metadata
base_model: BAAI/bge-large-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: other
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:104022
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
IZEA's market capitalization is $36 million, indicating potential for
raising additional funds if needed.
sentences:
- >-
IZEA's market capitalization is $35.65 million, with a P/E ratio of
-5.19, indicating unprofitability in the last twelve months as of Q3
2023.
- >-
NetApp sells its products and services through a direct sales force and
an ecosystem of partners.
- >-
SAIL's expansion plans have raised concerns among investors, leading to
underperformance in its stock compared to the Nifty 500 index.
- source_sentence: >-
Infinity Mining conducted an eight-hole reverse-circulation (RC) drilling
campaign at its Tambourah South project in Western Australia, targeting
lithium-caesium-tantalum (LCT) pegmatites.
sentences:
- >-
The disclosure must be made to a Regulatory Information Service, as
required by Rule 8 of the Takeover Code.
- >-
Infinity Mining plans to expand its exploration efforts at Tambourah
South, including the use of new technologies and techniques to identify
and evaluate concealed pegmatite targets.
- >-
Russia aims to export over 65 million tons of grain during the season, a
record volume.
- source_sentence: >-
Ukraine expects to receive about $1.5 billion from other international
financial institutions, including the World Bank, in 2024.
sentences:
- >-
Ukraine has an ongoing cooperation with the International Monetary Fund
(IMF), with a 48-month lending program worth $15.6 billion, receiving
$3.6 billion this year and expecting $900 million in December, and $5.4
billion in 2024 subject to reform targets and economic indicators.
- >-
Vodacom Group could be considered a reasonable income stock despite the
dividend cut, with a solid payout ratio but a less impressive dividend
track record.
- >-
CoStar Group employees, members of the Black Excellence Network and
Women's Network, worked alongside Feed More volunteers to facilitate the
giveaway.
- source_sentence: >-
WaFd paid out 27% of its profit in dividends last year, indicating a
comfortable payout ratio.
sentences:
- >-
USP35 knockdown in Hep3B cells inhibits tumor growth and reduces the
expression of ABHD17C, p-PI3K, and p-AKT in xenograft HCC models.
- >-
Nasdaq will suspend trading of CohBar, Inc.'s common stock at the
opening of business on November 29, 2023, unless the company requests a
hearing before a Nasdaq Hearings Panel to appeal the determination.
- >-
WaFd's earnings per share have grown at a rate of 9.4% per annum over
the past five years, demonstrating consistent growth.
- source_sentence: >-
Scope Control provides a digital ledger of inspected lines, creating a
credible line history that underscores Custom Truck One Source's
commitment to operational safety.
sentences:
- >-
China has implemented measures to address hidden debt, including
extending debt maturities, selling assets to repay debts, and replacing
short-term local government financial vehicle debts with longer-term,
lower-cost refinancing bonds.
- >-
Scope Control utilizes advanced Computer Vision and Deep Learning
technologies to accurately assess line health and categorize it as new,
used, or bad based on safety standards and residual break strength.
- >-
The current management regulations for the national social security fund
were approved in December 2001 and have been implemented for over 20
years. The MOF stated that parts of the content no longer address the
current needs of the Chinese financial market and the investment trend
for the national social security fund, necessitating a systematic and
thorough revision.
model-index:
- name: VANTIGE_NEWS_v3_EDGE_DETECTION
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.828
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.986
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.992
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.828
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19720000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0992
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.828
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.986
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.992
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9261911001883877
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9034555555555557
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9038902618135377
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.83
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.986
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.83
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1972
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.83
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.986
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9264556449878328
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9044190476190478
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9049635033323674
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.83
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.988
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.83
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19760000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.83
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.988
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9262131769268145
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9041
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9046338347982871
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.828
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.984
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.828
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1968
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.828
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.984
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9250967573273415
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.90265
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9031974089635855
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.832
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.986
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.992
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.832
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19720000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0992
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.832
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.986
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.992
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9276434508354098
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9054333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9058527890466532
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.822
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.978
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.986
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.822
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32599999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19720000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.822
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.986
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9224148281915946
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8989999999999999
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8995256769374417
name: Cosine Map@100
VANTIGE_NEWS_v3_EDGE_DETECTION
This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5. 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: BAAI/bge-large-en-v1.5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Language: en
- License: other
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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()
)
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
model = SentenceTransformer("dustyatx/news_v3_graph_edges_embeddings_setence_paragraph")
sentences = [
"Scope Control provides a digital ledger of inspected lines, creating a credible line history that underscores Custom Truck One Source's commitment to operational safety.",
'Scope Control utilizes advanced Computer Vision and Deep Learning technologies to accurately assess line health and categorize it as new, used, or bad based on safety standards and residual break strength.',
'China has implemented measures to address hidden debt, including extending debt maturities, selling assets to repay debts, and replacing short-term local government financial vehicle debts with longer-term, lower-cost refinancing bonds.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.828 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.986 |
cosine_accuracy@10 |
0.992 |
cosine_precision@1 |
0.828 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1972 |
cosine_precision@10 |
0.0992 |
cosine_recall@1 |
0.828 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.986 |
cosine_recall@10 |
0.992 |
cosine_ndcg@10 |
0.9262 |
cosine_mrr@10 |
0.9035 |
cosine_map@100 |
0.9039 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.83 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.986 |
cosine_accuracy@10 |
0.99 |
cosine_precision@1 |
0.83 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1972 |
cosine_precision@10 |
0.099 |
cosine_recall@1 |
0.83 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.986 |
cosine_recall@10 |
0.99 |
cosine_ndcg@10 |
0.9265 |
cosine_mrr@10 |
0.9044 |
cosine_map@100 |
0.905 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.83 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.988 |
cosine_accuracy@10 |
0.99 |
cosine_precision@1 |
0.83 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1976 |
cosine_precision@10 |
0.099 |
cosine_recall@1 |
0.83 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.988 |
cosine_recall@10 |
0.99 |
cosine_ndcg@10 |
0.9262 |
cosine_mrr@10 |
0.9041 |
cosine_map@100 |
0.9046 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.828 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.984 |
cosine_accuracy@10 |
0.99 |
cosine_precision@1 |
0.828 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1968 |
cosine_precision@10 |
0.099 |
cosine_recall@1 |
0.828 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.984 |
cosine_recall@10 |
0.99 |
cosine_ndcg@10 |
0.9251 |
cosine_mrr@10 |
0.9026 |
cosine_map@100 |
0.9032 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.832 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.986 |
cosine_accuracy@10 |
0.992 |
cosine_precision@1 |
0.832 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1972 |
cosine_precision@10 |
0.0992 |
cosine_recall@1 |
0.832 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.986 |
cosine_recall@10 |
0.992 |
cosine_ndcg@10 |
0.9276 |
cosine_mrr@10 |
0.9054 |
cosine_map@100 |
0.9059 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.822 |
cosine_accuracy@3 |
0.978 |
cosine_accuracy@5 |
0.986 |
cosine_accuracy@10 |
0.99 |
cosine_precision@1 |
0.822 |
cosine_precision@3 |
0.326 |
cosine_precision@5 |
0.1972 |
cosine_precision@10 |
0.099 |
cosine_recall@1 |
0.822 |
cosine_recall@3 |
0.978 |
cosine_recall@5 |
0.986 |
cosine_recall@10 |
0.99 |
cosine_ndcg@10 |
0.9224 |
cosine_mrr@10 |
0.899 |
cosine_map@100 |
0.8995 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 104,022 training samples
- Columns:
anchor
and positive
- Approximate statistics based on the first 1000 samples:
|
anchor |
positive |
type |
string |
string |
details |
- min: 16 tokens
- mean: 36.53 tokens
- max: 102 tokens
|
- min: 13 tokens
- mean: 35.17 tokens
- max: 117 tokens
|
- Samples:
anchor |
positive |
The general public, including retail investors, collectively own 11% of FINEOS Corporation Holdings' shares, representing a minority stake in the company. |
Private companies, with their 50% ownership stake, have substantial influence over FINEOS Corporation Holdings' management and governance decisions. |
A study by the Insurance Institute for Highway Safety (IIHS) found that SUVs and vans with hood heights exceeding 40 inches are approximately 45% more likely to cause pedestrian fatalities compared to vehicles with hood heights of 30 inches or less and a sloping profile. |
Vehicles with front ends exceeding 35 inches in height, particularly those lacking a sloping profile, are more likely to cause severe head, torso, and hip injuries to pedestrians. |
SpringWorks Therapeutics has a portfolio of small molecule targeted oncology product candidates and is conducting clinical trials for rare tumor types and genetically defined cancers. |
SpringWorks Therapeutics operates in the biopharmaceutical industry, specializing in precision medicine for underserved patient populations. |
- Loss:
MatryoshkaLoss
with these parameters:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: steps
per_device_train_batch_size
: 30
per_device_eval_batch_size
: 20
gradient_accumulation_steps
: 8
learning_rate
: 3e-05
num_train_epochs
: 2
lr_scheduler_type
: cosine
warmup_ratio
: 0.2
bf16
: True
tf32
: True
dataloader_num_workers
: 30
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
: steps
prediction_loss_only
: True
per_device_train_batch_size
: 30
per_device_eval_batch_size
: 20
per_gpu_train_batch_size
: None
per_gpu_eval_batch_size
: None
gradient_accumulation_steps
: 8
eval_accumulation_steps
: None
torch_empty_cache_steps
: None
learning_rate
: 3e-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
: cosine
lr_scheduler_kwargs
: {}
warmup_ratio
: 0.2
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
: 30
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}
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
: False
hub_always_push
: False
gradient_checkpointing
: False
gradient_checkpointing_kwargs
: None
include_inputs_for_metrics
: False
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
eval_use_gather_object
: False
batch_sampler
: no_duplicates
multi_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch |
Step |
Training Loss |
dim_1024_cosine_map@100 |
dim_128_cosine_map@100 |
dim_256_cosine_map@100 |
dim_512_cosine_map@100 |
dim_64_cosine_map@100 |
dim_768_cosine_map@100 |
0.0023 |
1 |
1.8313 |
- |
- |
- |
- |
- |
- |
0.0046 |
2 |
1.9678 |
- |
- |
- |
- |
- |
- |
0.0069 |
3 |
0.8038 |
- |
- |
- |
- |
- |
- |
0.0092 |
4 |
0.7993 |
- |
- |
- |
- |
- |
- |
0.0115 |
5 |
0.7926 |
- |
- |
- |
- |
- |
- |
0.0138 |
6 |
0.9348 |
- |
- |
- |
- |
- |
- |
0.0161 |
7 |
0.8707 |
- |
- |
- |
- |
- |
- |
0.0185 |
8 |
0.7293 |
- |
- |
- |
- |
- |
- |
0.0208 |
9 |
0.6618 |
- |
- |
- |
- |
- |
- |
0.0231 |
10 |
0.846 |
- |
- |
- |
- |
- |
- |
0.0254 |
11 |
0.6836 |
- |
- |
- |
- |
- |
- |
0.0277 |
12 |
0.7034 |
- |
- |
- |
- |
- |
- |
0.0300 |
13 |
0.7987 |
- |
- |
- |
- |
- |
- |
0.0323 |
14 |
0.6443 |
- |
- |
- |
- |
- |
- |
0.0346 |
15 |
0.5975 |
- |
- |
- |
- |
- |
- |
0.0369 |
16 |
0.4471 |
- |
- |
- |
- |
- |
- |
0.0392 |
17 |
0.4739 |
- |
- |
- |
- |
- |
- |
0.0415 |
18 |
0.4136 |
- |
- |
- |
- |
- |
- |
0.0438 |
19 |
0.3865 |
- |
- |
- |
- |
- |
- |
0.0461 |
20 |
0.3421 |
- |
- |
- |
- |
- |
- |
0.0484 |
21 |
0.5076 |
- |
- |
- |
- |
- |
- |
0.0507 |
22 |
0.1878 |
- |
- |
- |
- |
- |
- |
0.0531 |
23 |
0.3597 |
- |
- |
- |
- |
- |
- |
0.0554 |
24 |
0.23 |
- |
- |
- |
- |
- |
- |
0.0577 |
25 |
0.1331 |
- |
- |
- |
- |
- |
- |
0.0600 |
26 |
0.1793 |
- |
- |
- |
- |
- |
- |
0.0623 |
27 |
0.1309 |
- |
- |
- |
- |
- |
- |
0.0646 |
28 |
0.1077 |
- |
- |
- |
- |
- |
- |
0.0669 |
29 |
0.1681 |
- |
- |
- |
- |
- |
- |
0.0692 |
30 |
0.055 |
- |
- |
- |
- |
- |
- |
0.0715 |
31 |
0.1062 |
- |
- |
- |
- |
- |
- |
0.0738 |
32 |
0.0672 |
- |
- |
- |
- |
- |
- |
0.0761 |
33 |
0.067 |
- |
- |
- |
- |
- |
- |
0.0784 |
34 |
0.0953 |
- |
- |
- |
- |
- |
- |
0.0807 |
35 |
0.0602 |
- |
- |
- |
- |
- |
- |
0.0830 |
36 |
0.1312 |
- |
- |
- |
- |
- |
- |
0.0854 |
37 |
0.0356 |
- |
- |
- |
- |
- |
- |
0.0877 |
38 |
0.0707 |
- |
- |
- |
- |
- |
- |
0.0900 |
39 |
0.1525 |
- |
- |
- |
- |
- |
- |
0.0923 |
40 |
0.0362 |
- |
- |
- |
- |
- |
- |
0.0946 |
41 |
0.253 |
- |
- |
- |
- |
- |
- |
0.0969 |
42 |
0.0572 |
- |
- |
- |
- |
- |
- |
0.0992 |
43 |
0.1031 |
- |
- |
- |
- |
- |
- |
0.1015 |
44 |
0.1023 |
- |
- |
- |
- |
- |
- |
0.1038 |
45 |
0.052 |
- |
- |
- |
- |
- |
- |
0.1061 |
46 |
0.0614 |
- |
- |
- |
- |
- |
- |
0.1084 |
47 |
0.1256 |
- |
- |
- |
- |
- |
- |
0.1107 |
48 |
0.1624 |
- |
- |
- |
- |
- |
- |
0.1130 |
49 |
0.0363 |
- |
- |
- |
- |
- |
- |
0.1153 |
50 |
0.2001 |
0.8949 |
0.8940 |
0.8947 |
0.8950 |
0.8864 |
0.8972 |
0.1176 |
51 |
0.0846 |
- |
- |
- |
- |
- |
- |
0.1200 |
52 |
0.0338 |
- |
- |
- |
- |
- |
- |
0.1223 |
53 |
0.0648 |
- |
- |
- |
- |
- |
- |
0.1246 |
54 |
0.1232 |
- |
- |
- |
- |
- |
- |
0.1269 |
55 |
0.0318 |
- |
- |
- |
- |
- |
- |
0.1292 |
56 |
0.1148 |
- |
- |
- |
- |
- |
- |
0.1315 |
57 |
0.0826 |
- |
- |
- |
- |
- |
- |
0.1338 |
58 |
0.034 |
- |
- |
- |
- |
- |
- |
0.1361 |
59 |
0.0492 |
- |
- |
- |
- |
- |
- |
0.1384 |
60 |
0.0427 |
- |
- |
- |
- |
- |
- |
0.1407 |
61 |
0.0709 |
- |
- |
- |
- |
- |
- |
0.1430 |
62 |
0.0494 |
- |
- |
- |
- |
- |
- |
0.1453 |
63 |
0.0554 |
- |
- |
- |
- |
- |
- |
0.1476 |
64 |
0.061 |
- |
- |
- |
- |
- |
- |
0.1499 |
65 |
0.1155 |
- |
- |
- |
- |
- |
- |
0.1522 |
66 |
0.0419 |
- |
- |
- |
- |
- |
- |
0.1546 |
67 |
0.0185 |
- |
- |
- |
- |
- |
- |
0.1569 |
68 |
0.0559 |
- |
- |
- |
- |
- |
- |
0.1592 |
69 |
0.0219 |
- |
- |
- |
- |
- |
- |
0.1615 |
70 |
0.0302 |
- |
- |
- |
- |
- |
- |
0.1638 |
71 |
0.0322 |
- |
- |
- |
- |
- |
- |
0.1661 |
72 |
0.0604 |
- |
- |
- |
- |
- |
- |
0.1684 |
73 |
0.038 |
- |
- |
- |
- |
- |
- |
0.1707 |
74 |
0.0971 |
- |
- |
- |
- |
- |
- |
0.1730 |
75 |
0.0384 |
- |
- |
- |
- |
- |
- |
0.1753 |
76 |
0.0887 |
- |
- |
- |
- |
- |
- |
0.1776 |
77 |
0.0495 |
- |
- |
- |
- |
- |
- |
0.1799 |
78 |
0.0203 |
- |
- |
- |
- |
- |
- |
0.1822 |
79 |
0.0669 |
- |
- |
- |
- |
- |
- |
0.1845 |
80 |
0.0319 |
- |
- |
- |
- |
- |
- |
0.1869 |
81 |
0.0177 |
- |
- |
- |
- |
- |
- |
0.1892 |
82 |
0.0303 |
- |
- |
- |
- |
- |
- |
0.1915 |
83 |
0.037 |
- |
- |
- |
- |
- |
- |
0.1938 |
84 |
0.0122 |
- |
- |
- |
- |
- |
- |
0.1961 |
85 |
0.0377 |
- |
- |
- |
- |
- |
- |
0.1984 |
86 |
0.0578 |
- |
- |
- |
- |
- |
- |
0.2007 |
87 |
0.0347 |
- |
- |
- |
- |
- |
- |
0.2030 |
88 |
0.1288 |
- |
- |
- |
- |
- |
- |
0.2053 |
89 |
0.0964 |
- |
- |
- |
- |
- |
- |
0.2076 |
90 |
0.0172 |
- |
- |
- |
- |
- |
- |
0.2099 |
91 |
0.0726 |
- |
- |
- |
- |
- |
- |
0.2122 |
92 |
0.0225 |
- |
- |
- |
- |
- |
- |
0.2145 |
93 |
0.1011 |
- |
- |
- |
- |
- |
- |
0.2168 |
94 |
0.0248 |
- |
- |
- |
- |
- |
- |
0.2191 |
95 |
0.0431 |
- |
- |
- |
- |
- |
- |
0.2215 |
96 |
0.0243 |
- |
- |
- |
- |
- |
- |
0.2238 |
97 |
0.0221 |
- |
- |
- |
- |
- |
- |
0.2261 |
98 |
0.0529 |
- |
- |
- |
- |
- |
- |
0.2284 |
99 |
0.0459 |
- |
- |
- |
- |
- |
- |
0.2307 |
100 |
0.0869 |
0.9026 |
0.8967 |
0.8950 |
0.9003 |
0.8915 |
0.9009 |
0.2330 |
101 |
0.0685 |
- |
- |
- |
- |
- |
- |
0.2353 |
102 |
0.0801 |
- |
- |
- |
- |
- |
- |
0.2376 |
103 |
0.025 |
- |
- |
- |
- |
- |
- |
0.2399 |
104 |
0.0556 |
- |
- |
- |
- |
- |
- |
0.2422 |
105 |
0.0146 |
- |
- |
- |
- |
- |
- |
0.2445 |
106 |
0.0335 |
- |
- |
- |
- |
- |
- |
0.2468 |
107 |
0.0441 |
- |
- |
- |
- |
- |
- |
0.2491 |
108 |
0.0187 |
- |
- |
- |
- |
- |
- |
0.2514 |
109 |
0.1027 |
- |
- |
- |
- |
- |
- |
0.2537 |
110 |
0.0189 |
- |
- |
- |
- |
- |
- |
0.2561 |
111 |
0.1262 |
- |
- |
- |
- |
- |
- |
0.2584 |
112 |
0.1193 |
- |
- |
- |
- |
- |
- |
0.2607 |
113 |
0.0285 |
- |
- |
- |
- |
- |
- |
0.2630 |
114 |
0.0226 |
- |
- |
- |
- |
- |
- |
0.2653 |
115 |
0.1209 |
- |
- |
- |
- |
- |
- |
0.2676 |
116 |
0.0765 |
- |
- |
- |
- |
- |
- |
0.2699 |
117 |
0.1405 |
- |
- |
- |
- |
- |
- |
0.2722 |
118 |
0.0629 |
- |
- |
- |
- |
- |
- |
0.2745 |
119 |
0.0413 |
- |
- |
- |
- |
- |
- |
0.2768 |
120 |
0.0572 |
- |
- |
- |
- |
- |
- |
0.2791 |
121 |
0.0192 |
- |
- |
- |
- |
- |
- |
0.2814 |
122 |
0.0949 |
- |
- |
- |
- |
- |
- |
0.2837 |
123 |
0.0398 |
- |
- |
- |
- |
- |
- |
0.2860 |
124 |
0.0596 |
- |
- |
- |
- |
- |
- |
0.2884 |
125 |
0.0243 |
- |
- |
- |
- |
- |
- |
0.2907 |
126 |
0.0636 |
- |
- |
- |
- |
- |
- |
0.2930 |
127 |
0.0367 |
- |
- |
- |
- |
- |
- |
0.2953 |
128 |
0.0542 |
- |
- |
- |
- |
- |
- |
0.2976 |
129 |
0.0149 |
- |
- |
- |
- |
- |
- |
0.2999 |
130 |
0.097 |
- |
- |
- |
- |
- |
- |
0.3022 |
131 |
0.0213 |
- |
- |
- |
- |
- |
- |
0.3045 |
132 |
0.027 |
- |
- |
- |
- |
- |
- |
0.3068 |
133 |
0.0577 |
- |
- |
- |
- |
- |
- |
0.3091 |
134 |
0.0143 |
- |
- |
- |
- |
- |
- |
0.3114 |
135 |
0.0285 |
- |
- |
- |
- |
- |
- |
0.3137 |
136 |
0.033 |
- |
- |
- |
- |
- |
- |
0.3160 |
137 |
0.0412 |
- |
- |
- |
- |
- |
- |
0.3183 |
138 |
0.0125 |
- |
- |
- |
- |
- |
- |
0.3206 |
139 |
0.0512 |
- |
- |
- |
- |
- |
- |
0.3230 |
140 |
0.0189 |
- |
- |
- |
- |
- |
- |
0.3253 |
141 |
0.124 |
- |
- |
- |
- |
- |
- |
0.3276 |
142 |
0.0118 |
- |
- |
- |
- |
- |
- |
0.3299 |
143 |
0.017 |
- |
- |
- |
- |
- |
- |
0.3322 |
144 |
0.025 |
- |
- |
- |
- |
- |
- |
0.3345 |
145 |
0.0187 |
- |
- |
- |
- |
- |
- |
0.3368 |
146 |
0.0141 |
- |
- |
- |
- |
- |
- |
0.3391 |
147 |
0.0325 |
- |
- |
- |
- |
- |
- |
0.3414 |
148 |
0.0582 |
- |
- |
- |
- |
- |
- |
0.3437 |
149 |
0.0611 |
- |
- |
- |
- |
- |
- |
0.3460 |
150 |
0.0261 |
0.9047 |
0.8995 |
0.9003 |
0.9022 |
0.8998 |
0.9032 |
0.3483 |
151 |
0.014 |
- |
- |
- |
- |
- |
- |
0.3506 |
152 |
0.0077 |
- |
- |
- |
- |
- |
- |
0.3529 |
153 |
0.022 |
- |
- |
- |
- |
- |
- |
0.3552 |
154 |
0.0328 |
- |
- |
- |
- |
- |
- |
0.3576 |
155 |
0.0124 |
- |
- |
- |
- |
- |
- |
0.3599 |
156 |
0.0103 |
- |
- |
- |
- |
- |
- |
0.3622 |
157 |
0.0607 |
- |
- |
- |
- |
- |
- |
0.3645 |
158 |
0.0121 |
- |
- |
- |
- |
- |
- |
0.3668 |
159 |
0.0761 |
- |
- |
- |
- |
- |
- |
0.3691 |
160 |
0.0981 |
- |
- |
- |
- |
- |
- |
0.3714 |
161 |
0.1071 |
- |
- |
- |
- |
- |
- |
0.3737 |
162 |
0.1307 |
- |
- |
- |
- |
- |
- |
0.3760 |
163 |
0.0524 |
- |
- |
- |
- |
- |
- |
0.3783 |
164 |
0.0726 |
- |
- |
- |
- |
- |
- |
0.3806 |
165 |
0.0636 |
- |
- |
- |
- |
- |
- |
0.3829 |
166 |
0.0428 |
- |
- |
- |
- |
- |
- |
0.3852 |
167 |
0.0111 |
- |
- |
- |
- |
- |
- |
0.3875 |
168 |
0.0542 |
- |
- |
- |
- |
- |
- |
0.3899 |
169 |
0.0193 |
- |
- |
- |
- |
- |
- |
0.3922 |
170 |
0.0095 |
- |
- |
- |
- |
- |
- |
0.3945 |
171 |
0.0464 |
- |
- |
- |
- |
- |
- |
0.3968 |
172 |
0.0167 |
- |
- |
- |
- |
- |
- |
0.3991 |
173 |
0.0209 |
- |
- |
- |
- |
- |
- |
0.4014 |
174 |
0.0359 |
- |
- |
- |
- |
- |
- |
0.4037 |
175 |
0.071 |
- |
- |
- |
- |
- |
- |
0.4060 |
176 |
0.0189 |
- |
- |
- |
- |
- |
- |
0.4083 |
177 |
0.0448 |
- |
- |
- |
- |
- |
- |
0.4106 |
178 |
0.0161 |
- |
- |
- |
- |
- |
- |
0.4129 |
179 |
0.0427 |
- |
- |
- |
- |
- |
- |
0.4152 |
180 |
0.0229 |
- |
- |
- |
- |
- |
- |
0.4175 |
181 |
0.0274 |
- |
- |
- |
- |
- |
- |
0.4198 |
182 |
0.0173 |
- |
- |
- |
- |
- |
- |
0.4221 |
183 |
0.0123 |
- |
- |
- |
- |
- |
- |
0.4245 |
184 |
0.0395 |
- |
- |
- |
- |
- |
- |
0.4268 |
185 |
0.015 |
- |
- |
- |
- |
- |
- |
0.4291 |
186 |
0.0168 |
- |
- |
- |
- |
- |
- |
0.4314 |
187 |
0.0165 |
- |
- |
- |
- |
- |
- |
0.4337 |
188 |
0.0412 |
- |
- |
- |
- |
- |
- |
0.4360 |
189 |
0.0961 |
- |
- |
- |
- |
- |
- |
0.4383 |
190 |
0.0551 |
- |
- |
- |
- |
- |
- |
0.4406 |
191 |
0.0685 |
- |
- |
- |
- |
- |
- |
0.4429 |
192 |
0.1561 |
- |
- |
- |
- |
- |
- |
0.4452 |
193 |
0.0333 |
- |
- |
- |
- |
- |
- |
0.4475 |
194 |
0.0567 |
- |
- |
- |
- |
- |
- |
0.4498 |
195 |
0.0081 |
- |
- |
- |
- |
- |
- |
0.4521 |
196 |
0.0297 |
- |
- |
- |
- |
- |
- |
0.4544 |
197 |
0.0131 |
- |
- |
- |
- |
- |
- |
0.4567 |
198 |
0.0322 |
- |
- |
- |
- |
- |
- |
0.4591 |
199 |
0.0224 |
- |
- |
- |
- |
- |
- |
0.4614 |
200 |
0.0068 |
0.8989 |
0.8941 |
0.8983 |
0.8985 |
0.8975 |
0.9002 |
0.4637 |
201 |
0.0115 |
- |
- |
- |
- |
- |
- |
0.4660 |
202 |
0.0098 |
- |
- |
- |
- |
- |
- |
0.4683 |
203 |
0.101 |
- |
- |
- |
- |
- |
- |
0.4706 |
204 |
0.0282 |
- |
- |
- |
- |
- |
- |
0.4729 |
205 |
0.0721 |
- |
- |
- |
- |
- |
- |
0.4752 |
206 |
0.0123 |
- |
- |
- |
- |
- |
- |
0.4775 |
207 |
0.1014 |
- |
- |
- |
- |
- |
- |
0.4798 |
208 |
0.0257 |
- |
- |
- |
- |
- |
- |
0.4821 |
209 |
0.1126 |
- |
- |
- |
- |
- |
- |
0.4844 |
210 |
0.0586 |
- |
- |
- |
- |
- |
- |
0.4867 |
211 |
0.0307 |
- |
- |
- |
- |
- |
- |
0.4890 |
212 |
0.0226 |
- |
- |
- |
- |
- |
- |
0.4913 |
213 |
0.0471 |
- |
- |
- |
- |
- |
- |
0.4937 |
214 |
0.025 |
- |
- |
- |
- |
- |
- |
0.4960 |
215 |
0.0799 |
- |
- |
- |
- |
- |
- |
0.4983 |
216 |
0.0173 |
- |
- |
- |
- |
- |
- |
0.5006 |
217 |
0.0208 |
- |
- |
- |
- |
- |
- |
0.5029 |
218 |
0.0461 |
- |
- |
- |
- |
- |
- |
0.5052 |
219 |
0.0592 |
- |
- |
- |
- |
- |
- |
0.5075 |
220 |
0.0076 |
- |
- |
- |
- |
- |
- |
0.5098 |
221 |
0.0156 |
- |
- |
- |
- |
- |
- |
0.5121 |
222 |
0.0149 |
- |
- |
- |
- |
- |
- |
0.5144 |
223 |
0.0138 |
- |
- |
- |
- |
- |
- |
0.5167 |
224 |
0.0526 |
- |
- |
- |
- |
- |
- |
0.5190 |
225 |
0.0689 |
- |
- |
- |
- |
- |
- |
0.5213 |
226 |
0.0191 |
- |
- |
- |
- |
- |
- |
0.5236 |
227 |
0.0094 |
- |
- |
- |
- |
- |
- |
0.5260 |
228 |
0.0125 |
- |
- |
- |
- |
- |
- |
0.5283 |
229 |
0.0632 |
- |
- |
- |
- |
- |
- |
0.5306 |
230 |
0.0773 |
- |
- |
- |
- |
- |
- |
0.5329 |
231 |
0.0147 |
- |
- |
- |
- |
- |
- |
0.5352 |
232 |
0.0145 |
- |
- |
- |
- |
- |
- |
0.5375 |
233 |
0.0068 |
- |
- |
- |
- |
- |
- |
0.5398 |
234 |
0.0673 |
- |
- |
- |
- |
- |
- |
0.5421 |
235 |
0.0131 |
- |
- |
- |
- |
- |
- |
0.5444 |
236 |
0.0217 |
- |
- |
- |
- |
- |
- |
0.5467 |
237 |
0.0126 |
- |
- |
- |
- |
- |
- |
0.5490 |
238 |
0.0172 |
- |
- |
- |
- |
- |
- |
0.5513 |
239 |
0.0122 |
- |
- |
- |
- |
- |
- |
0.5536 |
240 |
0.0175 |
- |
- |
- |
- |
- |
- |
0.5559 |
241 |
0.0184 |
- |
- |
- |
- |
- |
- |
0.5582 |
242 |
0.0422 |
- |
- |
- |
- |
- |
- |
0.5606 |
243 |
0.0106 |
- |
- |
- |
- |
- |
- |
0.5629 |
244 |
0.071 |
- |
- |
- |
- |
- |
- |
0.5652 |
245 |
0.0089 |
- |
- |
- |
- |
- |
- |
0.5675 |
246 |
0.0099 |
- |
- |
- |
- |
- |
- |
0.5698 |
247 |
0.0133 |
- |
- |
- |
- |
- |
- |
0.5721 |
248 |
0.0627 |
- |
- |
- |
- |
- |
- |
0.5744 |
249 |
0.0248 |
- |
- |
- |
- |
- |
- |
0.5767 |
250 |
0.0349 |
0.8970 |
0.8968 |
0.8961 |
0.8961 |
0.8952 |
0.8963 |
0.5790 |
251 |
0.0145 |
- |
- |
- |
- |
- |
- |
0.5813 |
252 |
0.0052 |
- |
- |
- |
- |
- |
- |
0.5836 |
253 |
0.0198 |
- |
- |
- |
- |
- |
- |
0.5859 |
254 |
0.0065 |
- |
- |
- |
- |
- |
- |
0.5882 |
255 |
0.007 |
- |
- |
- |
- |
- |
- |
0.5905 |
256 |
0.0072 |
- |
- |
- |
- |
- |
- |
0.5928 |
257 |
0.1878 |
- |
- |
- |
- |
- |
- |
0.5952 |
258 |
0.0091 |
- |
- |
- |
- |
- |
- |
0.5975 |
259 |
0.0421 |
- |
- |
- |
- |
- |
- |
0.5998 |
260 |
0.0166 |
- |
- |
- |
- |
- |
- |
0.6021 |
261 |
0.0909 |
- |
- |
- |
- |
- |
- |
0.6044 |
262 |
0.0107 |
- |
- |
- |
- |
- |
- |
0.6067 |
263 |
0.0191 |
- |
- |
- |
- |
- |
- |
0.6090 |
264 |
0.0168 |
- |
- |
- |
- |
- |
- |
0.6113 |
265 |
0.0814 |
- |
- |
- |
- |
- |
- |
0.6136 |
266 |
0.0736 |
- |
- |
- |
- |
- |
- |
0.6159 |
267 |
0.0297 |
- |
- |
- |
- |
- |
- |
0.6182 |
268 |
0.016 |
- |
- |
- |
- |
- |
- |
0.6205 |
269 |
0.0201 |
- |
- |
- |
- |
- |
- |
0.6228 |
270 |
0.0111 |
- |
- |
- |
- |
- |
- |
0.6251 |
271 |
0.0164 |
- |
- |
- |
- |
- |
- |
0.6275 |
272 |
0.0106 |
- |
- |
- |
- |
- |
- |
0.6298 |
273 |
0.0287 |
- |
- |
- |
- |
- |
- |
0.6321 |
274 |
0.0595 |
- |
- |
- |
- |
- |
- |
0.6344 |
275 |
0.0446 |
- |
- |
- |
- |
- |
- |
0.6367 |
276 |
0.0203 |
- |
- |
- |
- |
- |
- |
0.6390 |
277 |
0.0079 |
- |
- |
- |
- |
- |
- |
0.6413 |
278 |
0.0345 |
- |
- |
- |
- |
- |
- |
0.6436 |
279 |
0.0461 |
- |
- |
- |
- |
- |
- |
0.6459 |
280 |
0.0803 |
- |
- |
- |
- |
- |
- |
0.6482 |
281 |
0.0218 |
- |
- |
- |
- |
- |
- |
0.6505 |
282 |
0.0288 |
- |
- |
- |
- |
- |
- |
0.6528 |
283 |
0.0745 |
- |
- |
- |
- |
- |
- |
0.6551 |
284 |
0.0102 |
- |
- |
- |
- |
- |
- |
0.6574 |
285 |
0.0626 |
- |
- |
- |
- |
- |
- |
0.6597 |
286 |
0.0606 |
- |
- |
- |
- |
- |
- |
0.6621 |
287 |
0.0319 |
- |
- |
- |
- |
- |
- |
0.6644 |
288 |
0.0303 |
- |
- |
- |
- |
- |
- |
0.6667 |
289 |
0.0216 |
- |
- |
- |
- |
- |
- |
0.6690 |
290 |
0.0417 |
- |
- |
- |
- |
- |
- |
0.6713 |
291 |
0.0061 |
- |
- |
- |
- |
- |
- |
0.6736 |
292 |
0.0386 |
- |
- |
- |
- |
- |
- |
0.6759 |
293 |
0.0117 |
- |
- |
- |
- |
- |
- |
0.6782 |
294 |
0.0283 |
- |
- |
- |
- |
- |
- |
0.6805 |
295 |
0.013 |
- |
- |
- |
- |
- |
- |
0.6828 |
296 |
0.1237 |
- |
- |
- |
- |
- |
- |
0.6851 |
297 |
0.0878 |
- |
- |
- |
- |
- |
- |
0.6874 |
298 |
0.0158 |
- |
- |
- |
- |
- |
- |
0.6897 |
299 |
0.0562 |
- |
- |
- |
- |
- |
- |
0.6920 |
300 |
0.0871 |
0.9022 |
0.9027 |
0.9074 |
0.9055 |
0.8990 |
0.9027 |
0.6943 |
301 |
0.0657 |
- |
- |
- |
- |
- |
- |
0.6967 |
302 |
0.0239 |
- |
- |
- |
- |
- |
- |
0.6990 |
303 |
0.0053 |
- |
- |
- |
- |
- |
- |
0.7013 |
304 |
0.0237 |
- |
- |
- |
- |
- |
- |
0.7036 |
305 |
0.0182 |
- |
- |
- |
- |
- |
- |
0.7059 |
306 |
0.0135 |
- |
- |
- |
- |
- |
- |
0.7082 |
307 |
0.0059 |
- |
- |
- |
- |
- |
- |
0.7105 |
308 |
0.0061 |
- |
- |
- |
- |
- |
- |
0.7128 |
309 |
0.0072 |
- |
- |
- |
- |
- |
- |
0.7151 |
310 |
0.0319 |
- |
- |
- |
- |
- |
- |
0.7174 |
311 |
0.1183 |
- |
- |
- |
- |
- |
- |
0.7197 |
312 |
0.0447 |
- |
- |
- |
- |
- |
- |
0.7220 |
313 |
0.0369 |
- |
- |
- |
- |
- |
- |
0.7243 |
314 |
0.0462 |
- |
- |
- |
- |
- |
- |
0.7266 |
315 |
0.0233 |
- |
- |
- |
- |
- |
- |
0.7290 |
316 |
0.0114 |
- |
- |
- |
- |
- |
- |
0.7313 |
317 |
0.0179 |
- |
- |
- |
- |
- |
- |
0.7336 |
318 |
0.0203 |
- |
- |
- |
- |
- |
- |
0.7359 |
319 |
0.0071 |
- |
- |
- |
- |
- |
- |
0.7382 |
320 |
0.1297 |
- |
- |
- |
- |
- |
- |
0.7405 |
321 |
0.0249 |
- |
- |
- |
- |
- |
- |
0.7428 |
322 |
0.063 |
- |
- |
- |
- |
- |
- |
0.7451 |
323 |
0.0479 |
- |
- |
- |
- |
- |
- |
0.7474 |
324 |
0.1483 |
- |
- |
- |
- |
- |
- |
0.7497 |
325 |
0.0058 |
- |
- |
- |
- |
- |
- |
0.7520 |
326 |
0.0191 |
- |
- |
- |
- |
- |
- |
0.7543 |
327 |
0.0855 |
- |
- |
- |
- |
- |
- |
0.7566 |
328 |
0.0156 |
- |
- |
- |
- |
- |
- |
0.7589 |
329 |
0.0147 |
- |
- |
- |
- |
- |
- |
0.7612 |
330 |
0.0124 |
- |
- |
- |
- |
- |
- |
0.7636 |
331 |
0.0242 |
- |
- |
- |
- |
- |
- |
0.7659 |
332 |
0.0433 |
- |
- |
- |
- |
- |
- |
0.7682 |
333 |
0.0103 |
- |
- |
- |
- |
- |
- |
0.7705 |
334 |
0.0833 |
- |
- |
- |
- |
- |
- |
0.7728 |
335 |
0.0082 |
- |
- |
- |
- |
- |
- |
0.7751 |
336 |
0.0122 |
- |
- |
- |
- |
- |
- |
0.7774 |
337 |
0.031 |
- |
- |
- |
- |
- |
- |
0.7797 |
338 |
0.0116 |
- |
- |
- |
- |
- |
- |
0.7820 |
339 |
0.0947 |
- |
- |
- |
- |
- |
- |
0.7843 |
340 |
0.0323 |
- |
- |
- |
- |
- |
- |
0.7866 |
341 |
0.0177 |
- |
- |
- |
- |
- |
- |
0.7889 |
342 |
0.0487 |
- |
- |
- |
- |
- |
- |
0.7912 |
343 |
0.0123 |
- |
- |
- |
- |
- |
- |
0.7935 |
344 |
0.0075 |
- |
- |
- |
- |
- |
- |
0.7958 |
345 |
0.0061 |
- |
- |
- |
- |
- |
- |
0.7982 |
346 |
0.0057 |
- |
- |
- |
- |
- |
- |
0.8005 |
347 |
0.1108 |
- |
- |
- |
- |
- |
- |
0.8028 |
348 |
0.0104 |
- |
- |
- |
- |
- |
- |
0.8051 |
349 |
0.0131 |
- |
- |
- |
- |
- |
- |
0.8074 |
350 |
0.0229 |
0.9053 |
0.9041 |
0.9033 |
0.9066 |
0.8965 |
0.9052 |
0.8097 |
351 |
0.0478 |
- |
- |
- |
- |
- |
- |
0.8120 |
352 |
0.0127 |
- |
- |
- |
- |
- |
- |
0.8143 |
353 |
0.1143 |
- |
- |
- |
- |
- |
- |
0.8166 |
354 |
0.0365 |
- |
- |
- |
- |
- |
- |
0.8189 |
355 |
0.0418 |
- |
- |
- |
- |
- |
- |
0.8212 |
356 |
0.0494 |
- |
- |
- |
- |
- |
- |
0.8235 |
357 |
0.0082 |
- |
- |
- |
- |
- |
- |
0.8258 |
358 |
0.0212 |
- |
- |
- |
- |
- |
- |
0.8281 |
359 |
0.0106 |
- |
- |
- |
- |
- |
- |
0.8304 |
360 |
0.1009 |
- |
- |
- |
- |
- |
- |
0.8328 |
361 |
0.0316 |
- |
- |
- |
- |
- |
- |
0.8351 |
362 |
0.0313 |
- |
- |
- |
- |
- |
- |
0.8374 |
363 |
0.0108 |
- |
- |
- |
- |
- |
- |
0.8397 |
364 |
0.0198 |
- |
- |
- |
- |
- |
- |
0.8420 |
365 |
0.0112 |
- |
- |
- |
- |
- |
- |
0.8443 |
366 |
0.0197 |
- |
- |
- |
- |
- |
- |
0.8466 |
367 |
0.058 |
- |
- |
- |
- |
- |
- |
0.8489 |
368 |
0.0187 |
- |
- |
- |
- |
- |
- |
0.8512 |
369 |
0.0196 |
- |
- |
- |
- |
- |
- |
0.8535 |
370 |
0.0586 |
- |
- |
- |
- |
- |
- |
0.8558 |
371 |
0.0099 |
- |
- |
- |
- |
- |
- |
0.8581 |
372 |
0.0248 |
- |
- |
- |
- |
- |
- |
0.8604 |
373 |
0.0183 |
- |
- |
- |
- |
- |
- |
0.8627 |
374 |
0.0268 |
- |
- |
- |
- |
- |
- |
0.8651 |
375 |
0.0154 |
- |
- |
- |
- |
- |
- |
0.8674 |
376 |
0.0868 |
- |
- |
- |
- |
- |
- |
0.8697 |
377 |
0.0264 |
- |
- |
- |
- |
- |
- |
0.8720 |
378 |
0.0639 |
- |
- |
- |
- |
- |
- |
0.8743 |
379 |
0.1036 |
- |
- |
- |
- |
- |
- |
0.8766 |
380 |
0.0334 |
- |
- |
- |
- |
- |
- |
0.8789 |
381 |
0.04 |
- |
- |
- |
- |
- |
- |
0.8812 |
382 |
0.0095 |
- |
- |
- |
- |
- |
- |
0.8835 |
383 |
0.0371 |
- |
- |
- |
- |
- |
- |
0.8858 |
384 |
0.0585 |
- |
- |
- |
- |
- |
- |
0.8881 |
385 |
0.0353 |
- |
- |
- |
- |
- |
- |
0.8904 |
386 |
0.0095 |
- |
- |
- |
- |
- |
- |
0.8927 |
387 |
0.0126 |
- |
- |
- |
- |
- |
- |
0.8950 |
388 |
0.0384 |
- |
- |
- |
- |
- |
- |
0.8973 |
389 |
0.018 |
- |
- |
- |
- |
- |
- |
0.8997 |
390 |
0.057 |
- |
- |
- |
- |
- |
- |
0.9020 |
391 |
0.0371 |
- |
- |
- |
- |
- |
- |
0.9043 |
392 |
0.0475 |
- |
- |
- |
- |
- |
- |
0.9066 |
393 |
0.0972 |
- |
- |
- |
- |
- |
- |
0.9089 |
394 |
0.0189 |
- |
- |
- |
- |
- |
- |
0.9112 |
395 |
0.0993 |
- |
- |
- |
- |
- |
- |
0.9135 |
396 |
0.0527 |
- |
- |
- |
- |
- |
- |
0.9158 |
397 |
0.0466 |
- |
- |
- |
- |
- |
- |
0.9181 |
398 |
0.0383 |
- |
- |
- |
- |
- |
- |
0.9204 |
399 |
0.0322 |
- |
- |
- |
- |
- |
- |
0.9227 |
400 |
0.0651 |
0.9077 |
0.9074 |
0.9073 |
0.9077 |
0.9023 |
0.9078 |
0.9250 |
401 |
0.0055 |
- |
- |
- |
- |
- |
- |
0.9273 |
402 |
0.0083 |
- |
- |
- |
- |
- |
- |
0.9296 |
403 |
0.0062 |
- |
- |
- |
- |
- |
- |
0.9319 |
404 |
0.0085 |
- |
- |
- |
- |
- |
- |
0.9343 |
405 |
0.0179 |
- |
- |
- |
- |
- |
- |
0.9366 |
406 |
0.0041 |
- |
- |
- |
- |
- |
- |
0.9389 |
407 |
0.0978 |
- |
- |
- |
- |
- |
- |
0.9412 |
408 |
0.0068 |
- |
- |
- |
- |
- |
- |
0.9435 |
409 |
0.0145 |
- |
- |
- |
- |
- |
- |
0.9458 |
410 |
0.0098 |
- |
- |
- |
- |
- |
- |
0.9481 |
411 |
0.032 |
- |
- |
- |
- |
- |
- |
0.9504 |
412 |
0.0232 |
- |
- |
- |
- |
- |
- |
0.9527 |
413 |
0.0149 |
- |
- |
- |
- |
- |
- |
0.9550 |
414 |
0.0175 |
- |
- |
- |
- |
- |
- |
0.9573 |
415 |
0.0099 |
- |
- |
- |
- |
- |
- |
0.9596 |
416 |
0.0121 |
- |
- |
- |
- |
- |
- |
0.9619 |
417 |
0.108 |
- |
- |
- |
- |
- |
- |
0.9642 |
418 |
0.012 |
- |
- |
- |
- |
- |
- |
0.9666 |
419 |
0.0102 |
- |
- |
- |
- |
- |
- |
0.9689 |
420 |
0.0108 |
- |
- |
- |
- |
- |
- |
0.9712 |
421 |
0.2258 |
- |
- |
- |
- |
- |
- |
0.9735 |
422 |
0.0037 |
- |
- |
- |
- |
- |
- |
0.9758 |
423 |
0.0186 |
- |
- |
- |
- |
- |
- |
0.9781 |
424 |
0.0446 |
- |
- |
- |
- |
- |
- |
0.9804 |
425 |
0.1558 |
- |
- |
- |
- |
- |
- |
0.9827 |
426 |
0.023 |
- |
- |
- |
- |
- |
- |
0.9850 |
427 |
0.0075 |
- |
- |
- |
- |
- |
- |
0.9873 |
428 |
0.0095 |
- |
- |
- |
- |
- |
- |
0.9896 |
429 |
0.0141 |
- |
- |
- |
- |
- |
- |
0.9919 |
430 |
0.0617 |
- |
- |
- |
- |
- |
- |
0.9942 |
431 |
0.0961 |
- |
- |
- |
- |
- |
- |
0.9965 |
432 |
0.0058 |
- |
- |
- |
- |
- |
- |
0.9988 |
433 |
0.0399 |
- |
- |
- |
- |
- |
- |
1.0012 |
434 |
0.0063 |
- |
- |
- |
- |
- |
- |
1.0035 |
435 |
0.0288 |
- |
- |
- |
- |
- |
- |
1.0058 |
436 |
0.0041 |
- |
- |
- |
- |
- |
- |
1.0081 |
437 |
0.0071 |
- |
- |
- |
- |
- |
- |
1.0104 |
438 |
0.0233 |
- |
- |
- |
- |
- |
- |
1.0127 |
439 |
0.0135 |
- |
- |
- |
- |
- |
- |
1.0150 |
440 |
0.1015 |
- |
- |
- |
- |
- |
- |
1.0173 |
441 |
0.0045 |
- |
- |
- |
- |
- |
- |
1.0196 |
442 |
0.0088 |
- |
- |
- |
- |
- |
- |
1.0219 |
443 |
0.0086 |
- |
- |
- |
- |
- |
- |
1.0242 |
444 |
0.0072 |
- |
- |
- |
- |
- |
- |
1.0265 |
445 |
0.0147 |
- |
- |
- |
- |
- |
- |
1.0288 |
446 |
0.025 |
- |
- |
- |
- |
- |
- |
1.0311 |
447 |
0.0067 |
- |
- |
- |
- |
- |
- |
1.0334 |
448 |
0.0066 |
- |
- |
- |
- |
- |
- |
1.0358 |
449 |
0.0062 |
- |
- |
- |
- |
- |
- |
1.0381 |
450 |
0.0068 |
0.9091 |
0.9083 |
0.9045 |
0.9038 |
0.8983 |
0.9072 |
1.0404 |
451 |
0.0126 |
- |
- |
- |
- |
- |
- |
1.0427 |
452 |
0.0082 |
- |
- |
- |
- |
- |
- |
1.0450 |
453 |
0.0034 |
- |
- |
- |
- |
- |
- |
1.0473 |
454 |
0.04 |
- |
- |
- |
- |
- |
- |
1.0496 |
455 |
0.0235 |
- |
- |
- |
- |
- |
- |
1.0519 |
456 |
0.24 |
- |
- |
- |
- |
- |
- |
1.0542 |
457 |
0.0514 |
- |
- |
- |
- |
- |
- |
1.0565 |
458 |
0.0152 |
- |
- |
- |
- |
- |
- |
1.0588 |
459 |
0.0476 |
- |
- |
- |
- |
- |
- |
1.0611 |
460 |
0.0037 |
- |
- |
- |
- |
- |
- |
1.0634 |
461 |
0.0066 |
- |
- |
- |
- |
- |
- |
1.0657 |
462 |
0.0065 |
- |
- |
- |
- |
- |
- |
1.0681 |
463 |
0.0097 |
- |
- |
- |
- |
- |
- |
1.0704 |
464 |
0.0053 |
- |
- |
- |
- |
- |
- |
1.0727 |
465 |
0.0397 |
- |
- |
- |
- |
- |
- |
1.0750 |
466 |
0.0089 |
- |
- |
- |
- |
- |
- |
1.0773 |
467 |
0.0238 |
- |
- |
- |
- |
- |
- |
1.0796 |
468 |
0.0078 |
- |
- |
- |
- |
- |
- |
1.0819 |
469 |
0.0108 |
- |
- |
- |
- |
- |
- |
1.0842 |
470 |
0.0094 |
- |
- |
- |
- |
- |
- |
1.0865 |
471 |
0.0034 |
- |
- |
- |
- |
- |
- |
1.0888 |
472 |
0.0165 |
- |
- |
- |
- |
- |
- |
1.0911 |
473 |
0.0407 |
- |
- |
- |
- |
- |
- |
1.0934 |
474 |
0.0339 |
- |
- |
- |
- |
- |
- |
1.0957 |
475 |
0.0645 |
- |
- |
- |
- |
- |
- |
1.0980 |
476 |
0.0052 |
- |
- |
- |
- |
- |
- |
1.1003 |
477 |
0.0643 |
- |
- |
- |
- |
- |
- |
1.1027 |
478 |
0.0113 |
- |
- |
- |
- |
- |
- |
1.1050 |
479 |
0.007 |
- |
- |
- |
- |
- |
- |
1.1073 |
480 |
0.0062 |
- |
- |
- |
- |
- |
- |
1.1096 |
481 |
0.0232 |
- |
- |
- |
- |
- |
- |
1.1119 |
482 |
0.0374 |
- |
- |
- |
- |
- |
- |
1.1142 |
483 |
0.0582 |
- |
- |
- |
- |
- |
- |
1.1165 |
484 |
0.0396 |
- |
- |
- |
- |
- |
- |
1.1188 |
485 |
0.0041 |
- |
- |
- |
- |
- |
- |
1.1211 |
486 |
0.0064 |
- |
- |
- |
- |
- |
- |
1.1234 |
487 |
0.0248 |
- |
- |
- |
- |
- |
- |
1.1257 |
488 |
0.0052 |
- |
- |
- |
- |
- |
- |
1.1280 |
489 |
0.0095 |
- |
- |
- |
- |
- |
- |
1.1303 |
490 |
0.0681 |
- |
- |
- |
- |
- |
- |
1.1326 |
491 |
0.0082 |
- |
- |
- |
- |
- |
- |
1.1349 |
492 |
0.0279 |
- |
- |
- |
- |
- |
- |
1.1373 |
493 |
0.008 |
- |
- |
- |
- |
- |
- |
1.1396 |
494 |
0.0032 |
- |
- |
- |
- |
- |
- |
1.1419 |
495 |
0.041 |
- |
- |
- |
- |
- |
- |
1.1442 |
496 |
0.0089 |
- |
- |
- |
- |
- |
- |
1.1465 |
497 |
0.0289 |
- |
- |
- |
- |
- |
- |
1.1488 |
498 |
0.0232 |
- |
- |
- |
- |
- |
- |
1.1511 |
499 |
0.059 |
- |
- |
- |
- |
- |
- |
1.1534 |
500 |
0.0053 |
0.9039 |
0.9059 |
0.9032 |
0.9046 |
0.8995 |
0.9050 |
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}