	64	13.7708	-	-	-	-	-
0.6373	65	8.9201	-	-	-	-	-
0.6471	66	7.1201	-	-	-	-	-
0.6569	67	10.5854	-	-	-	-	-
0.6667	68	10.8858	-	-	-	-	-
0.6765	69	5.099	-	-	-	-	-
0.6863	70	7.2317	-	-	-	-	-
0.6961	71	12.0098	-	-	-	-	-
0.7059	72	11.1372	-	-	-	-	-
0.7157	73	13.2915	-	-	-	-	-
0.7255	74	7.666	-	-	-	-	-
0.7353	75	8.9583	-	-	-	-	-
0.7451	76	14.4897	-	-	-	-	-
0.7549	77	5.2462	-	-	-	-	-
0.7647	78	8.6225	-	-	-	-	-
0.7745	79	11.8394	-	-	-	-	-
0.7843	80	13.6184	-	-	-	-	-
0.7941	81	8.3545	-	-	-	-	-
0.8039	82	10.7644	-	-	-	-	-
0.8137	83	11.0633	-	-	-	-	-
0.8235	84	12.6646	-	-	-	-	-
0.8333	85	9.8459	-	-	-	-	-
0.8431	86	9.6683	-	-	-	-	-
0.8529	87	8.6824	-	-	-	-	-
0.8627	88	11.2598	-	-	-	-	-
0.8725	89	7.7519	-	-	-	-	-
0.8824	90	6.2339	-	-	-	-	-
0.8922	91	12.3876	-	-	-	-	-
0.9020	92	9.0571	-	-	-	-	-
0.9118	93	11.2924	-	-	-	-	-
0.9216	94	9.8345	-	-	-	-	-
0.9314	95	11.6696	-	-	-	-	-
0.9412	96	4.8431	-	-	-	-	-
0.9510	97	6.9415	-	-	-	-	-
0.9608	98	6.5089	-	-	-	-	-
0.9706	99	7.2339	-	-	-	-	-
0.9804	100	6.9042	-	-	-	-	-
0.9902	101	7.3693	-	-	-	-	-
1.0	102	7.3341	0.3214	0.3273	0.3465	0.3125	0.2972
1.0098	103	2.4598	-	-	-	-	-
1.0196	104	6.2536	-	-	-	-	-
1.0294	105	3.4257	-	-	-	-	-
1.0392	106	3.8891	-	-	-	-	-
1.0490	107	4.9762	-	-	-	-	-
1.0588	108	7.4206	-	-	-	-	-
1.0686	109	7.4156	-	-	-	-	-
1.0784	110	7.7509	-	-	-	-	-
1.0882	111	7.8635	-	-	-	-	-
1.0980	112	5.5645	-	-	-	-	-
1.1078	113	4.4094	-	-	-	-	-
1.1176	114	7.3678	-	-	-	-	-
1.1275	115	5.9872	-	-	-	-	-
1.1373	116	10.9788	-	-	-	-	-
1.1471	117	10.184	-	-	-	-	-
1.1569	118	9.0405	-	-	-	-	-
1.1667	119	4.9606	-	-	-	-	-
1.1765	120	5.9992	-	-	-	-	-
1.1863	121	4.846	-	-	-	-	-
1.1961	122	9.0015	-	-	-	-	-
1.2059	123	3.7348	-	-	-	-	-
1.2157	124	5.6321	-	-	-	-	-
1.2255	125	5.4282	-	-	-	-	-
1.2353	126	4.7605	-	-	-	-	-
1.2451	127	6.4746	-	-	-	-	-
1.2549	128	5.2789	-	-	-	-	-
1.2647	129	8.5016	-	-	-	-	-
1.2745	130	5.6314	-	-	-	-	-
1.2843	131	5.0657	-	-	-	-	-
1.2941	132	5.4924	-	-	-	-	-
1.3039	133	4.5245	-	-	-	-	-
1.3137	134	3.4378	-	-	-	-	-
1.3235	135	9.0247	-	-	-	-	-
1.3333	136	8.8494	-	-	-	-	-
1.3431	137	5.8472	-	-	-	-	-
1.3529	138	9.1909	-	-	-	-	-
1.3627	139	7.2911	-	-	-	-	-
1.3725	140	4.8004	-	-	-	-	-
1.3824	141	5.212	-	-	-	-	-
1.3922	142	6.5621	-	-	-	-	-
1.4020	143	4.5448	-	-	-	-	-
1.4118	144	6.5068	-	-	-	-	-
1.4216	145	3.3916	-	-	-	-	-
1.4314	146	4.8277	-	-	-	-	-
1.4412	147	7.9691	-	-	-	-	-
1.4510	148	5.8588	-	-	-	-	-
1.4608	149	4.7892	-	-	-	-	-
1.4706	150	6.8099	-	-	-	-	-
1.4804	151	8.754	-	-	-	-	-
1.4902	152	7.0441	-	-	-	-	-
1.5	153	9.6257	-	-	-	-	-
1.5098	154	2.521	-	-	-	-	-
1.5196	155	5.5603	-	-	-	-	-
1.5294	156	1.9541	-	-	-	-	-
1.5392	157	5.8526	-	-	-	-	-
1.5490	158	7.6458	-	-	-	-	-
1.5588	159	6.384	-	-	-	-	-
1.5686	160	5.6423	-	-	-	-	-
1.5784	161	5.5204	-	-	-	-	-
1.5882	162	7.6642	-	-	-	-	-
1.5980	163	3.7946	-	-	-	-	-
1.6078	164	4.2481	-	-	-	-	-
1.6176	165	4.4644	-	-	-	-	-
1.6275	166	4.674	-	-	-	-	-
1.6373	167	3.3481	-	-	-	-	-
1.6471	168	10.6918	-	-	-	-	-
1.6569	169	5.1483	-	-	-	-	-
1.6667	170	5.0832	-	-	-	-	-
1.6765	171	4.7421	-	-	-	-	-
1.6863	172	4.2187	-	-	-	-	-
1.6961	173	6.6807	-	-	-	-	-
1.7059	174	2.2695	-	-	-	-	-
1.7157	175	2.53	-	-	-	-	-
1.7255	176	6.0218	-	-	-	-	-
1.7353	177	8.7176	-	-	-	-	-
1.7451	178	6.9877	-	-	-	-	-
1.7549	179	7.2235	-	-	-	-	-
1.7647	180	2.7433	-	-	-	-	-
1.7745	181	4.1857	-	-	-	-	-
1.7843	182	8.8419	-	-	-	-	-
1.7941	183	4.2731	-	-	-	-	-
1.8039	184	5.9068	-	-	-	-	-
1.8137	185	3.8098	-	-	-	-	-
1.8235	186	3.6064	-	-	-	-	-
1.8333	187	7.5581	-	-	-	-	-
1.8431	188	5.1475	-	-	-	-	-
1.8529	189	0.9157	-	-	-	-	-
1.8627	190	5.8079	-	-	-	-	-
1.8725	191	3.7	-	-	-	-	-
1.8824	192	5.0955	-	-	-	-	-
1.8922	193	4.1939	-	-	-	-	-
1.9020	194	9.5075	-	-	-	-	-
1.9118	195	8.3701	-	-	-	-	-
1.9216	196	4.8127	-	-	-	-	-
1.9314	197	10.9187	-	-	-	-	-
1.9412	198	3.5791	-	-	-	-	-
1.9510	199	11.3306	-	-	-	-	-
1.9608	200	6.3793	-	-	-	-	-
1.9706	201	4.1617	-	-	-	-	-
1.9804	202	13.2734	-	-	-	-	-
1.9902	203	7.1173	-	-	-	-	-
2.0	204	2.266	0.3906	0.3760	0.3709	0.3645	0.3147
2.0098	205	3.1302	-	-	-	-	-
2.0196	206	1.3766	-	-	-	-	-
2.0294	207	3.3127	-	-	-	-	-
2.0392	208	2.5981	-	-	-	-	-
2.0490	209	3.4364	-	-	-	-	-
2.0588	210	2.6902	-	-	-	-	-
2.0686	211	2.2741	-	-	-	-	-
2.0784	212	3.3271	-	-	-	-	-
2.0882	213	2.7175	-	-	-	-	-
2.0980	214	6.3425	-	-	-	-	-
2.1078	215	6.2902	-	-	-	-	-
2.1176	216	3.145	-	-	-	-	-
2.1275	217	2.8716	-	-	-	-	-
2.1373	218	2.3294	-	-	-	-	-
2.1471	219	2.1109	-	-	-	-	-
2.1569	220	0.5804	-	-	-	-	-
2.1667	221	3.2296	-	-	-	-	-
2.1765	222	2.5376	-	-	-	-	-
2.1863	223	2.3902	-	-	-	-	-
2.1961	224	3.6877	-	-	-	-	-
2.2059	225	5.9844	-	-	-	-	-
2.2157	226	1.8207	-	-	-	-	-
2.2255	227	4.5828	-	-	-	-	-
2.2353	228	2.2631	-	-	-	-	-
2.2451	229	2.9204	-	-	-	-	-
2.2549	230	2.8955	-	-	-	-	-
2.2647	231	2.2151	-	-	-	-	-
2.2745	232	2.4886	-	-	-	-	-
2.2843	233	4.7441	-	-	-	-	-
2.2941	234	2.8377	-	-	-	-	-
2.3039	235	5.3201	-	-	-	-	-
2.3137	236	1.1322	-	-	-	-	-
2.3235	237	3.7221	-	-	-	-	-
2.3333	238	2.9845	-	-	-	-	-
2.3431	239	0.8198	-	-	-	-	-
2.3529	240	3.2936	-	-	-	-	-
2.3627	241	1.4042	-	-	-	-	-
2.3725	242	4.6838	-	-	-	-	-
2.3824	243	6.6713	-	-	-	-	-
2.3922	244	4.7596	-	-	-	-	-
2.4020	245	5.5073	-	-	-	-	-
2.4118	246	5.8471	-	-	-	-	-
2.4216	247	2.5645	-	-	-	-	-
2.4314	248	4.8164	-	-	-	-	-
2.4412	249	3.479	-	-	-	-	-
2.4510	250	4.7469	-	-	-	-	-
2.4608	251	3.8473	-	-	-	-	-
2.4706	252	1.9741	-	-	-	-	-
2.4804	253	4.2415	-	-	-	-	-
2.4902	254	5.7806	-	-	-	-	-
2.5	255	4.1767	-	-	-	-	-
2.5098	256	1.4454	-	-	-	-	-
2.5196	257	6.4449	-	-	-	-	-
2.5294	258	2.5377	-	-	-	-	-
2.5392	259	5.2794	-	-	-	-	-
2.5490	260	6.4417	-	-	-	-	-
2.5588	261	2.2805	-	-	-	-	-
2.5686	262	1.5983	-	-	-	-	-
2.5784	263	4.0917	-	-	-	-	-
2.5882	264	5.2794	-	-	-	-	-
2.5980	265	5.2811	-	-	-	-	-
2.6078	266	2.5164	-	-	-	-	-
2.6176	267	4.1126	-	-	-	-	-
2.6275	268	3.4238	-	-	-	-	-
2.6373	269	3.0646	-	-	-	-	-
2.6471	270	5.9979	-	-	-	-	-
2.6569	271	2.6063	-	-	-	-	-
2.6667	272	2.7587	-	-	-	-	-
2.6765	273	4.4089	-	-	-	-	-
2.6863	274	4.606	-	-	-	-	-
2.6961	275	2.1391	-	-	-	-	-
2.7059	276	2.9717	-	-	-	-	-
2.7157	277	2.9827	-	-	-	-	-
2.7255	278	4.3785	-	-	-	-	-
2.7353	279	1.3594	-	-	-	-	-
2.7451	280	3.4222	-	-	-	-	-
2.7549	281	4.1147	-	-	-	-	-
2.7647	282	1.4041	-	-	-	-	-
2.7745	283	3.4664	-	-	-	-	-
2.7843	284	4.6955	-	-	-	-	-
2.7941	285	7.5589	-	-	-	-	-
2.8039	286	2.2981	-	-	-	-	-
2.8137	287	1.9319	-	-	-	-	-
2.8235	288	3.8581	-	-	-	-	-
2.8333	289	1.486	-	-	-	-	-
2.8431	290	1.9626	-	-	-	-	-
2.8529	291	3.8278	-	-	-	-	-
2.8627	292	4.7401	-	-	-	-	-
2.8725	293	1.4546	-	-	-	-	-
2.8824	294	1.695	-	-	-	-	-
2.8922	295	1.5778	-	-	-	-	-
2.9020	296	0.7612	-	-	-	-	-
2.9118	297	3.9774	-	-	-	-	-
2.9216	298	1.601	-	-	-	-	-
2.9314	299	1.0608	-	-	-	-	-
2.9412	300	0.9563	-	-	-	-	-
2.9510	301	3.8269	-	-	-	-	-
2.9608	302	2.8383	-	-	-	-	-
2.9706	303	0.8521	-	-	-	-	-
2.9804	304	3.6911	-	-	-	-	-
2.9902	305	2.9831	-	-	-	-	-
3.0	306	2.86	0.4383	0.4420	0.4243	0.4179	0.3780
3.0098	307	2.1505	-	-	-	-	-
3.0196	308	1.951	-	-	-	-	-
3.0294	309	1.0424	-	-	-	-	-
3.0392	310	1.0313	-	-	-	-	-
3.0490	311	2.3466	-	-	-	-	-
3.0588	312	2.8182	-	-	-	-	-
3.0686	313	2.7665	-	-	-	-	-
3.0784	314	3.3207	-	-	-	-	-
3.0882	315	0.5337	-	-	-	-	-
3.0980	316	5.9269	-	-	-	-	-
3.1078	317	1.4661	-	-	-	-	-
3.1176	318	2.2047	-	-	-	-	-
3.1275	319	0.4942	-	-	-	-	-
3.1373	320	0.3199	-	-	-	-	-
3.1471	321	2.6207	-	-	-	-	-
3.1569	322	1.66	-	-	-	-	-
3.1667	323	1.6875	-	-	-	-	-
3.1765	324	0.9947	-	-	-	-	-
3.1863	325	2.8068	-	-	-	-	-
3.1961	326	0.7081	-	-	-	-	-
3.2059	327	2.8691	-	-	-	-	-
3.2157	328	2.2257	-	-	-	-	-
3.2255	329	2.6505	-	-	-	-	-
3.2353	330	0.5006	-	-	-	-	-
3.2451	331	0.8226	-	-	-	-	-
3.2549	332	0.7512	-	-	-	-	-
3.2647	333	3.9588	-	-	-	-	-
3.2745	334	6.0112	-	-	-	-	-
3.2843	335	4.641	-	-	-	-	-
3.2941	336	5.446	-	-	-	-	-
3.3039	337	2.1972	-	-	-	-	-
3.3137	338	0.7079	-	-	-	-	-
3.3235	339	1.2027	-	-	-	-	-
3.3333	340	3.0418	-	-	-	-	-
3.3431	341	4.8786	-	-	-	-	-
3.3529	342	1.3315	-	-	-	-	-
3.3627	343	4.3283	-	-	-	-	-
3.3725	344	1.1935	-	-	-	-	-
3.3824	345	2.4532	-	-	-	-	-
3.3922	346	1.9306	-	-	-	-	-
3.4020	347	2.1302	-	-	-	-	-
3.4118	348	0.8763	-	-	-	-	-
3.4216	349	2.3134	-	-	-	-	-
3.4314	350	1.1645	-	-	-	-	-
3.4412	351	4.0291	-	-	-	-	-
3.4510	352	2.413	-	-	-	-	-
3.4608	353	2.2474	-	-	-	-	-
3.4706	354	0.4758	-	-	-	-	-
3.4804	355	3.3463	-	-	-	-	-
3.4902	356	2.6824	-	-	-	-	-
3.5	357	2.0532	-	-	-	-	-
3.5098	358	4.2147	-	-	-	-	-
3.5196	359	0.6906	-	-	-	-	-
3.5294	360	4.5964	-	-	-	-	-
3.5392	361	3.9468	-	-	-	-	-
3.5490	362	0.2993	-	-	-	-	-
3.5588	363	0.5449	-	-	-	-	-
3.5686	364	6.1468	-	-	-	-	-
3.5784	365	3.3679	-	-	-	-	-
3.5882	366	0.2183	-	-	-	-	-
3.5980	367	3.1249	-	-	-	-	-
3.6078	368	0.2242	-	-	-	-	-
3.6176	369	2.8286	-	-	-	-	-
3.6275	370	4.7574	-	-	-	-	-
3.6373	371	1.8525	-	-	-	-	-
3.6471	372	0.3502	-	-	-	-	-
3.6569	373	0.4283	-	-	-	-	-
3.6667	374	1.4765	-	-	-	-	-
3.6765	375	1.4383	-	-	-	-	-
3.6863	376	3.0731	-	-	-	-	-
3.6961	377	7.8687	-	-	-	-	-
3.7059	378	3.4209	-	-	-	-	-
3.7157	379	3.5559	-	-	-	-	-
3.7255	380	5.7134	-	-	-	-	-
3.7353	381	0.3762	-	-	-	-	-
3.7451	382	4.7601	-	-	-	-	-
3.7549	383	2.5504	-	-	-	-	-
3.7647	384	2.0682	-	-	-	-	-
3.7745	385	2.3374	-	-	-	-	-
3.7843	386	0.8238	-	-	-	-	-
3.7941	387	1.3386	-	-	-	-	-
3.8039	388	3.9282	-	-	-	-	-
3.8137	389	3.3598	-	-	-	-	-
3.8235	390	1.2969	-	-	-	-	-
3.8333	391	2.7564	-	-	-	-	-
3.8431	392	4.6597	-	-	-	-	-
3.8529	393	3.5919	-	-	-	-	-
3.8627	394	1.4537	-	-	-	-	-
3.8725	395	2.0461	-	-	-	-	-
3.8824	396	1.5874	-	-	-	-	-
3.8922	397	1.3049	-	-	-	-	-
3.9020	398	2.3358	-	-	-	-	-
3.9118	399	3.2675	-	-	-	-	-
3.9216	400	5.0215	-	-	-	-	-
3.9314	401	2.3915	-	-	-	-	-
3.9412	402	4.3487	-	-	-	-	-
3.9510	403	1.9832	-	-	-	-	-
3.9608	404	3.0072	-	-	-	-	-
3.9706	405	0.9291	-	-	-	-	-
3.9804	406	1.8248	-	-	-	-	-
3.9902	407	2.9961	-	-	-	-	-
4.0	408	1.1937	0.4626	0.4678	0.4523	0.4299	0.3836
4.0098	409	0.9064	-	-	-	-	-
4.0196	410	2.1557	-	-	-	-	-
4.0294	411	2.1057	-	-	-	-	-
4.0392	412	0.6088	-	-	-	-	-
4.0490	413	0.2606	-	-	-	-	-
4.0588	414	1.1907	-	-	-	-	-
4.0686	415	0.6167	-	-	-	-	-
4.0784	416	1.0502	-	-	-	-	-
4.0882	417	3.8215	-	-	-	-	-
4.0980	418	2.3614	-	-	-	-	-
4.1078	419	2.3688	-	-	-	-	-
4.1176	420	1.1455	-	-	-	-	-
4.1275	421	1.1536	-	-	-	-	-
4.1373	422	2.409	-	-	-	-	-
4.1471	423	0.3791	-	-	-	-	-
4.1569	424	1.0055	-	-	-	-	-
4.1667	425	0.5284	-	-	-	-	-
4.1765	426	1.223	-	-	-	-	-
4.1863	427	0.7022	-	-	-	-	-
4.1961	428	2.4283	-	-	-	-	-
4.2059	429	1.8243	-	-	-	-	-
4.2157	430	0.6676	-	-	-	-	-
4.2255	431	1.79	-	-	-	-	-
4.2353	432	1.0209	-	-	-	-	-
4.2451	433	1.2226	-	-	-	-	-
4.2549	434	6.2845	-	-	-	-	-
4.2647	435	1.84	-	-	-	-	-
4.2745	436	2.1295	-	-	-	-	-
4.2843	437	6.0545	-	-	-	-	-
4.2941	438	2.5843	-	-	-	-	-
4.3039	439	2.7702	-	-	-	-	-
4.3137	440	0.3411	-	-	-	-	-
4.3235	441	3.9539	-	-	-	-	-
4.3333	442	0.8735	-	-	-	-	-
4.3431	443	1.1179	-	-	-	-	-
4.3529	444	0.8603	-	-	-	-	-
4.3627	445	1.0603	-	-	-	-	-
4.3725	446	2.5723	-	-	-	-	-
4.3824	447	0.7826	-	-	-	-	-
4.3922	448	2.1126	-	-	-	-	-
4.4020	449	0.8591	-	-	-	-	-
4.4118	450	0.5417	-	-	-	-	-
4.4216	451	0.3693	-	-	-	-	-
4.4314	452	2.6074	-	-	-	-	-
4.4412	453	0.5343	-	-	-	-	-
4.4510	454	2.1009	-	-	-	-	-
4.4608	455	0.1991	-	-	-	-	-
4.4706	456	4.0662	-	-	-	-	-
4.4804	457	0.2067	-	-	-	-	-
4.4902	458	0.1804	-	-	-	-	-
4.5	459	0.7831	-	-	-	-	-
4.5098	460	0.2989	-	-	-	-	-
4.5196	461	3.5961	-	-	-	-	-
4.5294	462	1.0125	-	-	-	-	-
4.5392	463	6.6978	-	-	-	-	-
4.5490	464	0.916	-	-	-	-	-
4.5588	465	1.3722	-	-	-	-	-
4.5686	466	0.8413	-	-	-	-	-
4.5784	467	0.8699	-	-	-	-	-
4.5882	468	1.158	-	-	-	-	-
4.5980	469	1.9805	-	-	-	-	-
4.6078	470	1.5379	-	-	-	-	-
4.6176	471	2.1312	-	-	-	-	-
4.6275	472	1.2694	-	-	-	-	-
4.6373	473	1.1249	-	-	-	-	-
4.6471	474	0.2574	-	-	-	-	-
4.6569	475	0.298	-	-	-	-	-
4.6667	476	0.993	-	-	-	-	-
4.6765	477	1.1082	-	-	-	-	-
4.6863	478	0.5371	-	-	-	-	-
4.6961	479	1.3218	-	-	-	-	-
4.7059	480	0.8123	-	-	-	-	-
4.7157	481	1.2833	-	-	-	-	-
4.7255	482	1.8921	-	-	-	-	-
4.7353	483	2.6904	-	-	-	-	-
4.7451	484	1.039	-	-	-	-	-
4.7549	485	0.6109	-	-	-	-	-
4.7647	486	0.1796	-	-	-	-	-
4.7745	487	1.8133	-	-	-	-	-
4.7843	488	2.3428	-	-	-	-	-
4.7941	489	0.5583	-	-	-	-	-
4.8039	490	1.7511	-	-	-	-	-
4.8137	491	4.4125	-	-	-	-	-
4.8235	492	0.7261	-	-	-	-	-
4.8333	493	3.266	-	-	-	-	-
4.8431	494	1.4778	-	-	-	-	-
4.8529	495	0.4726	-	-	-	-	-
4.8627	496	2.017	-	-	-	-	-
4.8725	497	4.986	-	-	-	-	-
4.8824	498	1.3917	-	-	-	-	-
4.8922	499	3.241	-	-	-	-	-
4.9020	500	0.8305	-	-	-	-	-
4.9118	501	2.1252	-	-	-	-	-
4.9216	502	0.5237	-	-	-	-	-
4.9314	503	3.5636	-	-	-	-	-
4.9412	504	1.1303	-	-	-	-	-
4.9510	505	0.4488	-	-	-	-	-
4.9608	506	1.2867	-	-	-	-	-
4.9706	507	2.4835	-	-	-	-	-
4.9804	508	1.8102	-	-	-	-	-
4.9902	509	0.4084	-	-	-	-	-
5.0	510	0.4287	0.4877	0.4856	0.4594	0.4635	0.4196
5.0098	511	1.4083	-	-	-	-	-
5.0196	512	0.1373	-	-	-	-	-
5.0294	513	0.2411	-	-	-	-	-
5.0392	514	0.9886	-	-	-	-	-
5.0490	515	1.0	-	-	-	-	-
5.0588	516	0.1746	-	-	-	-	-
5.0686	517	0.3335	-	-	-	-	-
5.0784	518	0.7792	-	-	-	-	-
5.0882	519	0.9675	-	-	-	-	-
5.0980	520	1.2497	-	-	-	-	-
5.1078	521	0.3268	-	-	-	-	-
5.1176	522	0.0865	-	-	-	-	-
5.1275	523	0.8019	-	-	-	-	-
5.1373	524	1.9829	-	-	-	-	-
5.1471	525	0.715	-	-	-	-	-
5.1569	526	0.8971	-	-	-	-	-
5.1667	527	0.8267	-	-	-	-	-
5.1765	528	1.4991	-	-	-	-	-
5.1863	529	1.8707	-	-	-	-	-
5.1961	530	1.0055	-	-	-	-	-
5.2059	531	1.6003	-	-	-	-	-
5.2157	532	0.3225	-	-	-	-	-
5.2255	533	1.1942	-	-	-	-	-
5.2353	534	0.0983	-	-	-	-	-
5.2451	535	0.2066	-	-	-	-	-
5.2549	536	0.473	-	-	-	-	-
5.2647	537	0.6633	-	-	-	-	-
5.2745	538	0.3503	-	-	-	-	-
5.2843	539	2.3147	-	-	-	-	-
5.2941	540	1.2655	-	-	-	-	-
5.3039	541	0.3521	-	-	-	-	-
5.3137	542	1.2058	-	-	-	-	-
5.3235	543	3.4029	-	-	-	-	-
5.3333	544	0.1811	-	-	-	-	-
5.3431	545	2.561	-	-	-	-	-
5.3529	546	0.5999	-	-	-	-	-
5.3627	547	0.7599	-	-	-	-	-
5.3725	548	0.6124	-	-	-	-	-
5.3824	549	0.1712	-	-	-	-	-
5.3922	550	1.5036	-	-	-	-	-
5.4020	551	2.1593	-	-	-	-	-
5.4118	552	1.3076	-	-	-	-	-
5.4216	553	1.4391	-	-	-	-	-
5.4314	554	1.3278	-	-	-	-	-
5.4412	555	0.3238	-	-	-	-	-
5.4510	556	2.1983	-	-	-	-	-
5.4608	557	1.3862	-	-	-	-	-
5.4706	558	0.3954	-	-	-	-	-
5.4804	559	0.2868	-	-	-	-	-
5.4902	560	0.2495	-	-	-	-	-
5.5	561	2.0504	-	-	-	-	-
5.5098	562	0.2046	-	-	-	-	-
5.5196	563	2.2235	-	-	-	-	-
5.5294	564	1.3584	-	-	-	-	-
5.5392	565	0.2429	-	-	-	-	-
5.5490	566	0.3024	-	-	-	-	-
5.5588	567	0.5984	-	-	-	-	-
5.5686	568	0.3191	-	-	-	-	-
5.5784	569	0.4595	-	-	-	-	-
5.5882	570	2.1861	-	-	-	-	-
5.5980	571	2.0487	-	-	-	-	-
5.6078	572	1.063	-	-	-	-	-
5.6176	573	0.4859	-	-	-	-	-
5.6275	574	0.7916	-	-	-	-	-
5.6373	575	1.8277	-	-	-	-	-
5.6471	576	1.966	-	-	-	-	-
5.6569	577	0.7466	-	-	-	-	-
5.6667	578	0.9443	-	-	-	-	-
5.6765	579	0.9522	-	-	-	-	-
5.6863	580	0.791	-	-	-	-	-
5.6961	581	0.9675	-	-	-	-	-
5.7059	582	0.253	-	-	-	-	-
5.7157	583	1.7913	-	-	-	-	-
5.7255	584	0.4794	-	-	-	-	-
5.7353	585	0.7508	-	-	-	-	-
5.7451	586	1.6652	-	-	-	-	-
5.7549	587	0.4571	-	-	-	-	-
5.7647	588	1.5655	-	-	-	-	-
5.7745	589	0.3066	-	-	-	-	-
5.7843	590	1.9775	-	-	-	-	-
5.7941	591	0.5368	-	-	-	-	-
5.8039	592	3.5144	-	-	-	-	-
5.8137	593	0.8008	-	-	-	-	-
5.8235	594	0.5214	-	-	-	-	-
5.8333	595	1.5262	-	-	-	-	-
5.8431	596	0.5599	-	-	-	-	-
5.8529	597	2.5003	-	-	-	-	-
5.8627	598	2.299	-	-	-	-	-
5.8725	599	0.4357	-	-	-	-	-
5.8824	600	1.3485	-	-	-	-	-
5.8922	601	0.9481	-	-	-	-	-
5.9020	602	1.9528	-	-	-	-	-
5.9118	603	1.2791	-	-	-	-	-
5.9216	604	1.2034	-	-	-	-	-
5.9314	605	0.5493	-	-	-	-	-
5.9412	606	0.3016	-	-	-	-	-
5.9510	607	2.149	-	-	-	-	-
5.9608	608	2.4052	-	-	-	-	-
5.9706	609	1.0008	-	-	-	-	-
5.9804	610	0.6253	-	-	-	-	-
5.9902	611	0.3332	-	-	-	-	-
6.0	612	0.0307	0.4805	0.4826	0.4801	0.4541	0.4263
6.0098	613	1.0663	-	-	-	-	-
6.0196	614	1.3709	-	-	-	-	-
6.0294	615	0.4196	-	-	-	-	-
6.0392	616	2.2074	-	-	-	-	-
6.0490	617	0.8465	-	-	-	-	-
6.0588	618	0.0855	-	-	-	-	-
6.0686	619	1.0775	-	-	-	-	-
6.0784	620	0.4405	-	-	-	-	-
6.0882	621	0.1843	-	-	-	-	-
6.0980	622	1.2837	-	-	-	-	-
6.1078	623	1.9711	-	-	-	-	-
6.1176	624	0.8662	-	-	-	-	-
6.1275	625	0.3973	-	-	-	-	-
6.1373	626	0.5848	-	-	-	-	-
6.1471	627	0.6703	-	-	-	-	-
6.1569	628	0.7336	-	-	-	-	-
6.1667	629	0.4416	-	-	-	-	-
6.1765	630	3.4996	-	-	-	-	-
6.1863	631	1.5145	-	-	-	-	-
6.1961	632	1.1684	-	-	-	-	-
6.2059	633	4.2434	-	-	-	-	-
6.2157	634	0.4169	-	-	-	-	-
6.2255	635	0.4279	-	-	-	-	-
6.2353	636	0.2271	-	-	-	-	-
6.2451	637	0.1208	-	-	-	-	-
6.2549	638	2.5412	-	-	-	-	-
6.2647	639	0.8021	-	-	-	-	-
6.2745	640	0.4896	-	-	-	-	-
6.2843	641	0.5744	-	-	-	-	-
6.2941	642	0.1416	-	-	-	-	-
6.3039	643	0.5951	-	-	-	-	-
6.3137	644	0.6001	-	-	-	-	-
6.3235	645	0.3476	-	-	-	-	-
6.3333	646	1.5666	-	-	-	-	-
6.3431	647	0.4627	-	-	-	-	-
6.3529	648	0.4656	-	-	-	-	-
6.3627	649	0.5176	-	-	-	-	-
6.3725	650	1.2141	-	-	-	-	-
6.3824	651	0.1015	-	-	-	-	-
6.3922	652	1.0091	-	-	-	-	-
6.4020	653	1.0072	-	-	-	-	-
6.4118	654	0.4348	-	-	-	-	-
6.4216	655	1.4236	-	-	-	-	-
6.4314	656	2.7582	-	-	-	-	-
6.4412	657	0.1325	-	-	-	-	-
6.4510	658	1.1764	-	-	-	-	-
6.4608	659	0.1993	-	-	-	-	-
6.4706	660	0.7112	-	-	-	-	-
6.4804	661	0.2277	-	-	-	-	-
6.4902	662	0.6941	-	-	-	-	-
6.5	663	0.0961	-	-	-	-	-
6.5098	664	0.1357	-	-	-	-	-
6.5196	665	0.9405	-	-	-	-	-
6.5294	666	2.4092	-	-	-	-	-
6.5392	667	0.5314	-	-	-	-	-
6.5490	668	1.6019	-	-	-	-	-
6.5588	669	0.2396	-	-	-	-	-
6.5686	670	0.7991	-	-	-	-	-
6.5784	671	0.1627	-	-	-	-	-
6.5882	672	1.1582	-	-	-	-	-
6.5980	673	0.1338	-	-	-	-	-
6.6078	674	0.7125	-	-	-	-	-
6.6176	675	2.1821	-	-	-	-	-
6.6275	676	1.3282	-	-	-	-	-
6.6373	677	0.4241	-	-	-	-	-
6.6471	678	0.6067	-	-	-	-	-
6.6569	679	0.6277	-	-	-	-	-
6.6667	680	0.6314	-	-	-	-	-
6.6765	681	0.1953	-	-	-	-	-
6.6863	682	0.8784	-	-	-	-	-
6.6961	683	1.0776	-	-	-	-	-
6.7059	684	0.3236	-	-	-	-	-
6.7157	685	0.2603	-	-	-	-	-
6.7255	686	0.2072	-	-	-	-	-
6.7353	687	0.2286	-	-	-	-	-
6.7451	688	0.2785	-	-	-	-	-
6.7549	689	1.0233	-	-	-	-	-
6.7647	690	1.1958	-	-	-	-	-
6.7745	691	0.4351	-	-	-	-	-
6.7843	692	0.7968	-	-	-	-	-
6.7941	693	1.4431	-	-	-	-	-
6.8039	694	2.7822	-	-	-	-	-
6.8137	695	0.1462	-	-	-	-	-
6.8235	696	0.7891	-	-	-	-	-
6.8333	697	2.3079	-	-	-	-	-
6.8431	698	0.8345	-	-	-	-	-
6.8529	699	0.9415	-	-	-	-	-
6.8627	700	1.5442	-	-	-	-	-
6.8725	701	0.5693	-	-	-	-	-
6.8824	702	0.6353	-	-	-	-	-
6.8922	703	0.5235	-	-	-	-	-
6.9020	704	1.9037	-	-	-	-	-
6.9118	705	0.2953	-	-	-	-	-
6.9216	706	0.3391	-	-	-	-	-
6.9314	707	0.0905	-	-	-	-	-
6.9412	708	2.1041	-	-	-	-	-
6.9510	709	1.8204	-	-	-	-	-
6.9608	710	1.7676	-	-	-	-	-
6.9706	711	1.4628	-	-	-	-	-
6.9804	712	0.6696	-	-	-	-	-
6.9902	713	1.4171	-	-	-	-	-
7.0	714	0.0389	0.4817	0.4788	0.4802	0.4604	0.4326
7.0098	715	0.1509	-	-	-	-	-
7.0196	716	0.1037	-	-	-	-	-
7.0294	717	0.4192	-	-	-	-	-
7.0392	718	0.1334	-	-	-	-	-
7.0490	719	0.4343	-	-	-	-	-
7.0588	720	3.0699	-	-	-	-	-
7.0686	721	0.9501	-	-	-	-	-
7.0784	722	0.1778	-	-	-	-	-
7.0882	723	0.4164	-	-	-	-	-
7.0980	724	0.2963	-	-	-	-	-
7.1078	725	1.3947	-	-	-	-	-
7.1176	726	0.505	-	-	-	-	-
7.1275	727	0.5072	-	-	-	-	-
7.1373	728	0.1854	-	-	-	-	-
7.1471	729	0.1622	-	-	-	-	-
7.1569	730	0.6847	-	-	-	-	-
7.1667	731	1.3038	-	-	-	-	-
7.1765	732	0.9911	-	-	-	-	-
7.1863	733	2.1003	-	-	-	-	-
7.1961	734	0.3802	-	-	-	-	-
7.2059	735	1.134	-	-	-	-	-
7.2157	736	0.3427	-	-	-	-	-
7.2255	737	0.8341	-	-	-	-	-
7.2353	738	1.1072	-	-	-	-	-
7.2451	739	0.3203	-	-	-	-	-
7.2549	740	0.3252	-	-	-	-	-
7.2647	741	1.0124	-	-	-	-	-
7.2745	742	0.1467	-	-	-	-	-
7.2843	743	0.1729	-	-	-	-	-
7.2941	744	0.167	-	-	-	-	-
7.3039	745	0.1038	-	-	-	-	-
7.3137	746	2.6918	-	-	-	-	-
7.3235	747	0.3386	-	-	-	-	-
7.3333	748	0.1204	-	-	-	-	-
7.3431	749	1.5078	-	-	-	-	-
7.3529	750	0.0813	-	-	-	-	-
7.3627	751	1.3693	-	-	-	-	-
7.3725	752	0.7416	-	-	-	-	-
7.3824	753	0.5061	-	-	-	-	-
7.3922	754	1.0782	-	-	-	-	-
7.4020	755	0.1061	-	-	-	-	-
7.4118	756	0.4726	-	-	-	-	-
7.4216	757	0.3794	-	-	-	-	-
7.4314	758	1.0286	-	-	-	-	-
7.4412	759	0.3516	-	-	-	-	-
7.4510	760	0.2665	-	-	-	-	-
7.4608	761	0.4261	-	-	-	-	-
7.4706	762	0.7815	-	-	-	-	-
7.4804	763	0.0516	-	-	-	-	-
7.4902	764	0.4681	-	-	-	-	-
7.5	765	0.1083	-	-	-	-	-
7.5098	766	0.1157	-	-	-	-	-
7.5196	767	0.0383	-	-	-	-	-
7.5294	768	0.1496	-	-	-	-	-
7.5392	769	1.5602	-	-	-	-	-
7.5490	770	1.2548	-	-	-	-	-
7.5588	771	0.5062	-	-	-	-	-
7.5686	772	1.3416	-	-	-	-	-
7.5784	773	0.0605	-	-	-	-	-
7.5882	774	0.4715	-	-	-	-	-
7.5980	775	0.0467	-	-	-	-	-
7.6078	776	0.9769	-	-	-	-	-
7.6176	777	0.0936	-	-	-	-	-
7.6275	778	0.3977	-	-	-	-	-
7.6373	779	0.0266	-	-	-	-	-
7.6471	780	0.2169	-	-	-	-	-
7.6569	781	0.4521	-	-	-	-	-
7.6667	782	0.5292	-	-	-	-	-
7.6765	783	0.9527	-	-	-	-	-
7.6863	784	0.2699	-	-	-	-	-
7.6961	785	0.0181	-	-	-	-	-
7.7059	786	1.7187	-	-	-	-	-
7.7157	787	1.8481	-	-	-	-	-
7.7255	788	0.1036	-	-	-	-	-
7.7353	789	0.2311	-	-	-	-	-
7.7451	790	0.221	-	-	-	-	-
7.7549	791	0.1773	-	-	-	-	-
7.7647	792	0.2201	-	-	-	-	-
7.7745	793	0.3505	-	-	-	-	-
7.7843	794	0.6739	-	-	-	-	-
7.7941	795	0.3774	-	-	-	-	-
7.8039	796	0.609	-	-	-	-	-
7.8137	797	0.106	-	-	-	-	-
7.8235	798	0.4456	-	-	-	-	-
7.8333	799	1.3334	-	-	-	-	-
7.8431	800	0.0451	-	-	-	-	-
7.8529	801	1.024	-	-	-	-	-
7.8627	802	0.1337	-	-	-	-	-
7.8725	803	1.0237	-	-	-	-	-
7.8824	804	0.7726	-	-	-	-	-
7.8922	805	0.5858	-	-	-	-	-
7.9020	806	0.0826	-	-	-	-	-
7.9118	807	1.5779	-	-	-	-	-
7.9216	808	0.5999	-	-	-	-	-
7.9314	809	0.1622	-	-	-	-	-
7.9412	810	0.8974	-	-	-	-	-
7.9510	811	0.1727	-	-	-	-	-
7.9608	812	0.5008	-	-	-	-	-
7.9706	813	0.1356	-	-	-	-	-
7.9804	814	1.4574	-	-	-	-	-
7.9902	815	0.7763	-	-	-	-	-
8.0	816	0.0359	0.4802	0.4862	0.4679	0.4621	0.4408

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: 15.21 tokens max: 36 tokens	min: 25 tokens mean: 369.39 tokens max: 512 tokens

IoannisKat1/legal-bert-base-uncased-ft-new

legal_bert_base_uncased 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

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/
legal-bert-base-uncased-ft-new