Bert base fine-tuned with Cantonese and English mixed STS dataset

This is a sentence-transformers model finetuned from hon9kon9ize/bert-large-cantonese-sts. It maps sentences & paragraphs to a 1024-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: hon9kon9ize/bert-large-cantonese-sts
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Language: yue
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}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hon9kon9ize/yue-embed")
# Run inference
sentences = [
    'query: when did england change from fahrenheit to celsius',
    'document: Metrication in the United Kingdom Adopting the metric system was discussed in Parliament as early as 1818 and some industries and even some government agencies had metricated, or were in the process of metricating by the mid 1960s. A formal government policy to support metrication was agreed by 1965. This policy, initiated in response to requests from industry, was to support voluntary metrication, with costs picked up where they fell. In 1969 the government created the Metrication Board as a quango to promote and coordinate metrication. In 1978, after some carpet retailers reverted to pricing by the square yard rather than the square metre, government policy shifted, and they started issuing orders making metrication mandatory in certain sectors. In 1980 government policy shifted again to prefer voluntary metrication, and the Metrication Board was abolished. By the time the Metrication Board was wound up, all the economic sectors that fell within its remit except road signage and parts of the retail trade sector had metricated.',
    "document: Periodic table Importantly, the organization of the periodic table can be utilized to derive relationships between various element properties, but also predicted chemical properties and behaviours of undiscovered or newly synthesized elements. Russian chemist Dmitri Mendeleev was first to publish a recognizable periodic table in 1869, developed mainly to illustrate periodic trends of the then-known elements. He also predicted some properties of unidentified elements that were expected to fill gaps within this table. Most of his forecasts proved to be correct. Mendeleev's idea has been slowly expanded and refined with the discovery or synthesis of further new elements and by developing new theoretical models to explain chemical behaviour. The modern periodic table now provides a useful framework for analyzing chemical reactions, and continues to be widely adopted in chemistry, nuclear physics and other sciences.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.06	0.1	0.06	0.12	0.18	0.08	0.1	0.12	0.56	0.06	0.12	0.18	0.3469
cosine_accuracy@3	0.2	0.26	0.1	0.22	0.38	0.16	0.1	0.26	0.66	0.12	0.34	0.22	0.7143
cosine_accuracy@5	0.22	0.44	0.1	0.26	0.4	0.2	0.12	0.38	0.68	0.14	0.52	0.32	0.7959
cosine_accuracy@10	0.26	0.52	0.12	0.36	0.44	0.24	0.18	0.44	0.8	0.22	0.64	0.36	0.9388
cosine_precision@1	0.06	0.1	0.06	0.12	0.18	0.08	0.1	0.12	0.56	0.06	0.12	0.18	0.3469
cosine_precision@3	0.0667	0.1267	0.0333	0.08	0.1333	0.0533	0.06	0.0867	0.2533	0.0533	0.1133	0.08	0.3265
cosine_precision@5	0.052	0.152	0.02	0.064	0.084	0.04	0.056	0.08	0.16	0.036	0.104	0.068	0.3061
cosine_precision@10	0.032	0.154	0.012	0.046	0.052	0.024	0.042	0.048	0.092	0.026	0.064	0.04	0.2714
cosine_recall@1	0.035	0.0058	0.05	0.0709	0.09	0.08	0.0024	0.11	0.49	0.0157	0.12	0.165	0.0173
cosine_recall@3	0.105	0.0257	0.09	0.1362	0.2	0.16	0.0045	0.24	0.6073	0.0367	0.34	0.21	0.06
cosine_recall@5	0.1267	0.0488	0.09	0.1499	0.21	0.2	0.0053	0.37	0.634	0.0407	0.52	0.3	0.1013
cosine_recall@10	0.144	0.0818	0.11	0.2119	0.26	0.24	0.0069	0.43	0.7407	0.0567	0.64	0.345	0.1705
cosine_ndcg@10	0.1074	0.1565	0.078	0.1599	0.2152	0.155	0.0514	0.2669	0.6316	0.0544	0.3668	0.2485	0.2934
cosine_mrr@10	0.1231	0.223	0.0753	0.1879	0.2793	0.1282	0.1127	0.2195	0.6266	0.1009	0.2815	0.2242	0.5437
cosine_map@100	0.0839	0.0848	0.0766	0.1279	0.1695	0.1439	0.0112	0.2213	0.6008	0.0383	0.2897	0.2308	0.1828

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.1605
cosine_accuracy@3	0.2873
cosine_accuracy@5	0.352
cosine_accuracy@10	0.4245
cosine_precision@1	0.1605
cosine_precision@3	0.1128
cosine_precision@5	0.094
cosine_precision@10	0.0695
cosine_recall@1	0.0963
cosine_recall@3	0.1704
cosine_recall@5	0.2151
cosine_recall@10	0.2644
cosine_ndcg@10	0.2142
cosine_mrr@10	0.2405
cosine_map@100	0.1739

Training Details

Training Dataset

Unnamed Dataset

Size: 129,371 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 13 tokens
mean: 22.58 tokens
max: 134 tokens

min: 7 tokens
mean: 169.6 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 13 tokens mean: 22.58 tokens max: 134 tokens	min: 7 tokens mean: 169.6 tokens max: 512 tokens

Samples:

query	answer
`query: hotel and restaurant employees and bartenders international union`	document: Hotel Employees and Restaurant Employees Union The Hotel Employees and Restaurant Employees Union (HERE) was a United States labor union representing workers of the hospitality industry, formed in 1891. In 2004, HERE merged with the Union of Needletrades, Industrial, and Textile Employees (UNITE) to form UNITE HERE. HERE notably organized the staff of Yale University in 1984. Other major employers that contracted with this union included several large casinos (Harrah's, Caesars Palace, and Wynn Resorts); hotels (Hilton, Hyatt and Starwood), and Walt Disney World. HERE was affiliated with the AFL-CIO.
`query: 多肢离断伤的并发症是什么？`	`document: 失血性休克；血循环危象；急性肾功能衰竭`
`query: who is the father of kelly taylor's son on 90210`	`document: Kelly Taylor (90210) In 2008, Kelly Taylor returned in the spin-off 90210, now working as a guidance counselor at her alma mater West Beverly Hills High School. It was revealed that in the intervening years, she attained a master's degree and had a son named Sammy with Dylan. She and Dylan ended their relationship soon after. It was also revealed that West Beverly principal Harry Wilson was Kelly's neighbor growing up.[39]`

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
), 'temperature': 0.01}

Evaluation Dataset

Unnamed Dataset

Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 11 tokens
mean: 22.61 tokens
max: 146 tokens

min: 7 tokens
mean: 164.27 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 11 tokens mean: 22.61 tokens max: 146 tokens	min: 7 tokens mean: 164.27 tokens max: 512 tokens

Samples:

query	answer
`query: 微创经皮肾镜手术的推荐药有些什么？`	`document: 阿司匹林`
`query: why are the fires in ca called the thomas fires`	document: Thomas Fire On December 4, 2017, the Thomas Fire was reported at 6:26 p.m. PST,[36] to the north of Santa Paula, near Steckel Park and Thomas Aquinas College,[3][24] after which the fire is named.[37] That night, the small brush fire exploded in size and raced through the rugged mountain terrain that lies west of Santa Paula, between Ventura and Ojai.[19][38] Officials blamed strong Santa Ana winds that gusted up to 60 miles per hour (97 km/h) for the sudden expansion.[28][39] Soon after the fire had started, a second blaze was ignited nearly 30 minutes later, about 4 miles (6.4 km) to the north in Upper Ojai at the top of Koenigstein Road.[40] According to eyewitnesses, this second fire was sparked by an explosion in the power line over the area. The second fire was rapidly expanded by the strong Santa Ana winds, and soon merged into the Thomas Fire later that night.[40]
`query: which mountain man rediscovered south pass and brought back important information about this trail`	document: Jedediah Smith Jedediah Strong Smith (January 6, 1799 – May 27, 1831), was a clerk, frontiersman, hunter, trapper, author, cartographer, and explorer of the Rocky Mountains, the North American West, and the Southwest during the early 19th century. After 75 years of obscurity following his death, Smith was rediscovered as the American whose explorations led to the use of the 20-mile (32 km)-wide South Pass as the dominant point of crossing the Continental Divide for pioneers on the Oregon Trail.

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
), 'temperature': 0.01}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.05
seed: 12
bf16: True
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
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: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
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: 12
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: False
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}
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
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
dispatch_batches: None
split_batches: 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: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0010	1	31.7042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0049	5	32.9433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0099	10	27.0338	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0148	15	18.1598	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0198	20	12.5771	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0247	25	8.6872	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0297	30	6.0455	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0346	35	5.1917	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0396	40	4.8424	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0445	45	4.4785	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0495	50	4.1896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0544	55	4.2621	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0593	60	3.8401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0643	65	3.9482	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0692	70	3.7762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0742	75	3.4895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0791	80	3.5892	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0841	85	3.5312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0890	90	3.3244	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0940	95	3.4369	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0989	100	3.1867	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1039	105	3.1734	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1088	110	3.2156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1137	115	2.8888	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1187	120	2.8613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1236	125	2.8905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1286	130	2.5984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1335	135	2.6853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1385	140	2.7013	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1434	145	2.5577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1484	150	2.6287	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1533	155	2.6481	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1583	160	2.7741	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1632	165	2.5738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1682	170	2.5335	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1731	175	2.531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1780	180	2.437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1830	185	2.4836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1879	190	2.4642	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1929	195	2.399	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1978	200	2.3896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2028	205	2.3738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2077	210	2.5518	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2127	215	2.4836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2176	220	2.2157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2226	225	2.2986	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2275	230	2.4967	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2324	235	2.121	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2374	240	2.4301	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2423	245	2.5054	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2473	250	2.3213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2522	255	2.1182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2572	260	2.2966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2621	265	2.2662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2671	270	2.3188	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2720	275	2.1836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2770	280	2.2206	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2819	285	2.3144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2868	290	2.2496	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2918	295	1.9909	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2967	300	2.1294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3017	305	2.119	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3066	310	2.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3116	315	2.127	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3165	320	2.1309	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3215	325	2.0868	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3264	330	1.9429	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3314	335	1.9	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3363	340	1.82	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3412	345	1.9731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3462	350	2.0156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3511	355	2.0106	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3561	360	1.9383	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3610	365	2.0491	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3660	370	1.8893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3709	375	1.958	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3759	380	1.9821	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3808	385	2.024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3858	390	2.0182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3907	395	1.9659	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3956	400	1.8339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4006	405	1.9081	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4055	410	1.7876	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4105	415	1.8371	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4154	420	1.8274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4204	425	1.7863	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4253	430	1.9064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4303	435	1.7721	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4352	440	1.7162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4402	445	1.9112	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4451	450	1.9384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4500	455	1.8096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4550	460	1.7145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4599	465	1.784	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4649	470	1.9506	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4698	475	1.7243	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4748	480	1.8003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4797	485	1.7568	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4847	490	1.5696	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4896	495	1.8973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4946	500	1.6981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4995	505	1.7616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5045	510	1.6573	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5094	515	1.8685	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5143	520	1.8532	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5193	525	1.7603	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5242	530	1.7636	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5292	535	1.4829	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5341	540	1.6959	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5391	545	1.6389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5440	550	1.6624	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5490	555	1.8193	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5539	560	1.7144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5589	565	1.4954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5638	570	1.6659	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5687	575	1.669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5737	580	1.6931	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5786	585	1.6894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5836	590	1.6437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5885	595	1.7259	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5935	600	1.7937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5984	605	1.7279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6034	610	1.6769	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6083	615	1.4731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6133	620	1.6466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6182	625	1.6954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6231	630	1.6224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6281	635	1.62	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6330	640	1.5795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6380	645	1.5245	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6429	650	1.7629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6479	655	1.5767	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6528	660	1.6749	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6578	665	1.5602	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6627	670	1.6768	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6677	675	1.8311	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6726	680	1.5973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6775	685	1.5066	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6825	690	1.6036	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6874	695	1.7857	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6924	700	1.4387	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6973	705	1.5886	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7023	710	1.551	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7072	715	1.5561	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7122	720	1.4458	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7171	725	1.5703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7221	730	1.6162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7270	735	1.5643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7319	740	1.4894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7369	745	1.6413	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7418	750	1.5406	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7468	755	1.5185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7517	760	1.488	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7567	765	1.5041	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7616	770	1.4665	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7666	775	1.5252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7715	780	1.4925	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7765	785	1.3833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7814	790	1.3808	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7864	795	1.5468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7913	800	1.5317	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7962	805	1.5385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8012	810	1.4012	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8061	815	1.5531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8111	820	1.6032	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8160	825	1.4053	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8210	830	1.5082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8259	835	1.5559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8309	840	1.4286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8358	845	1.4336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8408	850	1.3731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8457	855	1.5706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8506	860	1.4184	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8556	865	1.4312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8605	870	1.4364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8655	875	1.5605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8704	880	1.4219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8754	885	1.4082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8803	890	1.3846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8853	895	1.4292	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8902	900	1.4195	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8952	905	1.5103	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9001	910	1.5041	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	915	1.427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9100	920	1.4385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9149	925	1.298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9199	930	1.4499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9248	935	1.4752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9298	940	1.4752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9347	945	1.3705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9397	950	1.4567	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9446	955	1.3364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9496	960	1.376	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9545	965	1.35	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9594	970	1.5841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9644	975	1.3449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9693	980	1.2132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9743	985	1.3414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9792	990	1.5148	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9842	995	1.3866	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9891	1000	1.2051	1.3370	0.0906	0.1578	0.0712	0.1504	0.1887	0.1554	0.0466	0.2528	0.6197	0.0672	0.2857	0.2291	0.2718	0.1990
0.9941	1005	1.3021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9990	1010	1.391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0040	1015	1.1452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0089	1020	1.3989	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0138	1025	1.2142	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0188	1030	1.2472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0237	1035	1.3058	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0287	1040	1.2643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0336	1045	1.2581	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0386	1050	1.2434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0435	1055	1.1874	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0485	1060	1.0421	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0534	1065	1.3834	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0584	1070	1.3279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0633	1075	1.3779	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0682	1080	1.3071	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0732	1085	1.1569	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0781	1090	1.2427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0831	1095	1.1607	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0880	1100	1.2691	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0930	1105	1.2936	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0979	1110	1.2527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1029	1115	1.1143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1078	1120	1.1508	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1128	1125	1.1627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1177	1130	0.9774	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1227	1135	1.1827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1276	1140	0.9429	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1325	1145	1.0029	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1375	1150	1.0764	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1424	1155	1.0555	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1474	1160	1.0559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1523	1165	1.0081	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1573	1170	1.1928	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1622	1175	1.0774	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1672	1180	0.9185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1721	1185	1.0838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1771	1190	0.9981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1820	1195	1.0395	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1869	1200	0.9522	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1919	1205	0.9652	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1968	1210	1.0276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2018	1215	0.9663	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2067	1220	1.1356	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2117	1225	1.159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2166	1230	0.8575	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2216	1235	0.9134	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2265	1240	1.1889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2315	1245	0.935	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2364	1250	0.975	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2413	1255	1.073	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2463	1260	1.0709	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2512	1265	0.9241	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2562	1270	1.0101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2611	1275	1.1451	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2661	1280	1.0501	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2710	1285	0.9724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2760	1290	0.9222	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2809	1295	1.086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2859	1300	0.973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2908	1305	0.9287	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2957	1310	0.9051	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3007	1315	0.9531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3056	1320	0.9605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3106	1325	0.8778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3155	1330	0.9399	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3205	1335	0.9185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3254	1340	0.9078	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3304	1345	0.8266	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3353	1350	0.8186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3403	1355	0.9394	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3452	1360	1.0972	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3501	1365	0.8895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3551	1370	0.8678	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3600	1375	0.9493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3650	1380	0.8449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3699	1385	0.917	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3749	1390	0.8899	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3798	1395	0.9516	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3848	1400	0.9538	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3897	1405	0.9964	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3947	1410	0.9123	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3996	1415	0.86	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4045	1420	0.9382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4095	1425	0.764	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4144	1430	0.9161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4194	1435	0.937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4243	1440	0.8487	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4293	1445	0.7928	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4342	1450	0.8586	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4392	1455	0.9355	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4441	1460	0.965	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4491	1465	0.9019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4540	1470	0.8624	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4590	1475	0.8204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4639	1480	1.0131	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4688	1485	0.9222	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4738	1490	0.9182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4787	1495	0.8247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4837	1500	0.7746	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4886	1505	0.882	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4936	1510	0.8482	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4985	1515	0.9623	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5035	1520	0.8804	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5084	1525	0.8874	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5134	1530	0.9747	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5183	1535	0.8805	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5232	1540	0.8776	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5282	1545	0.7627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5331	1550	0.8975	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5381	1555	0.8213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5430	1560	0.9472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5480	1565	0.9379	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5529	1570	0.9312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5579	1575	0.7866	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5628	1580	0.8629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5678	1585	0.8156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5727	1590	0.8737	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5776	1595	0.942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5826	1600	0.8167	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5875	1605	0.9468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5925	1610	0.9117	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5974	1615	1.0137	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6024	1620	0.8357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6073	1625	0.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6123	1630	0.905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6172	1635	0.9265	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6222	1640	0.846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6271	1645	0.7729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6320	1650	0.7885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6370	1655	0.8717	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6419	1660	0.9845	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6469	1665	0.8286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6518	1670	0.8979	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6568	1675	0.8502	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6617	1680	0.9423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6667	1685	1.0128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6716	1690	0.8535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6766	1695	0.737	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6815	1700	0.9871	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6864	1705	0.8828	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6914	1710	0.8178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6963	1715	0.7703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7013	1720	0.8739	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7062	1725	0.8582	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7112	1730	0.9181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7161	1735	0.8801	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7211	1740	0.8009	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7260	1745	0.9779	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7310	1750	0.7777	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7359	1755	0.7864	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7409	1760	1.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7458	1765	0.7776	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7507	1770	0.8122	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7557	1775	0.8025	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7606	1780	0.7559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7656	1785	0.8819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7705	1790	0.8901	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7755	1795	0.7598	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7804	1800	0.7542	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7854	1805	0.8178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7903	1810	0.8374	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7953	1815	0.8363	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8002	1820	0.8177	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8051	1825	0.9488	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8101	1830	0.9959	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8150	1835	0.7942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8200	1840	0.8747	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8249	1845	0.9053	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8299	1850	0.7853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8348	1855	0.838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8398	1860	0.7732	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8447	1865	0.8613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8497	1870	0.791	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8546	1875	0.8203	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8595	1880	0.7558	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8645	1885	0.9918	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8694	1890	0.8272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8744	1895	0.8552	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8793	1900	0.8135	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8843	1905	0.8297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8892	1910	0.7844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8942	1915	0.8466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8991	1920	0.9099	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9041	1925	0.8139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9090	1930	0.8628	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9139	1935	0.6778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9189	1940	0.8251	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9238	1945	0.8915	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9288	1950	0.8136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9337	1955	0.8879	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9387	1960	0.8758	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9436	1965	0.8153	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9486	1970	0.7253	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9535	1975	0.8493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9585	1980	1.0186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9634	1985	0.8412	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9683	1990	0.7027	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9733	1995	0.744	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9782	2000	0.9555	1.1452	0.1064	0.1577	0.0780	0.1597	0.2144	0.1550	0.0513	0.2643	0.6316	0.0525	0.3670	0.2485	0.2937	0.2139
1.9832	2005	0.9095	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9881	2010	0.7378	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9931	2015	0.8024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9980	2020	0.9107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0	2022	-	-	0.1074	0.1565	0.0780	0.1599	0.2152	0.1550	0.0514	0.2669	0.6316	0.0544	0.3668	0.2485	0.2934	0.2142

Framework Versions

Python: 3.11.2
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.4.0+cu121
Accelerate: 1.0.1
Datasets: 3.1.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

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