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legal-bert-base-uncased

Name: legal-bert-base-uncased-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 = [
    'What may impede authorities in the discharge of their responsibilities under Union law?',
    'The objectives and principles of Directive 95/46/EC remain sound, but it has not prevented fragmentation in the implementation of data protection across the Union, legal uncertainty or a widespread public perception that there are significant risks to the protection of natural persons, in particular with regard to online activity. Differences in the level of protection of the rights and freedoms of natural persons, in particular the right to the protection of personal data, with regard to the processing of personal data in the Member States may prevent the free flow of personal data throughout the Union. Those differences may therefore constitute an obstacle to the pursuit of economic activities at the level of the Union, distort competition and impede authorities in the discharge of their responsibilities under Union law. Such a difference in levels of protection is due to the existence of differences in the implementation and application of Directive 95/46/EC.',
    'This Regulation is without prejudice to international agreements concluded between the Union and third countries regulating the transfer of personal data including appropriate safeguards for the data subjects. Member States may conclude international agreements which involve the transfer of personal data to third countries or international organisations, as far as such agreements do not affect this Regulation or any other provisions of Union law and include an appropriate level of protection for the fundamental rights of the data subjects.',
]
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, 0.7482, 0.4027],
#         [0.7482, 1.0000, 0.4551],
#         [0.4027, 0.4551, 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	0.2945
cosine_accuracy@3	0.299
cosine_accuracy@5	0.3291
cosine_accuracy@10	0.3611
cosine_precision@1	0.2945
cosine_precision@3	0.2941
cosine_precision@5	0.2903
cosine_precision@10	0.2743
cosine_recall@1	0.0303
cosine_recall@3	0.0883
cosine_recall@5	0.1355
cosine_recall@10	0.2141
cosine_ndcg@10	0.3187
cosine_mrr@10	0.3059
cosine_map@100	0.3756

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.2913
cosine_accuracy@3	0.2951
cosine_accuracy@5	0.3188
cosine_accuracy@10	0.3553
cosine_precision@1	0.2913
cosine_precision@3	0.2904
cosine_precision@5	0.2851
cosine_precision@10	0.2681
cosine_recall@1	0.0304
cosine_recall@3	0.0883
cosine_recall@5	0.1341
cosine_recall@10	0.2091
cosine_ndcg@10	0.3126
cosine_mrr@10	0.3018
cosine_map@100	0.3699

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.2913
cosine_accuracy@3	0.2951
cosine_accuracy@5	0.3169
cosine_accuracy@10	0.3419
cosine_precision@1	0.2913
cosine_precision@3	0.2909
cosine_precision@5	0.286
cosine_precision@10	0.2652
cosine_recall@1	0.0293
cosine_recall@3	0.0858
cosine_recall@5	0.1321
cosine_recall@10	0.2058
cosine_ndcg@10	0.3091
cosine_mrr@10	0.2998
cosine_map@100	0.3618

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.2804
cosine_accuracy@3	0.2843
cosine_accuracy@5	0.3041
cosine_accuracy@10	0.3265
cosine_precision@1	0.2804
cosine_precision@3	0.2796
cosine_precision@5	0.2743
cosine_precision@10	0.2528
cosine_recall@1	0.0289
cosine_recall@3	0.084
cosine_recall@5	0.1279
cosine_recall@10	0.1954
cosine_ndcg@10	0.2965
cosine_mrr@10	0.2883
cosine_map@100	0.3458

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.2625
cosine_accuracy@3	0.265
cosine_accuracy@5	0.2862
cosine_accuracy@10	0.3067
cosine_precision@1	0.2625
cosine_precision@3	0.261
cosine_precision@5	0.2563
cosine_precision@10	0.2385
cosine_recall@1	0.0264
cosine_recall@3	0.0763
cosine_recall@5	0.1163
cosine_recall@10	0.1798
cosine_ndcg@10	0.2782
cosine_mrr@10	0.27
cosine_map@100	0.3246

Training Details

Training Dataset

Unnamed Dataset

Size: 391 training samples
Columns: anchor and positive
Approximate statistics based on the first 391 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 15.08 tokens
max: 29 tokens
min: 25 tokens
mean: 358.31 tokens
max: 512 tokens

Samples:

anchor	positive
`On what date did the act occur?`	Court (Civil/Criminal): Civil [object Object]Provisions: Directive 2015/366, Law 4537/2018 [object Object]Time of the act: 31.08.2022 [object Object]Outcome (not guilty, guilty): Partially accepts the claim. [object Object]Reasoning: The Athens Peace Court ordered the bank to return the amount that was withdrawn from the plaintiffs' account and to pay additional compensation for the moral damage they suffered. [object Object]Facts: The case concerns plaintiffs who fell victim to electronic fraud via phishing, resulting in the withdrawal of money from their bank account. The plaintiffs claimed that the bank did not take the necessary security measures to protect their accounts and sought compensation for the financial loss and moral damage they suffered. The court determined that the bank is responsible for the loss of the money, as it did not prove that the transactions were authorized by the plaintiffs. Furthermore, the court recognized that the bank's refusal to return the funds constitutes an infringement of the plaintiffs' personal rights, as it...
`For what purposes can more specific rules be provided regarding the employment context?`	1.Member States may, by law or by collective agreements, provide for more specific rules to ensure the protection of the rights and freedoms in respect of the processing of employees' personal data in the employment context, in particular for the purposes of the recruitment, the performance of the contract of employment, including discharge of obligations laid down by law or by collective agreements, management, planning and organisation of work, equality and diversity in the workplace, health and safety at work, protection of employer's or customer's property and for the purposes of the exercise and enjoyment, on an individual or collective basis, of rights and benefits related to employment, and for the purpose of the termination of the employment relationship.[object Object]2.Those rules shall include suitable and specific measures to safeguard the data subject's human dignity, legitimate interests and fundamental rights, with particular regard to the transparency of processing, the transfer of p...
`On which date were transactions detailed in the provided text conducted?`	[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]The plaintiff holds credit card number ............ with the defendant banking corporation. Based on the application for alternative networks dated 19/7/2015 with number ......... submitted at a branch of the defendant, he was granted access to the electronic banking service (e-banking) to conduct banking transactions (debit, credit, updates, payments) remotely. On 30/11/2020, the plaintiff fell victim to electronic fraud through the "phishing" method, whereby an unknown perpetrator managed to withdraw a total amount of €3,121.75 from the aforementioned credit card. Specifically, the plaintiff received an email at 1:35 PM on 29/11/2020 from sender ...... with address ........, informing him that due to an impending system change, he needed to verify the mobile phone number linked to the credit card, urging him to complete the verification...

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
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
gradient_accumulation_steps: 2
learning_rate: 2e-05
num_train_epochs: 20
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: 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: 2
per_device_eval_batch_size: 2
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: 20
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: None
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
0.0102	1	7.1696	-	-	-	-	-
0.0204	2	5.5986	-	-	-	-	-
0.0306	3	7.0556	-	-	-	-	-
0.0408	4	3.6816	-	-	-	-	-
0.0510	5	2.7039	-	-	-	-	-
0.0612	6	4.2351	-	-	-	-	-
0.0714	7	4.2931	-	-	-	-	-
0.0816	8	7.8564	-	-	-	-	-
0.0918	9	5.2709	-	-	-	-	-
0.1020	10	2.8151	-	-	-	-	-
0.1122	11	4.8541	-	-	-	-	-
0.1224	12	4.5369	-	-	-	-	-
0.1327	13	3.4808	-	-	-	-	-
0.1429	14	2.8361	-	-	-	-	-
0.1531	15	3.2782	-	-	-	-	-
0.1633	16	3.1139	-	-	-	-	-
0.1735	17	8.5683	-	-	-	-	-
0.1837	18	2.9852	-	-	-	-	-
0.1939	19	7.1109	-	-	-	-	-
0.2041	20	4.5516	-	-	-	-	-
0.2143	21	5.421	-	-	-	-	-
0.2245	22	5.0981	-	-	-	-	-
0.2347	23	5.6382	-	-	-	-	-
0.2449	24	6.2661	-	-	-	-	-
0.2551	25	2.7698	-	-	-	-	-
0.2653	26	4.0075	-	-	-	-	-
0.2755	27	7.6512	-	-	-	-	-
0.2857	28	4.7715	-	-	-	-	-
0.2959	29	4.6595	-	-	-	-	-
0.3061	30	4.795	-	-	-	-	-
0.3163	31	4.8058	-	-	-	-	-
0.3265	32	4.8049	-	-	-	-	-
0.3367	33	6.2773	-	-	-	-	-
0.3469	34	3.3515	-	-	-	-	-
0.3571	35	3.2643	-	-	-	-	-
0.3673	36	3.5992	-	-	-	-	-
0.3776	37	3.8876	-	-	-	-	-
0.3878	38	11.1147	-	-	-	-	-
0.3980	39	5.3685	-	-	-	-	-
0.4082	40	4.4782	-	-	-	-	-
0.4184	41	2.3301	-	-	-	-	-
0.4286	42	5.3515	-	-	-	-	-
0.4388	43	4.2881	-	-	-	-	-
0.4490	44	5.8402	-	-	-	-	-
0.4592	45	4.4051	-	-	-	-	-
0.4694	46	3.7015	-	-	-	-	-
0.4796	47	3.8899	-	-	-	-	-
0.4898	48	6.1056	-	-	-	-	-
0.5	49	5.0372	-	-	-	-	-
0.5102	50	3.5458	-	-	-	-	-
0.5204	51	5.2707	-	-	-	-	-
0.5306	52	5.3742	-	-	-	-	-
0.5408	53	4.952	-	-	-	-	-
0.5510	54	1.8328	-	-	-	-	-
0.5612	55	3.1727	-	-	-	-	-
0.5714	56	3.0359	-	-	-	-	-
0.5816	57	2.7896	-	-	-	-	-
0.5918	58	2.6978	-	-	-	-	-
0.6020	59	2.5506	-	-	-	-	-
0.6122	60	3.8039	-	-	-	-	-
0.6224	61	2.893	-	-	-	-	-
0.6327	62	3.5782	-	-	-	-	-
0.6429	63	4.1546	-	-	-	-	-
0.6531	64	7.4876	-	-	-	-	-
0.6633	65	2.2801	-	-	-	-	-
0.6735	66	5.4241	-	-	-	-	-
0.6837	67	5.5202	-	-	-	-	-
0.6939	68	3.6768	-	-	-	-	-
0.7041	69	3.0628	-	-	-	-	-
0.7143	70	5.0465	-	-	-	-	-
0.7245	71	3.7249	-	-	-	-	-
0.7347	72	3.3501	-	-	-	-	-
0.7449	73	3.2268	-	-	-	-	-
0.7551	74	3.1353	-	-	-	-	-
0.7653	75	4.0545	-	-	-	-	-
0.7755	76	1.4042	-	-	-	-	-
0.7857	77	0.929	-	-	-	-	-
0.7959	78	2.7907	-	-	-	-	-
0.8061	79	4.691	-	-	-	-	-
0.8163	80	1.4842	-	-	-	-	-
0.8265	81	2.9783	-	-	-	-	-
0.8367	82	3.0866	-	-	-	-	-
0.8469	83	1.1731	-	-	-	-	-
0.8571	84	0.5525	-	-	-	-	-
0.8673	85	2.5626	-	-	-	-	-
0.8776	86	1.0867	-	-	-	-	-
0.8878	87	1.3064	-	-	-	-	-
0.8980	88	3.0336	-	-	-	-	-
0.9082	89	8.6704	-	-	-	-	-
0.9184	90	2.0829	-	-	-	-	-
0.9286	91	0.9734	-	-	-	-	-
0.9388	92	4.8751	-	-	-	-	-
0.9490	93	1.7869	-	-	-	-	-
0.9592	94	2.261	-	-	-	-	-
0.9694	95	0.8735	-	-	-	-	-
0.9796	96	2.0015	-	-	-	-	-
0.9898	97	8.3582	-	-	-	-	-
1.0	98	2.7564	0.2305	0.2320	0.2428	0.2297	0.1815
1.0102	99	4.9172	-	-	-	-	-
1.0204	100	2.3303	-	-	-	-	-
1.0306	101	2.7368	-	-	-	-	-
1.0408	102	2.0585	-	-	-	-	-
1.0510	103	2.8806	-	-	-	-	-
1.0612	104	1.3272	-	-	-	-	-
1.0714	105	0.9525	-	-	-	-	-
1.0816	106	0.3944	-	-	-	-	-
1.0918	107	0.5496	-	-	-	-	-
1.1020	108	4.0204	-	-	-	-	-
1.1122	109	0.7699	-	-	-	-	-
1.1224	110	1.573	-	-	-	-	-
1.1327	111	2.2908	-	-	-	-	-
1.1429	112	2.2194	-	-	-	-	-
1.1531	113	5.5098	-	-	-	-	-
1.1633	114	1.4926	-	-	-	-	-
1.1735	115	0.5118	-	-	-	-	-
1.1837	116	0.352	-	-	-	-	-
1.1939	117	1.8558	-	-	-	-	-
1.2041	118	3.5846	-	-	-	-	-
1.2143	119	3.2194	-	-	-	-	-
1.2245	120	0.463	-	-	-	-	-
1.2347	121	0.0158	-	-	-	-	-
1.2449	122	0.373	-	-	-	-	-
1.2551	123	8.6146	-	-	-	-	-
1.2653	124	3.335	-	-	-	-	-
1.2755	125	0.3582	-	-	-	-	-
1.2857	126	0.9795	-	-	-	-	-
1.2959	127	0.1047	-	-	-	-	-
1.3061	128	0.0824	-	-	-	-	-
1.3163	129	9.3996	-	-	-	-	-
1.3265	130	0.3556	-	-	-	-	-
1.3367	131	4.8549	-	-	-	-	-
1.3469	132	2.2411	-	-	-	-	-
1.3571	133	8.3107	-	-	-	-	-
1.3673	134	0.7372	-	-	-	-	-
1.3776	135	0.5628	-	-	-	-	-
1.3878	136	1.1153	-	-	-	-	-
1.3980	137	1.3439	-	-	-	-	-
1.4082	138	1.8474	-	-	-	-	-
1.4184	139	2.622	-	-	-	-	-
1.4286	140	0.609	-	-	-	-	-
1.4388	141	1.6592	-	-	-	-	-
1.4490	142	2.3689	-	-	-	-	-
1.4592	143	0.9918	-	-	-	-	-
1.4694	144	3.2973	-	-	-	-	-
1.4796	145	5.0454	-	-	-	-	-
1.4898	146	3.5016	-	-	-	-	-
1.5	147	0.0423	-	-	-	-	-
1.5102	148	0.3454	-	-	-	-	-
1.5204	149	5.5514	-	-	-	-	-
1.5306	150	9.9022	-	-	-	-	-
1.5408	151	0.2767	-	-	-	-	-
1.5510	152	0.5092	-	-	-	-	-
1.5612	153	0.2002	-	-	-	-	-
1.5714	154	0.4579	-	-	-	-	-
1.5816	155	0.0617	-	-	-	-	-
1.5918	156	0.7426	-	-	-	-	-
1.6020	157	2.8018	-	-	-	-	-
1.6122	158	0.5183	-	-	-	-	-
1.6224	159	4.9833	-	-	-	-	-
1.6327	160	0.6326	-	-	-	-	-
1.6429	161	1.5892	-	-	-	-	-
1.6531	162	6.4426	-	-	-	-	-
1.6633	163	4.3646	-	-	-	-	-
1.6735	164	7.2462	-	-	-	-	-
1.6837	165	1.6232	-	-	-	-	-
1.6939	166	0.0539	-	-	-	-	-
1.7041	167	5.1647	-	-	-	-	-
1.7143	168	0.239	-	-	-	-	-
1.7245	169	6.1138	-	-	-	-	-
1.7347	170	1.6571	-	-	-	-	-
1.7449	171	0.2895	-	-	-	-	-
1.7551	172	0.2621	-	-	-	-	-
1.7653	173	0.0144	-	-	-	-	-
1.7755	174	0.0988	-	-	-	-	-
1.7857	175	0.025	-	-	-	-	-
1.7959	176	2.7099	-	-	-	-	-
1.8061	177	3.878	-	-	-	-	-
1.8163	178	2.0187	-	-	-	-	-
1.8265	179	26.4641	-	-	-	-	-
1.8367	180	3.9726	-	-	-	-	-
1.8469	181	1.9337	-	-	-	-	-
1.8571	182	1.6689	-	-	-	-	-
1.8673	183	2.8942	-	-	-	-	-
1.8776	184	0.4883	-	-	-	-	-
1.8878	185	0.0029	-	-	-	-	-
1.8980	186	0.2828	-	-	-	-	-
1.9082	187	1.4594	-	-	-	-	-
1.9184	188	0.0992	-	-	-	-	-
1.9286	189	0.9195	-	-	-	-	-
1.9388	190	4.6248	-	-	-	-	-
1.9490	191	0.0364	-	-	-	-	-
1.9592	192	0.8291	-	-	-	-	-
1.9694	193	5.1303	-	-	-	-	-
1.9796	194	0.3142	-	-	-	-	-
1.9898	195	0.182	-	-	-	-	-
2.0	196	0.0019	0.2938	0.2853	0.2893	0.2719	0.2200
2.0102	197	3.262	-	-	-	-	-
2.0204	198	0.0092	-	-	-	-	-
2.0306	199	0.3517	-	-	-	-	-
2.0408	200	0.0116	-	-	-	-	-
2.0510	201	0.0846	-	-	-	-	-
2.0612	202	0.0027	-	-	-	-	-
2.0714	203	2.1304	-	-	-	-	-
2.0816	204	1.1392	-	-	-	-	-
2.0918	205	0.2868	-	-	-	-	-
2.1020	206	5.8102	-	-	-	-	-
2.1122	207	0.0089	-	-	-	-	-
2.1224	208	0.191	-	-	-	-	-
2.1327	209	0.0439	-	-	-	-	-
2.1429	210	11.698	-	-	-	-	-
2.1531	211	0.2859	-	-	-	-	-
2.1633	212	0.0321	-	-	-	-	-
2.1735	213	0.0025	-	-	-	-	-
2.1837	214	0.5854	-	-	-	-	-
2.1939	215	5.8049	-	-	-	-	-
2.2041	216	2.782	-	-	-	-	-
2.2143	217	0.3969	-	-	-	-	-
2.2245	218	0.8192	-	-	-	-	-
2.2347	219	0.0015	-	-	-	-	-
2.2449	220	5.6306	-	-	-	-	-
2.2551	221	12.1614	-	-	-	-	-
2.2653	222	2.142	-	-	-	-	-
2.2755	223	0.3337	-	-	-	-	-
2.2857	224	1.502	-	-	-	-	-
2.2959	225	0.0516	-	-	-	-	-
2.3061	226	0.0015	-	-	-	-	-
2.3163	227	0.005	-	-	-	-	-
2.3265	228	2.7072	-	-	-	-	-
2.3367	229	0.0176	-	-	-	-	-
2.3469	230	0.2738	-	-	-	-	-
2.3571	231	2.1149	-	-	-	-	-
2.3673	232	5.956	-	-	-	-	-
2.3776	233	0.6448	-	-	-	-	-
2.3878	234	0.1135	-	-	-	-	-
2.3980	235	0.0345	-	-	-	-	-
2.4082	236	2.4979	-	-	-	-	-
2.4184	237	0.6361	-	-	-	-	-
2.4286	238	0.3688	-	-	-	-	-
2.4388	239	7.7828	-	-	-	-	-
2.4490	240	4.2094	-	-	-	-	-
2.4592	241	0.1711	-	-	-	-	-
2.4694	242	0.0468	-	-	-	-	-
2.4796	243	0.0016	-	-	-	-	-
2.4898	244	0.5277	-	-	-	-	-
2.5	245	0.0386	-	-	-	-	-
2.5102	246	10.168	-	-	-	-	-
2.5204	247	6.9855	-	-	-	-	-
2.5306	248	7.1669	-	-	-	-	-
2.5408	249	0.8908	-	-	-	-	-
2.5510	250	1.839	-	-	-	-	-
2.5612	251	0.0424	-	-	-	-	-
2.5714	252	2.5308	-	-	-	-	-
2.5816	253	0.6599	-	-	-	-	-
2.5918	254	0.0395	-	-	-	-	-
2.6020	255	0.1428	-	-	-	-	-
2.6122	256	3.4492	-	-	-	-	-
2.6224	257	4.8398	-	-	-	-	-
2.6327	258	0.0124	-	-	-	-	-
2.6429	259	0.0069	-	-	-	-	-
2.6531	260	0.2163	-	-	-	-	-
2.6633	261	4.8929	-	-	-	-	-
2.6735	262	0.0561	-	-	-	-	-
2.6837	263	0.1611	-	-	-	-	-
2.6939	264	1.3758	-	-	-	-	-
2.7041	265	3.2582	-	-	-	-	-
2.7143	266	18.0246	-	-	-	-	-
2.7245	267	0.0016	-	-	-	-	-
2.7347	268	2.5819	-	-	-	-	-
2.7449	269	0.4953	-	-	-	-	-
2.7551	270	0.1712	-	-	-	-	-
2.7653	271	0.0173	-	-	-	-	-
2.7755	272	9.0557	-	-	-	-	-
2.7857	273	0.0104	-	-	-	-	-
2.7959	274	1.2539	-	-	-	-	-
2.8061	275	0.0	-	-	-	-	-
2.8163	276	0.0692	-	-	-	-	-
2.8265	277	0.0416	-	-	-	-	-
2.8367	278	1.4689	-	-	-	-	-
2.8469	279	7.7806	-	-	-	-	-
2.8571	280	0.0189	-	-	-	-	-
2.8673	281	1.6739	-	-	-	-	-
2.8776	282	0.0527	-	-	-	-	-
2.8878	283	3.8894	-	-	-	-	-
2.8980	284	4.7123	-	-	-	-	-
2.9082	285	0.6912	-	-	-	-	-
2.9184	286	0.2394	-	-	-	-	-
2.9286	287	1.1657	-	-	-	-	-
2.9388	288	0.0046	-	-	-	-	-
2.9490	289	0.0011	-	-	-	-	-
2.9592	290	0.0098	-	-	-	-	-
2.9694	291	0.4745	-	-	-	-	-
2.9796	292	0.964	-	-	-	-	-
2.9898	293	0.0369	-	-	-	-	-
3.0	294	0.0179	0.2878	0.2820	0.2770	0.2625	0.2361
3.0102	295	3.7066	-	-	-	-	-
3.0204	296	0.0002	-	-	-	-	-
3.0306	297	0.042	-	-	-	-	-
3.0408	298	9.0249	-	-	-	-	-
3.0510	299	1.1905	-	-	-	-	-
3.0612	300	0.1012	-	-	-	-	-
3.0714	301	0.0468	-	-	-	-	-
3.0816	302	0.002	-	-	-	-	-
3.0918	303	0.0003	-	-	-	-	-
3.1020	304	0.093	-	-	-	-	-
3.1122	305	2.4288	-	-	-	-	-
3.1224	306	2.7864	-	-	-	-	-
3.1327	307	0.1523	-	-	-	-	-
3.1429	308	0.004	-	-	-	-	-
3.1531	309	12.1307	-	-	-	-	-
3.1633	310	0.0162	-	-	-	-	-
3.1735	311	0.0012	-	-	-	-	-
3.1837	312	1.4673	-	-	-	-	-
3.1939	313	0.0212	-	-	-	-	-
3.2041	314	0.0026	-	-	-	-	-
3.2143	315	4.5828	-	-	-	-	-
3.2245	316	0.0001	-	-	-	-	-
3.2347	317	0.0708	-	-	-	-	-
3.2449	318	0.3905	-	-	-	-	-
3.2551	319	0.0472	-	-	-	-	-
3.2653	320	0.6012	-	-	-	-	-
3.2755	321	0.0233	-	-	-	-	-
3.2857	322	2.4017	-	-	-	-	-
3.2959	323	0.0008	-	-	-	-	-
3.3061	324	0.09	-	-	-	-	-
3.3163	325	0.6235	-	-	-	-	-
3.3265	326	0.0004	-	-	-	-	-
3.3367	327	0.0036	-	-	-	-	-
3.3469	328	0.0573	-	-	-	-	-
3.3571	329	1.7098	-	-	-	-	-
3.3673	330	0.0395	-	-	-	-	-
3.3776	331	0.0052	-	-	-	-	-
3.3878	332	0.0095	-	-	-	-	-
3.3980	333	7.6863	-	-	-	-	-
3.4082	334	0.1564	-	-	-	-	-
3.4184	335	0.0134	-	-	-	-	-
3.4286	336	0.0212	-	-	-	-	-
3.4388	337	0.0004	-	-	-	-	-
3.4490	338	0.0001	-	-	-	-	-
3.4592	339	0.0808	-	-	-	-	-
3.4694	340	0.0006	-	-	-	-	-
3.4796	341	0.4523	-	-	-	-	-
3.4898	342	0.3906	-	-	-	-	-
3.5	343	0.0364	-	-	-	-	-
3.5102	344	0.055	-	-	-	-	-
3.5204	345	0.1381	-	-	-	-	-
3.5306	346	2.0386	-	-	-	-	-
3.5408	347	0.0003	-	-	-	-	-
3.5510	348	0.119	-	-	-	-	-
3.5612	349	0.0003	-	-	-	-	-
3.5714	350	0.0165	-	-	-	-	-
3.5816	351	6.8156	-	-	-	-	-
3.5918	352	1.5111	-	-	-	-	-
3.6020	353	0.0001	-	-	-	-	-
3.6122	354	1.5603	-	-	-	-	-
3.6224	355	0.5631	-	-	-	-	-
3.6327	356	0.238	-	-	-	-	-
3.6429	357	0.1564	-	-	-	-	-
3.6531	358	0.0211	-	-	-	-	-
3.6633	359	0.0516	-	-	-	-	-
3.6735	360	0.0184	-	-	-	-	-
3.6837	361	0.0944	-	-	-	-	-
3.6939	362	0.0242	-	-	-	-	-
3.7041	363	8.1297	-	-	-	-	-
3.7143	364	0.025	-	-	-	-	-
3.7245	365	0.0041	-	-	-	-	-
3.7347	366	0.0012	-	-	-	-	-
3.7449	367	3.6937	-	-	-	-	-
3.7551	368	0.1472	-	-	-	-	-
3.7653	369	1.8883	-	-	-	-	-
3.7755	370	0.0229	-	-	-	-	-
3.7857	371	1.1389	-	-	-	-	-
3.7959	372	0.276	-	-	-	-	-
3.8061	373	0.2737	-	-	-	-	-
3.8163	374	0.0002	-	-	-	-	-
3.8265	375	0.0959	-	-	-	-	-
3.8367	376	0.1988	-	-	-	-	-
3.8469	377	0.0002	-	-	-	-	-
3.8571	378	0.4024	-	-	-	-	-
3.8673	379	0.0025	-	-	-	-	-
3.8776	380	0.0077	-	-	-	-	-
3.8878	381	2.5045	-	-	-	-	-
3.8980	382	0.0271	-	-	-	-	-
3.9082	383	1.1894	-	-	-	-	-
3.9184	384	1.2235	-	-	-	-	-
3.9286	385	2.7343	-	-	-	-	-
3.9388	386	0.503	-	-	-	-	-
3.9490	387	0.0885	-	-	-	-	-
3.9592	388	0.0001	-	-	-	-	-
3.9694	389	0.6582	-	-	-	-	-
3.9796	390	0.0002	-	-	-	-	-
3.9898	391	0.0041	-	-	-	-	-
4.0	392	0.0001	0.3158	0.3166	0.3072	0.2876	0.2621
4.0102	393	6.4082	-	-	-	-	-
4.0204	394	0.0927	-	-	-	-	-
4.0306	395	4.3878	-	-	-	-	-
4.0408	396	0.0233	-	-	-	-	-
4.0510	397	0.0001	-	-	-	-	-
4.0612	398	0.0006	-	-	-	-	-
4.0714	399	0.0001	-	-	-	-	-
4.0816	400	0.0006	-	-	-	-	-
4.0918	401	0.0132	-	-	-	-	-
4.1020	402	0.0265	-	-	-	-	-
4.1122	403	3.4777	-	-	-	-	-
4.1224	404	0.0022	-	-	-	-	-
4.1327	405	0.0001	-	-	-	-	-
4.1429	406	0.0007	-	-	-	-	-
4.1531	407	0.0055	-	-	-	-	-
4.1633	408	0.0002	-	-	-	-	-
4.1735	409	0.0316	-	-	-	-	-
4.1837	410	0.0479	-	-	-	-	-
4.1939	411	0.0004	-	-	-	-	-
4.2041	412	0.0019	-	-	-	-	-
4.2143	413	0.1181	-	-	-	-	-
4.2245	414	0.1845	-	-	-	-	-
4.2347	415	0.0001	-	-	-	-	-
4.2449	416	5.1701	-	-	-	-	-
4.2551	417	0.049	-	-	-	-	-
4.2653	418	0.077	-	-	-	-	-
4.2755	419	4.0434	-	-	-	-	-
4.2857	420	4.7865	-	-	-	-	-
4.2959	421	0.8345	-	-	-	-	-
4.3061	422	6.5911	-	-	-	-	-
4.3163	423	0.0784	-	-	-	-	-
4.3265	424	0.005	-	-	-	-	-
4.3367	425	0.0003	-	-	-	-	-
4.3469	426	1.6826	-	-	-	-	-
4.3571	427	0.1201	-	-	-	-	-
4.3673	428	0.0016	-	-	-	-	-
4.3776	429	0.011	-	-	-	-	-
4.3878	430	0.001	-	-	-	-	-
4.3980	431	0.0008	-	-	-	-	-
4.4082	432	0.0127	-	-	-	-	-
4.4184	433	0.4294	-	-	-	-	-
4.4286	434	0.0054	-	-	-	-	-
4.4388	435	0.0	-	-	-	-	-
4.4490	436	0.8544	-	-	-	-	-
4.4592	437	4.1478	-	-	-	-	-
4.4694	438	0.261	-	-	-	-	-
4.4796	439	0.0	-	-	-	-	-
4.4898	440	0.4865	-	-	-	-	-
4.5	441	0.0084	-	-	-	-	-
4.5102	442	0.2217	-	-	-	-	-
4.5204	443	0.7317	-	-	-	-	-
4.5306	444	0.0415	-	-	-	-	-
4.5408	445	0.0008	-	-	-	-	-
4.5510	446	0.0004	-	-	-	-	-
4.5612	447	0.4987	-	-	-	-	-
4.5714	448	0.0141	-	-	-	-	-
4.5816	449	0.2476	-	-	-	-	-
4.5918	450	0.0463	-	-	-	-	-
4.6020	451	0.0011	-	-	-	-	-
4.6122	452	0.0155	-	-	-	-	-
4.6224	453	0.0068	-	-	-	-	-
4.6327	454	0.0009	-	-	-	-	-
4.6429	455	0.3858	-	-	-	-	-
4.6531	456	0.6687	-	-	-	-	-
4.6633	457	6.9477	-	-	-	-	-
4.6735	458	0.9326	-	-	-	-	-
4.6837	459	0.0208	-	-	-	-	-
4.6939	460	0.058	-	-	-	-	-
4.7041	461	0.0307	-	-	-	-	-
4.7143	462	0.0	-	-	-	-	-
4.7245	463	23.8009	-	-	-	-	-
4.7347	464	0.0001	-	-	-	-	-
4.7449	465	3.4537	-	-	-	-	-
4.7551	466	4.185	-	-	-	-	-
4.7653	467	1.3744	-	-	-	-	-
4.7755	468	0.4893	-	-	-	-	-
4.7857	469	0.0023	-	-	-	-	-
4.7959	470	0.0163	-	-	-	-	-
4.8061	471	0.001	-	-	-	-	-
4.8163	472	0.0	-	-	-	-	-
4.8265	473	0.0074	-	-	-	-	-
4.8367	474	0.0006	-	-	-	-	-
4.8469	475	0.0011	-	-	-	-	-
4.8571	476	1.6108	-	-	-	-	-
4.8673	477	0.1876	-	-	-	-	-
4.8776	478	0.0262	-	-	-	-	-
4.8878	479	0.1159	-	-	-	-	-
4.8980	480	0.5904	-	-	-	-	-
4.9082	481	0.0002	-	-	-	-	-
4.9184	482	2.7912	-	-	-	-	-
4.9286	483	2.9303	-	-	-	-	-
4.9388	484	0.0127	-	-	-	-	-
4.9490	485	2.9811	-	-	-	-	-
4.9592	486	0.0252	-	-	-	-	-
4.9694	487	0.0522	-	-	-	-	-
4.9796	488	0.2255	-	-	-	-	-
4.9898	489	0.1411	-	-	-	-	-
5.0	490	0.0711	0.3197	0.3140	0.3032	0.2937	0.2607
5.0102	491	0.012	-	-	-	-	-
5.0204	492	0.0008	-	-	-	-	-
5.0306	493	0.0028	-	-	-	-	-
5.0408	494	1.8711	-	-	-	-	-
5.0510	495	0.0761	-	-	-	-	-
5.0612	496	0.1384	-	-	-	-	-
5.0714	497	0.11	-	-	-	-	-
5.0816	498	0.001	-	-	-	-	-
5.0918	499	0.0005	-	-	-	-	-
5.1020	500	0.8153	-	-	-	-	-
5.1122	501	0.0	-	-	-	-	-
5.1224	502	0.0057	-	-	-	-	-
5.1327	503	0.0104	-	-	-	-	-
5.1429	504	0.0001	-	-	-	-	-
5.1531	505	0.0026	-	-	-	-	-
5.1633	506	0.0015	-	-	-	-	-
5.1735	507	0.0022	-	-	-	-	-
5.1837	508	0.0052	-	-	-	-	-
5.1939	509	0.0362	-	-	-	-	-
5.2041	510	6.6671	-	-	-	-	-
5.2143	511	0.8716	-	-	-	-	-
5.2245	512	0.0141	-	-	-	-	-
5.2347	513	0.0	-	-	-	-	-
5.2449	514	0.0217	-	-	-	-	-
5.2551	515	0.0001	-	-	-	-	-
5.2653	516	0.0	-	-	-	-	-
5.2755	517	0.0258	-	-	-	-	-
5.2857	518	0.0001	-	-	-	-	-
5.2959	519	0.0092	-	-	-	-	-
5.3061	520	0.001	-	-	-	-	-
5.3163	521	0.0174	-	-	-	-	-
5.3265	522	0.2128	-	-	-	-	-
5.3367	523	0.0222	-	-	-	-	-
5.3469	524	0.0084	-	-	-	-	-
5.3571	525	0.0005	-	-	-	-	-
5.3673	526	0.0164	-	-	-	-	-
5.3776	527	0.004	-	-	-	-	-
5.3878	528	0.0154	-	-	-	-	-
5.3980	529	0.0002	-	-	-	-	-
5.4082	530	0.0178	-	-	-	-	-
5.4184	531	0.0	-	-	-	-	-
5.4286	532	2.6306	-	-	-	-	-
5.4388	533	0.0014	-	-	-	-	-
5.4490	534	0.0007	-	-	-	-	-
5.4592	535	0.0088	-	-	-	-	-
5.4694	536	0.0011	-	-	-	-	-
5.4796	537	0.0032	-	-	-	-	-
5.4898	538	0.0004	-	-	-	-	-
5.5	539	0.0005	-	-	-	-	-
5.5102	540	0.0002	-	-	-	-	-
5.5204	541	0.0046	-	-	-	-	-
5.5306	542	0.0258	-	-	-	-	-
5.5408	543	0.754	-	-	-	-	-
5.5510	544	0.7433	-	-	-	-	-
5.5612	545	0.0332	-	-	-	-	-
5.5714	546	0.0001	-	-	-	-	-
5.5816	547	0.0093	-	-	-	-	-
5.5918	548	0.0109	-	-	-	-	-
5.6020	549	0.0003	-	-	-	-	-
5.6122	550	0.0003	-	-	-	-	-
5.6224	551	0.0008	-	-	-	-	-
5.6327	552	0.0001	-	-	-	-	-
5.6429	553	0.0017	-	-	-	-	-
5.6531	554	0.0084	-	-	-	-	-
5.6633	555	0.0005	-	-	-	-	-
5.6735	556	0.023	-	-	-	-	-
5.6837	557	0.0137	-	-	-	-	-
5.6939	558	0.0102	-	-	-	-	-
5.7041	559	0.4275	-	-	-	-	-
5.7143	560	0.0001	-	-	-	-	-
5.7245	561	0.0001	-	-	-	-	-
5.7347	562	0.0009	-	-	-	-	-
5.7449	563	0.013	-	-	-	-	-
5.7551	564	0.0001	-	-	-	-	-
5.7653	565	0.0006	-	-	-	-	-
5.7755	566	0.0001	-	-	-	-	-
5.7857	567	0.0003	-	-	-	-	-
5.7959	568	0.0001	-	-	-	-	-
5.8061	569	0.8792	-	-	-	-	-
5.8163	570	0.7551	-	-	-	-	-
5.8265	571	0.0002	-	-	-	-	-
5.8367	572	0.0	-	-	-	-	-
5.8469	573	0.3999	-	-	-	-	-
5.8571	574	0.0168	-	-	-	-	-
5.8673	575	0.0014	-	-	-	-	-
5.8776	576	0.0004	-	-	-	-	-
5.8878	577	9.7985	-	-	-	-	-
5.8980	578	0.0001	-	-	-	-	-
5.9082	579	0.0078	-	-	-	-	-
5.9184	580	1.6446	-	-	-	-	-
5.9286	581	1.8624	-	-	-	-	-
5.9388	582	0.3274	-	-	-	-	-
5.9490	583	0.1845	-	-	-	-	-
5.9592	584	0.0044	-	-	-	-	-
5.9694	585	0.0016	-	-	-	-	-
5.9796	586	2.6768	-	-	-	-	-
5.9898	587	3.167	-	-	-	-	-
6.0	588	0.0013	0.3256	0.3222	0.3151	0.2987	0.273
6.0102	589	0.0262	-	-	-	-	-
6.0204	590	0.021	-	-	-	-	-
6.0306	591	0.0165	-	-	-	-	-
6.0408	592	0.5149	-	-	-	-	-
6.0510	593	1.1763	-	-	-	-	-
6.0612	594	0.0205	-	-	-	-	-
6.0714	595	0.0006	-	-	-	-	-
6.0816	596	0.0002	-	-	-	-	-
6.0918	597	0.0011	-	-	-	-	-
6.1020	598	0.0005	-	-	-	-	-
6.1122	599	0.0002	-	-	-	-	-
6.1224	600	0.0002	-	-	-	-	-
6.1327	601	0.0149	-	-	-	-	-
6.1429	602	0.0065	-	-	-	-	-
6.1531	603	0.0	-	-	-	-	-
6.1633	604	0.0018	-	-	-	-	-
6.1735	605	0.0	-	-	-	-	-
6.1837	606	0.001	-	-	-	-	-
6.1939	607	0.105	-	-	-	-	-
6.2041	608	0.002	-	-	-	-	-
6.2143	609	3.1424	-	-	-	-	-
6.2245	610	1.9828	-	-	-	-	-
6.2347	611	0.0056	-	-	-	-	-
6.2449	612	0.0001	-	-	-	-	-
6.2551	613	0.0177	-	-	-	-	-
6.2653	614	0.0358	-	-	-	-	-
6.2755	615	0.0001	-	-	-	-	-
6.2857	616	0.0	-	-	-	-	-
6.2959	617	0.0006	-	-	-	-	-
6.3061	618	0.0105	-	-	-	-	-
6.3163	619	0.0005	-	-	-	-	-
6.3265	620	0.0002	-	-	-	-	-
6.3367	621	0.0043	-	-	-	-	-
6.3469	622	0.0001	-	-	-	-	-
6.3571	623	0.0009	-	-	-	-	-
6.3673	624	0.0018	-	-	-	-	-
6.3776	625	0.0066	-	-	-	-	-
6.3878	626	0.0004	-	-	-	-	-
6.3980	627	0.0018	-	-	-	-	-
6.4082	628	0.0002	-	-	-	-	-
6.4184	629	0.0056	-	-	-	-	-
6.4286	630	0.0	-	-	-	-	-
6.4388	631	0.0001	-	-	-	-	-
6.4490	632	0.0017	-	-	-	-	-
6.4592	633	0.0177	-	-	-	-	-
6.4694	634	0.0002	-	-	-	-	-
6.4796	635	0.0004	-	-	-	-	-
6.4898	636	0.0015	-	-	-	-	-
6.5	637	0.0004	-	-	-	-	-
6.5102	638	0.0018	-	-	-	-	-
6.5204	639	0.0185	-	-	-	-	-
6.5306	640	0.0	-	-	-	-	-
6.5408	641	0.0051	-	-	-	-	-
6.5510	642	0.0018	-	-	-	-	-
6.5612	643	0.0144	-	-	-	-	-
6.5714	644	0.0114	-	-	-	-	-
6.5816	645	0.0391	-	-	-	-	-
6.5918	646	0.3066	-	-	-	-	-
6.6020	647	0.0047	-	-	-	-	-
6.6122	648	0.0	-	-	-	-	-
6.6224	649	0.7053	-	-	-	-	-
6.6327	650	0.0003	-	-	-	-	-
6.6429	651	0.0319	-	-	-	-	-
6.6531	652	1.205	-	-	-	-	-
6.6633	653	0.0098	-	-	-	-	-
6.6735	654	0.0009	-	-	-	-	-
6.6837	655	0.0	-	-	-	-	-
6.6939	656	0.0577	-	-	-	-	-
6.7041	657	0.0054	-	-	-	-	-
6.7143	658	0.0018	-	-	-	-	-
6.7245	659	4.6084	-	-	-	-	-
6.7347	660	0.1262	-	-	-	-	-
6.7449	661	0.0538	-	-	-	-	-
6.7551	662	0.0	-	-	-	-	-
6.7653	663	0.0041	-	-	-	-	-
6.7755	664	0.0046	-	-	-	-	-
6.7857	665	0.0	-	-	-	-	-
6.7959	666	0.1917	-	-	-	-	-
6.8061	667	0.1963	-	-	-	-	-
6.8163	668	0.0	-	-	-	-	-
6.8265	669	0.0002	-	-	-	-	-
6.8367	670	0.001	-	-	-	-	-
6.8469	671	0.0	-	-	-	-	-
6.8571	672	0.0089	-	-	-	-	-
6.8673	673	0.0002	-	-	-	-	-
6.8776	674	0.0	-	-	-	-	-
6.8878	675	0.0001	-	-	-	-	-
6.8980	676	0.0029	-	-	-	-	-
6.9082	677	0.0003	-	-	-	-	-
6.9184	678	0.0002	-	-	-	-	-
6.9286	679	0.0144	-	-	-	-	-
6.9388	680	0.0002	-	-	-	-	-
6.9490	681	9.5598	-	-	-	-	-
6.9592	682	7.4394	-	-	-	-	-
6.9694	683	0.0395	-	-	-	-	-
6.9796	684	0.0073	-	-	-	-	-
6.9898	685	0.0001	-	-	-	-	-
7.0	686	0.0024	0.3219	0.3160	0.3064	0.2943	0.2634
7.0102	687	0.1988	-	-	-	-	-
7.0204	688	0.0029	-	-	-	-	-
7.0306	689	0.0565	-	-	-	-	-
7.0408	690	0.0001	-	-	-	-	-
7.0510	691	0.3333	-	-	-	-	-
7.0612	692	0.0	-	-	-	-	-
7.0714	693	0.0397	-	-	-	-	-
7.0816	694	0.0002	-	-	-	-	-
7.0918	695	6.99	-	-	-	-	-
7.1020	696	0.2037	-	-	-	-	-
7.1122	697	0.0058	-	-	-	-	-
7.1224	698	0.1683	-	-	-	-	-
7.1327	699	3.2532	-	-	-	-	-
7.1429	700	0.0063	-	-	-	-	-
7.1531	701	0.0	-	-	-	-	-
7.1633	702	0.0051	-	-	-	-	-
7.1735	703	0.8695	-	-	-	-	-
7.1837	704	0.0	-	-	-	-	-
7.1939	705	0.0001	-	-	-	-	-
7.2041	706	1.9942	-	-	-	-	-
7.2143	707	0.0	-	-	-	-	-
7.2245	708	0.0007	-	-	-	-	-
7.2347	709	0.0003	-	-	-	-	-
7.2449	710	0.0	-	-	-	-	-
7.2551	711	0.0	-	-	-	-	-
7.2653	712	0.0008	-	-	-	-	-
7.2755	713	0.0021	-	-	-	-	-
7.2857	714	0.0001	-	-	-	-	-
7.2959	715	0.0014	-	-	-	-	-
7.3061	716	0.0	-	-	-	-	-
7.3163	717	0.4907	-	-	-	-	-
7.3265	718	0.0007	-	-	-	-	-
7.3367	719	0.1083	-	-	-	-	-
7.3469	720	0.0003	-	-	-	-	-
7.3571	721	0.0005	-	-	-	-	-
7.3673	722	0.0317	-	-	-	-	-
7.3776	723	0.0005	-	-	-	-	-
7.3878	724	0.0056	-	-	-	-	-
7.3980	725	0.0094	-	-	-	-	-
7.4082	726	0.0604	-	-	-	-	-
7.4184	727	4.4169	-	-	-	-	-
7.4286	728	0.012	-	-	-	-	-
7.4388	729	5.5525	-	-	-	-	-
7.4490	730	2.3835	-	-	-	-	-
7.4592	731	0.0003	-	-	-	-	-
7.4694	732	0.0016	-	-	-	-	-
7.4796	733	0.0	-	-	-	-	-
7.4898	734	0.0	-	-	-	-	-
7.5	735	0.0421	-	-	-	-	-
7.5102	736	0.0003	-	-	-	-	-
7.5204	737	0.0029	-	-	-	-	-
7.5306	738	0.0708	-	-	-	-	-
7.5408	739	0.0025	-	-	-	-	-
7.5510	740	0.0003	-	-	-	-	-
7.5612	741	0.0	-	-	-	-	-
7.5714	742	0.001	-	-	-	-	-
7.5816	743	0.9904	-	-	-	-	-
7.5918	744	8.014	-	-	-	-	-
7.6020	745	0.0015	-	-	-	-	-
7.6122	746	0.0002	-	-	-	-	-
7.6224	747	0.0034	-	-	-	-	-
7.6327	748	0.0004	-	-	-	-	-
7.6429	749	0.023	-	-	-	-	-
7.6531	750	7.3282	-	-	-	-	-
7.6633	751	0.0244	-	-	-	-	-
7.6735	752	0.1192	-	-	-	-	-
7.6837	753	0.004	-	-	-	-	-
7.6939	754	0.0007	-	-	-	-	-
7.7041	755	0.0003	-	-	-	-	-
7.7143	756	0.0024	-	-	-	-	-
7.7245	757	0.0035	-	-	-	-	-
7.7347	758	0.0	-	-	-	-	-
7.7449	759	0.0025	-	-	-	-	-
7.7551	760	0.0017	-	-	-	-	-
7.7653	761	0.0005	-	-	-	-	-
7.7755	762	2.9901	-	-	-	-	-
7.7857	763	0.0004	-	-	-	-	-
7.7959	764	0.0022	-	-	-	-	-
7.8061	765	0.0013	-	-	-	-	-
7.8163	766	0.0002	-	-	-	-	-
7.8265	767	0.0179	-	-	-	-	-
7.8367	768	0.0009	-	-	-	-	-
7.8469	769	0.0002	-	-	-	-	-
7.8571	770	0.0013	-	-	-	-	-
7.8673	771	0.0007	-	-	-	-	-
7.8776	772	0.0063	-	-	-	-	-
7.8878	773	0.0002	-	-	-	-	-
7.8980	774	0.002	-	-	-	-	-
7.9082	775	0.0005	-	-	-	-	-
7.9184	776	0.0005	-	-	-	-	-
7.9286	777	0.0002	-	-	-	-	-
7.9388	778	0.0	-	-	-	-	-
7.9490	779	0.0	-	-	-	-	-
7.9592	780	0.0017	-	-	-	-	-
7.9694	781	0.0049	-	-	-	-	-
7.9796	782	5.2118	-	-	-	-	-
7.9898	783	0.0035	-	-	-	-	-
8.0	784	0.0	0.3187	0.3126	0.3091	0.2965	0.2782

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.51.3
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
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: 15.08 tokens max: 29 tokens	min: 25 tokens mean: 358.31 tokens max: 512 tokens

IoannisKat1/legal-bert-base-uncased-new

legal-bert-base-uncased

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-new