metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:CachedMultipleNegativesRankingLoss
  - loss:AnglELoss
base_model: nomic-ai/nomic-embed-text-v1.5
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: mw-59
    sentences:
      - i9
      - beats headphones
      - tablet stands for 7in
  - source_sentence: totod
    sentences:
      - torxh
      - massage warehouse
      - ferry boats scrub cap
  - source_sentence: 'search_query: ecloth'
    sentences:
      - 'search_query: friend wine stopper'
      - 'search_query: pants for teen girls'
      - 'search_query: 11x14 frame without mat'
  - source_sentence: skull
    sentences:
      - dog kennel
      - duct tape colors
      - mustard tie
  - source_sentence: 'search_query: dab rig'
    sentences:
      - 'search_query: aga stove'
      - 'search_query: jerky slicer machine'
      - 'search_query: womens wallet phone'
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.702
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.3047
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.7038
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.7034
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.7038
            name: Max Accuracy
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.44005266442024726
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.42992442611334314
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.40023272026373946
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.4006937930339286
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.400264197728783
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.4007885190533924
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.443162859807234
            name: Pearson Dot
          - type: spearman_dot
            value: 0.435512515703368
            name: Spearman Dot
          - type: pearson_max
            value: 0.443162859807234
            name: Pearson Max
          - type: spearman_max
            value: 0.435512515703368
            name: Spearman Max

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5 on the triplets and pairs datasets. 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: nomic-ai/nomic-embed-text-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Datasets:
- triplets
- pairs

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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: dab rig',
    'search_query: aga stove',
    'search_query: jerky slicer machine',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.702
dot_accuracy	0.3047
manhattan_accuracy	0.7038
euclidean_accuracy	0.7034
max_accuracy	0.7038

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.4401
spearman_cosine	0.4299
pearson_manhattan	0.4002
spearman_manhattan	0.4007
pearson_euclidean	0.4003
spearman_euclidean	0.4008
pearson_dot	0.4432
spearman_dot	0.4355
pearson_max	0.4432
spearman_max	0.4355

Training Details

Training Datasets

triplets

Dataset: triplets
Size: 261,250 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 11.57 tokens max: 40 tokens	min: 17 tokens mean: 43.83 tokens max: 119 tokens	min: 15 tokens mean: 43.31 tokens max: 112 tokens

Samples:

anchor	positive	negative
`search_query: ear warmers women north face`	`search_document: The North Face Women's Oh-Mega Fur Pom Beanie, TNF Black, OS, The North Face, Tnf Black`	`search_document: The North Face Shinsky Beanie, TNF Light Grey Heather, OS, The North Face, Tnf Light Grey Heather`
`search_query: natural braided hairstyles without weave for black women`	`search_document: Baseball Cap Wig Long Ombre Braids Cap Wig Hat with Synthetic Small Box Braiding Hair for Women Girls(B-53), Yunkang, B-53`	`search_document: K'ryssma Dark Brown Synthetic Wigs for women - Natural Looking Long Wavy Right Side Parting NONE Lace Heat Resistant Replacement Wig Full Machine Made 24 inch (#2), K'ryssma, Dark Brown`
`search_query: boy siracha shirt`	`search_document: Sriracha Distressed Label Graphic T-Shirt, Sriracha, Red`	`search_document: Pho Sho`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

pairs

Dataset: pairs
Size: 261,250 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 6.73 tokens
max: 33 tokens

min: 10 tokens
mean: 40.14 tokens
max: 98 tokens

min: 0.0
mean: 0.77
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 6.73 tokens max: 33 tokens	min: 10 tokens mean: 40.14 tokens max: 98 tokens	min: 0.0 mean: 0.77 max: 1.0

Samples:

sentence1	sentence2	score
`I would choose a medium weight waterproof fabric, hip length jacket or longer, long sleeves, zip front, with a hood and deep pockets with zips`	`ZSHOW Men's Winter Hooded Packable Down Jacket(Blue, XX-Large), ZSHOW, Blue`	`1.0`
`sequin dance costume girls`	`Yeahdor Big Girls' Lyrical Latin Ballet Dance Costumes Dresses Halter Sequins Irregular Tutu Skirted Leotard Dancewear Pink 12-14, Yeahdor, Pink`	`1.0`
`paint easel bulk`	`Artecho Artist Easel Display Easel Stand, 2 Pack Metal Tripod Stand Easel for Painting, Hold Canvas from 21" to 66", Floor and Tabletop Displaying, Painting with Portable Bag, Artecho, Black`	`1.0`

Loss: AnglELoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_angle_sim"
}

Evaluation Datasets

triplets

Dataset: triplets
Size: 10,000 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 11.52 tokens max: 33 tokens	min: 18 tokens mean: 42.45 tokens max: 113 tokens	min: 15 tokens mean: 42.7 tokens max: 116 tokens

Samples:

anchor	positive	negative
`search_query: non damaging eyelash glue`	`search_document: Professional Eyelash Extension Remover Gel - Quickly And Easily Remove Individual Semi Permanent False Lashes - Works With Even The Strongest Fake Eyelash Glue or Adhesive, BEAU LASHES, Clear`	`search_document: Premade Volume Eyelash Extensions 4D-D-0.10-14 Long Stem Premade Fans Eyelash Extensions C D Curl Volume Lash Extensions Pre made Lash Fans(4D-D-0.10, 14mm), B&Qaugen, 4D-0.10-D`
`search_query: christmas tablecloths for rectangle tables 60 x 120 gold`	`search_document: Aquazolax Damask Tablecloth for Rectangle Table 60 x 120 Damask Foliate Pattern Jacquard Heavy Weight Fabric Table Overlay, Gold, Aquazolax, 02 - Gold`	`search_document: Benson Mills Harmony Scroll Woven Damask Fabric Tablecloth (60" X 104" Rectangular, Gold), Benson Mills, Gold`
`search_query: #10 standard no tint no window not self seal`	`search_document: #10 Security Tinted Self-Seal Envelopes - No Window - EnveGuard, Size 4-1/8 X 9-1/2 Inches - White - 24 LB - 100 Count (34100), Aimoh, White`	`search_document: Chalktastic Liquid Chalk Markers for Kids - Set of 8 Washable, Dry Erase Pens for School, Menu Board & Car Window Glass - Neon, Erasable Chalkboard Pen Pack - Gifts for Artists, Chalktastic, Classic`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

pairs

Dataset: pairs
Size: 10,000 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 6.8 tokens
max: 34 tokens

min: 9 tokens
mean: 39.7 tokens
max: 101 tokens

min: 0.0
mean: 0.73
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 6.8 tokens max: 34 tokens	min: 9 tokens mean: 39.7 tokens max: 101 tokens	min: 0.0 mean: 0.73 max: 1.0

Samples:

sentence1	sentence2	score
`outdoor ceiling fans without light`	`44" Plaza Industrial Indoor Outdoor Ceiling Fan with Remote Control Oil Rubbed Bronze Damp Rated for Patio Porch - Casa Vieja, Casa Vieja, No Light Kit - Bronze`	`1.0`
`bathroom cabinet`	`Homfa Bathroom Floor Cabinet Free Standing with Single Door Multifunctional Bathroom Storage Organizer Toiletries(Ivory White), Homfa, White`	`1.0`
`fitbit charge 3`	`TreasureMax Compatible with Fitbit Charge 2 Bands for Women/Men,Silicone Fadeless Pattern Printed Replacement Floral Bands for Fitbit Charge 2 HR Wristbands, TreasureMax, Paw 2`	`0.2`

Loss: AnglELoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_angle_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 1e-05
num_train_epochs: 5
lr_scheduler_type: cosine_with_restarts
warmup_ratio: 0.1
dataloader_drop_last: True
dataloader_num_workers: 4
dataloader_prefetch_factor: 2
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
learning_rate: 1e-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: 5
max_steps: -1
lr_scheduler_type: cosine_with_restarts
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
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
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: True
dataloader_num_workers: 4
dataloader_prefetch_factor: 2
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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	pairs loss	triplets loss	cosine_accuracy	spearman_cosine
0.0031	100	0.9224	-	-	-	-
0.0061	200	0.9823	-	-	-	-
0.0092	300	0.906	-	-	-	-
0.0122	400	0.9692	-	-	-	-
0.0153	500	1.0174	-	-	-	-
0.0184	600	0.9488	-	-	-	-
0.0214	700	0.9094	-	-	-	-
0.0245	800	1.086	-	-	-	-
0.0276	900	0.9104	-	-	-	-
0.0306	1000	0.8288	1.3267	0.7466	0.6776	0.3661
0.0337	1100	0.9905	-	-	-	-
0.0367	1200	0.9511	-	-	-	-
0.0398	1300	0.894	-	-	-	-
0.0429	1400	0.7935	-	-	-	-
0.0459	1500	0.9212	-	-	-	-
0.0490	1600	0.846	-	-	-	-
0.0521	1700	0.9323	-	-	-	-
0.0551	1800	0.8216	-	-	-	-
0.0582	1900	0.7616	-	-	-	-
0.0612	2000	0.8028	1.1716	0.6940	0.6942	0.4072
0.0643	2100	0.8196	-	-	-	-
0.0674	2200	0.8022	-	-	-	-
0.0704	2300	0.814	-	-	-	-
0.0735	2400	0.8388	-	-	-	-
0.0766	2500	0.7658	-	-	-	-
0.0796	2600	0.7226	-	-	-	-
0.0827	2700	0.7802	-	-	-	-
0.0857	2800	0.8148	-	-	-	-
0.0888	2900	0.7444	-	-	-	-
0.0919	3000	0.7463	1.0475	0.6718	0.7019	0.4410
0.0949	3100	0.7129	-	-	-	-
0.0980	3200	0.6884	-	-	-	-
0.1011	3300	0.7072	-	-	-	-
0.1041	3400	0.7956	-	-	-	-
0.1072	3500	0.7932	-	-	-	-
0.1102	3600	0.6843	-	-	-	-
0.1133	3700	0.8722	-	-	-	-
0.1164	3800	0.6767	-	-	-	-
0.1194	3900	0.6905	-	-	-	-
0.1225	4000	0.7022	1.0538	0.6663	0.706	0.4501
0.1256	4100	0.6574	-	-	-	-
0.1286	4200	0.8011	-	-	-	-
0.1317	4300	0.6902	-	-	-	-
0.1347	4400	0.836	-	-	-	-
0.1378	4500	0.6457	-	-	-	-
0.1409	4600	0.6786	-	-	-	-
0.1439	4700	0.7356	-	-	-	-
0.1470	4800	0.8078	-	-	-	-
0.1500	4900	0.7157	-	-	-	-
0.1531	5000	0.6629	1.0507	0.6669	0.7108	0.4493
0.1562	5100	0.7387	-	-	-	-
0.1592	5200	0.7108	-	-	-	-
0.1623	5300	0.6361	-	-	-	-
0.1654	5400	0.6931	-	-	-	-
0.1684	5500	0.7409	-	-	-	-
0.1715	5600	0.7645	-	-	-	-
0.1745	5700	0.6577	-	-	-	-
0.1776	5800	0.7284	-	-	-	-
0.1807	5900	0.6774	-	-	-	-
0.1837	6000	0.7187	1.0089	0.6612	0.7112	0.4569
0.1868	6100	0.6003	-	-	-	-
0.1899	6200	0.7028	-	-	-	-
0.1929	6300	0.7195	-	-	-	-
0.1960	6400	0.6823	-	-	-	-
0.1990	6500	0.6665	-	-	-	-
0.2021	6600	0.6206	-	-	-	-
0.2052	6700	0.6442	-	-	-	-
0.2082	6800	0.7191	-	-	-	-
0.2113	6900	0.6074	-	-	-	-
0.2144	7000	0.6311	1.0315	0.6657	0.7109	0.4451
0.2174	7100	0.6444	-	-	-	-
0.2205	7200	0.6475	-	-	-	-
0.2235	7300	0.5911	-	-	-	-
0.2266	7400	0.6709	-	-	-	-
0.2297	7500	0.6306	-	-	-	-
0.2327	7600	0.7122	-	-	-	-
0.2358	7700	0.6461	-	-	-	-
0.2389	7800	0.6899	-	-	-	-
0.2419	7900	0.6413	-	-	-	-
0.2450	8000	0.691	1.0058	0.6705	0.7158	0.4520
0.2480	8100	0.609	-	-	-	-
0.2511	8200	0.6054	-	-	-	-
0.2542	8300	0.61	-	-	-	-
0.2572	8400	0.596	-	-	-	-
0.2603	8500	0.6999	-	-	-	-
0.2634	8600	0.5909	-	-	-	-
0.2664	8700	0.5965	-	-	-	-
0.2695	8800	0.5951	-	-	-	-
0.2725	8900	0.6058	-	-	-	-
0.2756	9000	0.5979	1.0287	0.6904	0.7142	0.4628
0.2787	9100	0.6249	-	-	-	-
0.2817	9200	0.6261	-	-	-	-
0.2848	9300	0.6365	-	-	-	-
0.2878	9400	0.5699	-	-	-	-
0.2909	9500	0.6675	-	-	-	-
0.2940	9600	0.5806	-	-	-	-
0.2970	9700	0.5832	-	-	-	-
0.3001	9800	0.6135	-	-	-	-
0.3032	9900	0.6005	-	-	-	-
0.3062	10000	0.6079	1.0232	0.7137	0.7163	0.4686
0.3093	10100	0.6452	-	-	-	-
0.3123	10200	0.5765	-	-	-	-
0.3154	10300	0.619	-	-	-	-
0.3185	10400	0.5154	-	-	-	-
0.3215	10500	0.6142	-	-	-	-
0.3246	10600	0.574	-	-	-	-
0.3277	10700	0.5569	-	-	-	-
0.3307	10800	0.6233	-	-	-	-
0.3338	10900	0.6183	-	-	-	-
0.3368	11000	0.5953	1.0279	0.7040	0.71	0.4571
0.3399	11100	0.5146	-	-	-	-
0.3430	11200	0.6029	-	-	-	-
0.3460	11300	0.6054	-	-	-	-
0.3491	11400	0.6324	-	-	-	-
0.3522	11500	0.5459	-	-	-	-
0.3552	11600	0.5721	-	-	-	-
0.3583	11700	0.5224	-	-	-	-
0.3613	11800	0.5979	-	-	-	-
0.3644	11900	0.5832	-	-	-	-
0.3675	12000	0.5638	1.0372	0.7184	0.7127	0.4475
0.3705	12100	0.4945	-	-	-	-
0.3736	12200	0.6368	-	-	-	-
0.3767	12300	0.5071	-	-	-	-
0.3797	12400	0.626	-	-	-	-
0.3828	12500	0.5986	-	-	-	-
0.3858	12600	0.539	-	-	-	-
0.3889	12700	0.5167	-	-	-	-
0.3920	12800	0.657	-	-	-	-
0.3950	12900	0.5264	-	-	-	-
0.3981	13000	0.4996	1.0362	0.7276	0.7162	0.4534
0.4012	13100	0.4991	-	-	-	-
0.4042	13200	0.5725	-	-	-	-
0.4073	13300	0.5788	-	-	-	-
0.4103	13400	0.5293	-	-	-	-
0.4134	13500	0.5192	-	-	-	-
0.4165	13600	0.5477	-	-	-	-
0.4195	13700	0.5151	-	-	-	-
0.4226	13800	0.5121	-	-	-	-
0.4256	13900	0.6849	-	-	-	-
0.4287	14000	0.5508	1.0062	0.7397	0.7103	0.4571
0.4318	14100	0.55	-	-	-	-
0.4348	14200	0.5041	-	-	-	-
0.4379	14300	0.5041	-	-	-	-
0.4410	14400	0.5198	-	-	-	-
0.4440	14500	0.5354	-	-	-	-
0.4471	14600	0.5535	-	-	-	-
0.4501	14700	0.5368	-	-	-	-
0.4532	14800	0.5379	-	-	-	-
0.4563	14900	0.47	-	-	-	-
0.4593	15000	0.5567	1.0441	0.7531	0.715	0.4516
0.4624	15100	0.5157	-	-	-	-
0.4655	15200	0.5698	-	-	-	-
0.4685	15300	0.5436	-	-	-	-
0.4716	15400	0.6344	-	-	-	-
0.4746	15500	0.4351	-	-	-	-
0.4777	15600	0.5286	-	-	-	-
0.4808	15700	0.552	-	-	-	-
0.4838	15800	0.508	-	-	-	-
0.4869	15900	0.5111	-	-	-	-
0.4900	16000	0.5411	1.0264	0.7570	0.7106	0.4506
0.4930	16100	0.5363	-	-	-	-
0.4961	16200	0.5259	-	-	-	-
0.4991	16300	0.5722	-	-	-	-
0.5022	16400	0.5059	-	-	-	-
0.5053	16500	0.5194	-	-	-	-
0.5083	16600	0.5099	-	-	-	-
0.5114	16700	0.4857	-	-	-	-
0.5145	16800	0.4585	-	-	-	-
0.5175	16900	0.5366	-	-	-	-
0.5206	17000	0.4825	1.0299	0.7740	0.7048	0.4504
0.5236	17100	0.543	-	-	-	-
0.5267	17200	0.5022	-	-	-	-
0.5298	17300	0.4399	-	-	-	-
0.5328	17400	0.5342	-	-	-	-
0.5359	17500	0.5064	-	-	-	-
0.5390	17600	0.5978	-	-	-	-
0.5420	17700	0.4947	-	-	-	-
0.5451	17800	0.4974	-	-	-	-
0.5481	17900	0.5555	-	-	-	-
0.5512	18000	0.5397	1.0885	0.7564	0.7143	0.4493
0.5543	18100	0.4415	-	-	-	-
0.5573	18200	0.3887	-	-	-	-
0.5604	18300	0.4956	-	-	-	-
0.5634	18400	0.471	-	-	-	-
0.5665	18500	0.4671	-	-	-	-
0.5696	18600	0.4279	-	-	-	-
0.5726	18700	0.5509	-	-	-	-
0.5757	18800	0.5135	-	-	-	-
0.5788	18900	0.595	-	-	-	-
0.5818	19000	0.4531	1.0569	0.7628	0.708	0.4528
0.5849	19100	0.4926	-	-	-	-
0.5879	19200	0.5718	-	-	-	-
0.5910	19300	0.4963	-	-	-	-
0.5941	19400	0.5222	-	-	-	-
0.5971	19500	0.4079	-	-	-	-
0.6002	19600	0.4662	-	-	-	-
0.6033	19700	0.4838	-	-	-	-
0.6063	19800	0.5238	-	-	-	-
0.6094	19900	0.5475	-	-	-	-
0.6124	20000	0.4	1.0716	0.7965	0.709	0.4691
0.6155	20100	0.5323	-	-	-	-
0.6186	20200	0.4544	-	-	-	-
0.6216	20300	0.4556	-	-	-	-
0.6247	20400	0.5716	-	-	-	-
0.6278	20500	0.5538	-	-	-	-
0.6308	20600	0.4546	-	-	-	-
0.6339	20700	0.4146	-	-	-	-
0.6369	20800	0.4811	-	-	-	-
0.6400	20900	0.4577	-	-	-	-
0.6431	21000	0.4901	1.0721	0.7903	0.7084	0.4594
0.6461	21100	0.4999	-	-	-	-
0.6492	21200	0.3999	-	-	-	-
0.6523	21300	0.4587	-	-	-	-
0.6553	21400	0.4737	-	-	-	-
0.6584	21500	0.4913	-	-	-	-
0.6614	21600	0.4612	-	-	-	-
0.6645	21700	0.432	-	-	-	-
0.6676	21800	0.4627	-	-	-	-
0.6706	21900	0.5023	-	-	-	-
0.6737	22000	0.4486	1.0888	0.7913	0.7056	0.4618
0.6768	22100	0.5068	-	-	-	-
0.6798	22200	0.4843	-	-	-	-
0.6829	22300	0.4687	-	-	-	-
0.6859	22400	0.5123	-	-	-	-
0.6890	22500	0.3802	-	-	-	-
0.6921	22600	0.4883	-	-	-	-
0.6951	22700	0.5069	-	-	-	-
0.6982	22800	0.4859	-	-	-	-
0.7012	22900	0.3931	-	-	-	-
0.7043	23000	0.4675	1.1026	0.8213	0.7051	0.4513
0.7074	23100	0.4948	-	-	-	-
0.7104	23200	0.4561	-	-	-	-
0.7135	23300	0.3874	-	-	-	-
0.7166	23400	0.4909	-	-	-	-
0.7196	23500	0.521	-	-	-	-
0.7227	23600	0.4997	-	-	-	-
0.7257	23700	0.4104	-	-	-	-
0.7288	23800	0.4801	-	-	-	-
0.7319	23900	0.5237	-	-	-	-
0.7349	24000	0.3782	1.0715	0.7962	0.7098	0.4583
0.7380	24100	0.493	-	-	-	-
0.7411	24200	0.489	-	-	-	-
0.7441	24300	0.4797	-	-	-	-
0.7472	24400	0.4636	-	-	-	-
0.7502	24500	0.406	-	-	-	-
0.7533	24600	0.3765	-	-	-	-
0.7564	24700	0.4746	-	-	-	-
0.7594	24800	0.447	-	-	-	-
0.7625	24900	0.5286	-	-	-	-
0.7656	25000	0.4814	1.0794	0.7977	0.7134	0.4614
0.7686	25100	0.505	-	-	-	-
0.7717	25200	0.4508	-	-	-	-
0.7747	25300	0.4317	-	-	-	-
0.7778	25400	0.5088	-	-	-	-
0.7809	25500	0.3931	-	-	-	-
0.7839	25600	0.4516	-	-	-	-
0.7870	25700	0.4394	-	-	-	-
0.7901	25800	0.4825	-	-	-	-
0.7931	25900	0.4248	-	-	-	-
0.7962	26000	0.4215	1.0887	0.8159	0.7065	0.4719
0.7992	26100	0.4674	-	-	-	-
0.8023	26200	0.4634	-	-	-	-
0.8054	26300	0.3975	-	-	-	-
0.8084	26400	0.402	-	-	-	-
0.8115	26500	0.4652	-	-	-	-
0.8146	26600	0.487	-	-	-	-
0.8176	26700	0.4677	-	-	-	-
0.8207	26800	0.4662	-	-	-	-
0.8237	26900	0.4658	-	-	-	-
0.8268	27000	0.4922	1.0792	0.8019	0.7126	0.4544
0.8299	27100	0.4551	-	-	-	-
0.8329	27200	0.4052	-	-	-	-
0.8360	27300	0.3713	-	-	-	-
0.8390	27400	0.4247	-	-	-	-
0.8421	27500	0.4167	-	-	-	-
0.8452	27600	0.4035	-	-	-	-
0.8482	27700	0.5203	-	-	-	-
0.8513	27800	0.4768	-	-	-	-
0.8544	27900	0.4085	-	-	-	-
0.8574	28000	0.3793	1.0920	0.7942	0.7146	0.4630
0.8605	28100	0.4188	-	-	-	-
0.8635	28200	0.4492	-	-	-	-
0.8666	28300	0.4534	-	-	-	-
0.8697	28400	0.4188	-	-	-	-
0.8727	28500	0.5298	-	-	-	-
0.8758	28600	0.4907	-	-	-	-
0.8789	28700	0.4415	-	-	-	-
0.8819	28800	0.4436	-	-	-	-
0.8850	28900	0.4105	-	-	-	-
0.8880	29000	0.5498	1.0937	0.8023	0.7127	0.4492
0.8911	29100	0.4478	-	-	-	-
0.8942	29200	0.4467	-	-	-	-
0.8972	29300	0.3691	-	-	-	-
0.9003	29400	0.358	-	-	-	-
0.9034	29500	0.4101	-	-	-	-
0.9064	29600	0.4568	-	-	-	-
0.9095	29700	0.4776	-	-	-	-
0.9125	29800	0.3909	-	-	-	-
0.9156	29900	0.4731	-	-	-	-
0.9187	30000	0.4407	1.1511	0.8187	0.7131	0.4423
0.9217	30100	0.5712	-	-	-	-
0.9248	30200	0.457	-	-	-	-
0.9279	30300	0.4141	-	-	-	-
0.9309	30400	0.4779	-	-	-	-
0.9340	30500	0.418	-	-	-	-
0.9370	30600	0.4377	-	-	-	-
0.9401	30700	0.3997	-	-	-	-
0.9432	30800	0.3443	-	-	-	-
0.9462	30900	0.5006	-	-	-	-
0.9493	31000	0.4728	1.1302	0.8141	0.7137	0.4555
0.9524	31100	0.5103	-	-	-	-
0.9554	31200	0.3898	-	-	-	-
0.9585	31300	0.4132	-	-	-	-
0.9615	31400	0.4567	-	-	-	-
0.9646	31500	0.4226	-	-	-	-
0.9677	31600	0.3669	-	-	-	-
0.9707	31700	0.4707	-	-	-	-
0.9738	31800	0.5012	-	-	-	-
0.9768	31900	0.4114	-	-	-	-
0.9799	32000	0.3666	1.1309	0.8225	0.7102	0.4632
0.9830	32100	0.4514	-	-	-	-
0.9860	32200	0.4329	-	-	-	-
0.9891	32300	0.4559	-	-	-	-
0.9922	32400	0.412	-	-	-	-
0.9952	32500	0.3883	-	-	-	-
0.9983	32600	0.3854	-	-	-	-
1.0013	32700	0.3886	-	-	-	-
1.0044	32800	0.41	-	-	-	-
1.0075	32900	0.4494	-	-	-	-
1.0105	33000	0.4862	1.1124	0.8362	0.7079	0.4494
1.0136	33100	0.3951	-	-	-	-
1.0167	33200	0.4714	-	-	-	-
1.0197	33300	0.4037	-	-	-	-
1.0228	33400	0.4534	-	-	-	-
1.0258	33500	0.5265	-	-	-	-
1.0289	33600	0.4432	-	-	-	-
1.0320	33700	0.3665	-	-	-	-
1.0350	33800	0.4235	-	-	-	-
1.0381	33900	0.3905	-	-	-	-
1.0412	34000	0.3532	1.1693	0.8203	0.7142	0.4420
1.0442	34100	0.3472	-	-	-	-
1.0473	34200	0.4316	-	-	-	-
1.0503	34300	0.3811	-	-	-	-
1.0534	34400	0.4753	-	-	-	-
1.0565	34500	0.3757	-	-	-	-
1.0595	34600	0.417	-	-	-	-
1.0626	34700	0.3727	-	-	-	-
1.0657	34800	0.4127	-	-	-	-
1.0687	34900	0.4487	-	-	-	-
1.0718	35000	0.3786	1.1073	0.8310	0.7159	0.4678
1.0748	35100	0.4043	-	-	-	-
1.0779	35200	0.4226	-	-	-	-
1.0810	35300	0.3585	-	-	-	-
1.0840	35400	0.407	-	-	-	-
1.0871	35500	0.4682	-	-	-	-
1.0902	35600	0.3273	-	-	-	-
1.0932	35700	0.3594	-	-	-	-
1.0963	35800	0.3795	-	-	-	-
1.0993	35900	0.3633	-	-	-	-
1.1024	36000	0.3729	1.1356	0.8248	0.7169	0.4525
1.1055	36100	0.4179	-	-	-	-
1.1085	36200	0.3907	-	-	-	-
1.1116	36300	0.4495	-	-	-	-
1.1146	36400	0.4093	-	-	-	-
1.1177	36500	0.327	-	-	-	-
1.1208	36600	0.2868	-	-	-	-
1.1238	36700	0.2917	-	-	-	-
1.1269	36800	0.3753	-	-	-	-
1.1300	36900	0.3508	-	-	-	-
1.1330	37000	0.4483	1.1865	0.8488	0.7035	0.4559
1.1361	37100	0.4439	-	-	-	-
1.1391	37200	0.3225	-	-	-	-
1.1422	37300	0.401	-	-	-	-
1.1453	37400	0.3858	-	-	-	-
1.1483	37500	0.4877	-	-	-	-
1.1514	37600	0.3456	-	-	-	-
1.1545	37700	0.3827	-	-	-	-
1.1575	37800	0.4412	-	-	-	-
1.1606	37900	0.3679	-	-	-	-
1.1636	38000	0.3465	1.1654	0.8383	0.7095	0.4498
1.1667	38100	0.3433	-	-	-	-
1.1698	38200	0.3745	-	-	-	-
1.1728	38300	0.3902	-	-	-	-
1.1759	38400	0.2779	-	-	-	-
1.1790	38500	0.3916	-	-	-	-
1.1820	38600	0.346	-	-	-	-
1.1851	38700	0.3742	-	-	-	-
1.1881	38800	0.3424	-	-	-	-
1.1912	38900	0.4042	-	-	-	-
1.1943	39000	0.2993	1.2051	0.8313	0.7106	0.4571
1.1973	39100	0.3167	-	-	-	-
1.2004	39200	0.3291	-	-	-	-
1.2035	39300	0.245	-	-	-	-
1.2065	39400	0.3289	-	-	-	-
1.2096	39500	0.3969	-	-	-	-
1.2126	39600	0.2511	-	-	-	-
1.2157	39700	0.2972	-	-	-	-
1.2188	39800	0.3434	-	-	-	-
1.2218	39900	0.324	-	-	-	-
1.2249	40000	0.2837	1.2372	0.8453	0.7121	0.4562
1.2280	40100	0.2727	-	-	-	-
1.2310	40200	0.3327	-	-	-	-
1.2341	40300	0.3468	-	-	-	-
1.2371	40400	0.3029	-	-	-	-
1.2402	40500	0.3583	-	-	-	-
1.2433	40600	0.3664	-	-	-	-
1.2463	40700	0.2661	-	-	-	-
1.2494	40800	0.2768	-	-	-	-
1.2524	40900	0.3065	-	-	-	-
1.2555	41000	0.309	1.2609	0.8644	0.704	0.4467
1.2586	41100	0.377	-	-	-	-
1.2616	41200	0.3031	-	-	-	-
1.2647	41300	0.2317	-	-	-	-
1.2678	41400	0.2504	-	-	-	-
1.2708	41500	0.2546	-	-	-	-
1.2739	41600	0.2859	-	-	-	-
1.2769	41700	0.3507	-	-	-	-
1.2800	41800	0.2578	-	-	-	-
1.2831	41900	0.297	-	-	-	-
1.2861	42000	0.3016	1.2546	0.8479	0.7115	0.4578
1.2892	42100	0.2067	-	-	-	-
1.2923	42200	0.3729	-	-	-	-
1.2953	42300	0.2365	-	-	-	-
1.2984	42400	0.2855	-	-	-	-
1.3014	42500	0.2272	-	-	-	-
1.3045	42600	0.2688	-	-	-	-
1.3076	42700	0.2285	-	-	-	-
1.3106	42800	0.2615	-	-	-	-
1.3137	42900	0.2599	-	-	-	-
1.3168	43000	0.2968	1.2860	0.8800	0.7071	0.4599
1.3198	43100	0.2464	-	-	-	-
1.3229	43200	0.2673	-	-	-	-
1.3259	43300	0.2108	-	-	-	-
1.3290	43400	0.2353	-	-	-	-
1.3321	43500	0.2396	-	-	-	-
1.3351	43600	0.237	-	-	-	-
1.3382	43700	0.2083	-	-	-	-
1.3413	43800	0.2638	-	-	-	-
1.3443	43900	0.2888	-	-	-	-
1.3474	44000	0.3166	1.2710	0.8729	0.7052	0.4501
1.3504	44100	0.1949	-	-	-	-
1.3535	44200	0.2285	-	-	-	-
1.3566	44300	0.1923	-	-	-	-
1.3596	44400	0.1875	-	-	-	-
1.3627	44500	0.2736	-	-	-	-
1.3658	44600	0.2154	-	-	-	-
1.3688	44700	0.1975	-	-	-	-
1.3719	44800	0.1799	-	-	-	-
1.3749	44900	0.2417	-	-	-	-
1.3780	45000	0.3224	1.3032	0.8788	0.7072	0.4521
1.3811	45100	0.2433	-	-	-	-
1.3841	45200	0.269	-	-	-	-
1.3872	45300	0.2034	-	-	-	-
1.3902	45400	0.236	-	-	-	-
1.3933	45500	0.2599	-	-	-	-
1.3964	45600	0.1798	-	-	-	-
1.3994	45700	0.1412	-	-	-	-
1.4025	45800	0.215	-	-	-	-
1.4056	45900	0.2081	-	-	-	-
1.4086	46000	0.2277	1.2555	0.8621	0.7075	0.4577
1.4117	46100	0.2005	-	-	-	-
1.4147	46200	0.2051	-	-	-	-
1.4178	46300	0.1588	-	-	-	-
1.4209	46400	0.2318	-	-	-	-
1.4239	46500	0.205	-	-	-	-
1.4270	46600	0.2404	-	-	-	-
1.4301	46700	0.2167	-	-	-	-
1.4331	46800	0.1729	-	-	-	-
1.4362	46900	0.1866	-	-	-	-
1.4392	47000	0.2168	1.3006	0.8624	0.7094	0.4562
1.4423	47100	0.1615	-	-	-	-
1.4454	47200	0.2104	-	-	-	-
1.4484	47300	0.2051	-	-	-	-
1.4515	47400	0.1904	-	-	-	-
1.4546	47500	0.1773	-	-	-	-
1.4576	47600	0.1494	-	-	-	-
1.4607	47700	0.1668	-	-	-	-
1.4637	47800	0.1527	-	-	-	-
1.4668	47900	0.1724	-	-	-	-
1.4699	48000	0.1707	1.3098	0.8911	0.7093	0.4421
1.4729	48100	0.2147	-	-	-	-
1.4760	48200	0.1513	-	-	-	-
1.4791	48300	0.2049	-	-	-	-
1.4821	48400	0.171	-	-	-	-
1.4852	48500	0.1283	-	-	-	-
1.4882	48600	0.1768	-	-	-	-
1.4913	48700	0.172	-	-	-	-
1.4944	48800	0.2131	-	-	-	-
1.4974	48900	0.1621	-	-	-	-
1.5005	49000	0.1941	1.3623	0.8859	0.7091	0.4501
1.5036	49100	0.1493	-	-	-	-
1.5066	49200	0.1544	-	-	-	-
1.5097	49300	0.1524	-	-	-	-
1.5127	49400	0.1137	-	-	-	-
1.5158	49500	0.1611	-	-	-	-
1.5189	49600	0.1396	-	-	-	-
1.5219	49700	0.1462	-	-	-	-
1.5250	49800	0.1261	-	-	-	-
1.5280	49900	0.122	-	-	-	-
1.5311	50000	0.1478	1.3027	0.8766	0.7093	0.4555
1.5342	50100	0.1324	-	-	-	-
1.5372	50200	0.1468	-	-	-	-
1.5403	50300	0.1795	-	-	-	-
1.5434	50400	0.1308	-	-	-	-
1.5464	50500	0.1796	-	-	-	-
1.5495	50600	0.2207	-	-	-	-
1.5525	50700	0.1383	-	-	-	-
1.5556	50800	0.0884	-	-	-	-
1.5587	50900	0.1208	-	-	-	-
1.5617	51000	0.1139	1.4073	0.9156	0.7061	0.4502
1.5648	51100	0.169	-	-	-	-
1.5679	51200	0.1142	-	-	-	-
1.5709	51300	0.1269	-	-	-	-
1.5740	51400	0.1664	-	-	-	-
1.5770	51500	0.1191	-	-	-	-
1.5801	51600	0.2078	-	-	-	-
1.5832	51700	0.1045	-	-	-	-
1.5862	51800	0.1564	-	-	-	-
1.5893	51900	0.219	-	-	-	-
1.5924	52000	0.1308	1.3284	0.9085	0.7003	0.4439
1.5954	52100	0.1002	-	-	-	-
1.5985	52200	0.1133	-	-	-	-
1.6015	52300	0.1612	-	-	-	-
1.6046	52400	0.1216	-	-	-	-
1.6077	52500	0.1767	-	-	-	-
1.6107	52600	0.1198	-	-	-	-
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1.6169	52800	0.1505	-	-	-	-
1.6199	52900	0.1503	-	-	-	-
1.6230	53000	0.161	1.3557	0.9038	0.7073	0.4517
1.6260	53100	0.1799	-	-	-	-
1.6291	53200	0.1794	-	-	-	-
1.6322	53300	0.1527	-	-	-	-
1.6352	53400	0.1093	-	-	-	-
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1.6475	53800	0.1083	-	-	-	-
1.6505	53900	0.0896	-	-	-	-
1.6536	54000	0.1524	1.4412	0.9069	0.7047	0.4428
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1.6597	54200	0.1643	-	-	-	-
1.6628	54300	0.0891	-	-	-	-
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1.6750	54700	0.112	-	-	-	-
1.6781	54800	0.1932	-	-	-	-
1.6812	54900	0.1298	-	-	-	-
1.6842	55000	0.1509	1.3844	0.8949	0.7094	0.4458
1.6873	55100	0.113	-	-	-	-
1.6903	55200	0.1516	-	-	-	-
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1.7087	55800	0.0908	-	-	-	-
1.7118	55900	0.1311	-	-	-	-
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1.7210	56200	0.1709	-	-	-	-
1.7240	56300	0.102	-	-	-	-
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1.7302	56500	0.1339	-	-	-	-
1.7332	56600	0.1961	-	-	-	-
1.7363	56700	0.1549	-	-	-	-
1.7393	56800	0.1545	-	-	-	-
1.7424	56900	0.1175	-	-	-	-
1.7455	57000	0.1447	1.4055	0.9385	0.7006	0.4433
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1.7516	57200	0.0765	-	-	-	-
1.7547	57300	0.1444	-	-	-	-
1.7577	57400	0.1617	-	-	-	-
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1.7700	57800	0.1381	-	-	-	-
1.7730	57900	0.132	-	-	-	-
1.7761	58000	0.1373	1.4008	0.9141	0.7088	0.4456
1.7792	58100	0.1018	-	-	-	-
1.7822	58200	0.0882	-	-	-	-
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1.7883	58400	0.1111	-	-	-	-
1.7914	58500	0.0985	-	-	-	-
1.7945	58600	0.1063	-	-	-	-
1.7975	58700	0.0696	-	-	-	-
1.8006	58800	0.113	-	-	-	-
1.8037	58900	0.1048	-	-	-	-
1.8067	59000	0.1305	1.4202	0.9253	0.7046	0.4450
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1.8128	59200	0.0975	-	-	-	-
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1.8220	59500	0.1438	-	-	-	-
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1.8312	59800	0.1726	-	-	-	-
1.8343	59900	0.1268	-	-	-	-
1.8373	60000	0.0755	1.4523	0.9355	0.7059	0.4424
1.8404	60100	0.1033	-	-	-	-
1.8435	60200	0.1231	-	-	-	-
1.8465	60300	0.1272	-	-	-	-
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1.8526	60500	0.1144	-	-	-	-
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1.8588	60700	0.1266	-	-	-	-
1.8618	60800	0.0837	-	-	-	-
1.8649	60900	0.1247	-	-	-	-
1.8680	61000	0.1297	1.4443	0.9315	0.7037	0.4498
1.8710	61100	0.1014	-	-	-	-
1.8741	61200	0.127	-	-	-	-
1.8771	61300	0.128	-	-	-	-
1.8802	61400	0.1021	-	-	-	-
1.8833	61500	0.1625	-	-	-	-
1.8863	61600	0.1177	-	-	-	-
1.8894	61700	0.1241	-	-	-	-
1.8925	61800	0.1289	-	-	-	-
1.8955	61900	0.1144	-	-	-	-
1.8986	62000	0.0968	1.4650	0.9320	0.7012	0.4421
1.9016	62100	0.0951	-	-	-	-
1.9047	62200	0.1262	-	-	-	-
1.9078	62300	0.1387	-	-	-	-
1.9108	62400	0.129	-	-	-	-
1.9139	62500	0.088	-	-	-	-
1.9170	62600	0.1166	-	-	-	-
1.9200	62700	0.1536	-	-	-	-
1.9231	62800	0.1216	-	-	-	-
1.9261	62900	0.1326	-	-	-	-
1.9292	63000	0.1014	1.4315	0.9462	0.6982	0.4377
1.9323	63100	0.1152	-	-	-	-
1.9353	63200	0.0821	-	-	-	-
1.9384	63300	0.1374	-	-	-	-
1.9415	63400	0.0827	-	-	-	-
1.9445	63500	0.1104	-	-	-	-
1.9476	63600	0.1578	-	-	-	-
1.9506	63700	0.1232	-	-	-	-
1.9537	63800	0.1482	-	-	-	-
1.9568	63900	0.1156	-	-	-	-
1.9598	64000	0.1177	1.4263	0.9434	0.706	0.4420
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1.9659	64200	0.1385	-	-	-	-
1.9690	64300	0.1083	-	-	-	-
1.9721	64400	0.1138	-	-	-	-
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1.9782	64600	0.0786	-	-	-	-
1.9813	64700	0.1043	-	-	-	-
1.9843	64800	0.1112	-	-	-	-
1.9874	64900	0.1237	-	-	-	-
1.9904	65000	0.1073	1.3901	0.9587	0.7002	0.4438
1.9935	65100	0.1174	-	-	-	-
1.9966	65200	0.1091	-	-	-	-
1.9996	65300	0.1143	-	-	-	-
2.0027	65400	0.1044	-	-	-	-
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2.0119	65700	0.1299	-	-	-	-
2.0149	65800	0.1017	-	-	-	-
2.0180	65900	0.124	-	-	-	-
2.0211	66000	0.1062	1.4390	0.9290	0.704	0.4440
2.0241	66100	0.1634	-	-	-	-
2.0272	66200	0.1149	-	-	-	-
2.0303	66300	0.0682	-	-	-	-
2.0333	66400	0.1386	-	-	-	-
2.0364	66500	0.0861	-	-	-	-
2.0394	66600	0.0669	-	-	-	-
2.0425	66700	0.0944	-	-	-	-
2.0456	66800	0.1332	-	-	-	-
2.0486	66900	0.0884	-	-	-	-
2.0517	67000	0.122	1.5088	0.9513	0.7063	0.4492
2.0548	67100	0.0934	-	-	-	-
2.0578	67200	0.102	-	-	-	-
2.0609	67300	0.1402	-	-	-	-
2.0639	67400	0.1394	-	-	-	-
2.0670	67500	0.1067	-	-	-	-
2.0701	67600	0.1052	-	-	-	-
2.0731	67700	0.1267	-	-	-	-
2.0762	67800	0.1048	-	-	-	-
2.0793	67900	0.0962	-	-	-	-
2.0823	68000	0.0929	1.4530	0.9247	0.7109	0.4491
2.0854	68100	0.1298	-	-	-	-
2.0884	68200	0.1332	-	-	-	-
2.0915	68300	0.0913	-	-	-	-
2.0946	68400	0.0843	-	-	-	-
2.0976	68500	0.0846	-	-	-	-
2.1007	68600	0.1142	-	-	-	-
2.1037	68700	0.1403	-	-	-	-
2.1068	68800	0.0961	-	-	-	-
2.1099	68900	0.0984	-	-	-	-
2.1129	69000	0.1509	1.4343	0.9552	0.7039	0.4382
2.1160	69100	0.0947	-	-	-	-
2.1191	69200	0.0877	-	-	-	-
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2.1282	69500	0.0765	-	-	-	-
2.1313	69600	0.0632	-	-	-	-
2.1344	69700	0.1792	-	-	-	-
2.1374	69800	0.0666	-	-	-	-
2.1405	69900	0.1225	-	-	-	-
2.1436	70000	0.0922	1.4291	0.9393	0.7053	0.4357
2.1466	70100	0.126	-	-	-	-
2.1497	70200	0.0991	-	-	-	-
2.1527	70300	0.0759	-	-	-	-
2.1558	70400	0.1024	-	-	-	-
2.1589	70500	0.0894	-	-	-	-
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2.1650	70700	0.1084	-	-	-	-
2.1681	70800	0.1013	-	-	-	-
2.1711	70900	0.111	-	-	-	-
2.1742	71000	0.0965	1.3915	0.9477	0.7052	0.4437
2.1772	71100	0.0837	-	-	-	-
2.1803	71200	0.0347	-	-	-	-
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2.1864	71400	0.0799	-	-	-	-
2.1895	71500	0.1173	-	-	-	-
2.1926	71600	0.0964	-	-	-	-
2.1956	71700	0.1036	-	-	-	-
2.1987	71800	0.0952	-	-	-	-
2.2017	71900	0.0752	-	-	-	-
2.2048	72000	0.0824	1.4657	0.9593	0.7011	0.4397
2.2079	72100	0.1081	-	-	-	-
2.2109	72200	0.0718	-	-	-	-
2.2140	72300	0.0644	-	-	-	-
2.2171	72400	0.0919	-	-	-	-
2.2201	72500	0.1099	-	-	-	-
2.2232	72600	0.072	-	-	-	-
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2.2293	72800	0.0568	-	-	-	-
2.2324	72900	0.0664	-	-	-	-
2.2354	73000	0.0926	1.4526	0.9607	0.701	0.4383
2.2385	73100	0.1089	-	-	-	-
2.2415	73200	0.1208	-	-	-	-
2.2446	73300	0.0583	-	-	-	-
2.2477	73400	0.0546	-	-	-	-
2.2507	73500	0.086	-	-	-	-
2.2538	73600	0.1029	-	-	-	-
2.2569	73700	0.0803	-	-	-	-
2.2599	73800	0.114	-	-	-	-
2.2630	73900	0.0542	-	-	-	-
2.2660	74000	0.0732	1.4112	0.9411	0.7028	0.4454
2.2691	74100	0.0641	-	-	-	-
2.2722	74200	0.072	-	-	-	-
2.2752	74300	0.0806	-	-	-	-
2.2783	74400	0.0845	-	-	-	-
2.2814	74500	0.0599	-	-	-	-
2.2844	74600	0.069	-	-	-	-
2.2875	74700	0.0808	-	-	-	-
2.2905	74800	0.0903	-	-	-	-
2.2936	74900	0.0693	-	-	-	-
2.2967	75000	0.074	1.4487	0.9686	0.6945	0.4495
2.2997	75100	0.1261	-	-	-	-
2.3028	75200	0.055	-	-	-	-
2.3059	75300	0.0828	-	-	-	-
2.3089	75400	0.0735	-	-	-	-
2.3120	75500	0.0539	-	-	-	-
2.3150	75600	0.0763	-	-	-	-
2.3181	75700	0.0598	-	-	-	-
2.3212	75800	0.0782	-	-	-	-
2.3242	75900	0.0681	-	-	-	-
2.3273	76000	0.0497	1.4493	0.9589	0.6972	0.4354
2.3304	76100	0.0353	-	-	-	-
2.3334	76200	0.0669	-	-	-	-
2.3365	76300	0.064	-	-	-	-
2.3395	76400	0.0814	-	-	-	-
2.3426	76500	0.0786	-	-	-	-
2.3457	76600	0.091	-	-	-	-
2.3487	76700	0.0861	-	-	-	-
2.3518	76800	0.0445	-	-	-	-
2.3549	76900	0.0589	-	-	-	-
2.3579	77000	0.0318	1.4455	0.9647	0.7012	0.4390
2.3610	77100	0.0425	-	-	-	-
2.3640	77200	0.0605	-	-	-	-
2.3671	77300	0.0523	-	-	-	-
2.3702	77400	0.0715	-	-	-	-
2.3732	77500	0.0756	-	-	-	-
2.3763	77600	0.0911	-	-	-	-
2.3793	77700	0.1023	-	-	-	-
2.3824	77800	0.0538	-	-	-	-
2.3855	77900	0.0571	-	-	-	-
2.3885	78000	0.0505	1.4434	0.9554	0.7048	0.4411
2.3916	78100	0.1114	-	-	-	-
2.3947	78200	0.0368	-	-	-	-
2.3977	78300	0.0636	-	-	-	-
2.4008	78400	0.0419	-	-	-	-
2.4038	78500	0.0691	-	-	-	-
2.4069	78600	0.0814	-	-	-	-
2.4100	78700	0.0644	-	-	-	-
2.4130	78800	0.0584	-	-	-	-
2.4161	78900	0.0745	-	-	-	-
2.4192	79000	0.0558	1.4218	0.9631	0.7024	0.4399
2.4222	79100	0.0478	-	-	-	-
2.4253	79200	0.1116	-	-	-	-
2.4283	79300	0.0487	-	-	-	-
2.4314	79400	0.0457	-	-	-	-
2.4345	79500	0.0441	-	-	-	-
2.4375	79600	0.037	-	-	-	-
2.4406	79700	0.0382	-	-	-	-
2.4437	79800	0.0453	-	-	-	-
2.4467	79900	0.0625	-	-	-	-
2.4498	80000	0.0649	1.4950	0.9400	0.7053	0.4516
2.4528	80100	0.0417	-	-	-	-
2.4559	80200	0.03	-	-	-	-
2.4590	80300	0.0281	-	-	-	-
2.4620	80400	0.0637	-	-	-	-
2.4651	80500	0.0415	-	-	-	-
2.4682	80600	0.048	-	-	-	-
2.4712	80700	0.0653	-	-	-	-
2.4743	80800	0.0382	-	-	-	-
2.4773	80900	0.0524	-	-	-	-
2.4804	81000	0.0699	1.4317	0.9833	0.7024	0.4487
2.4835	81100	0.0728	-	-	-	-
2.4865	81200	0.0346	-	-	-	-
2.4896	81300	0.0448	-	-	-	-
2.4927	81400	0.0425	-	-	-	-
2.4957	81500	0.0941	-	-	-	-
2.4988	81600	0.0385	-	-	-	-
2.5018	81700	0.0802	-	-	-	-
2.5049	81800	0.033	-	-	-	-
2.5080	81900	0.0653	-	-	-	-
2.5110	82000	0.0435	1.4630	0.9643	0.7006	0.4467
2.5141	82100	0.0301	-	-	-	-
2.5171	82200	0.0392	-	-	-	-
2.5202	82300	0.0402	-	-	-	-
2.5233	82400	0.0584	-	-	-	-
2.5263	82500	0.0334	-	-	-	-
2.5294	82600	0.0409	-	-	-	-
2.5325	82700	0.0587	-	-	-	-
2.5355	82800	0.0412	-	-	-	-
2.5386	82900	0.0477	-	-	-	-
2.5416	83000	0.0485	1.4052	0.9665	0.7021	0.4382
2.5447	83100	0.0501	-	-	-	-
2.5478	83200	0.0492	-	-	-	-
2.5508	83300	0.0829	-	-	-	-
2.5539	83400	0.0571	-	-	-	-
2.5570	83500	0.0353	-	-	-	-
2.5600	83600	0.0439	-	-	-	-
2.5631	83700	0.0264	-	-	-	-
2.5661	83800	0.0743	-	-	-	-
2.5692	83900	0.0467	-	-	-	-
2.5723	84000	0.0442	1.4663	0.9854	0.7022	0.4380
2.5753	84100	0.0461	-	-	-	-
2.5784	84200	0.0542	-	-	-	-
2.5815	84300	0.0842	-	-	-	-
2.5845	84400	0.0361	-	-	-	-
2.5876	84500	0.0577	-	-	-	-
2.5906	84600	0.0345	-	-	-	-
2.5937	84700	0.0296	-	-	-	-
2.5968	84800	0.025	-	-	-	-
2.5998	84900	0.0575	-	-	-	-
2.6029	85000	0.0567	1.5179	0.9760	0.7008	0.4469
2.6060	85100	0.0528	-	-	-	-
2.6090	85200	0.0437	-	-	-	-
2.6121	85300	0.0291	-	-	-	-
2.6151	85400	0.0689	-	-	-	-
2.6182	85500	0.0328	-	-	-	-
2.6213	85600	0.0473	-	-	-	-
2.6243	85700	0.0682	-	-	-	-
2.6274	85800	0.0544	-	-	-	-
2.6305	85900	0.0641	-	-	-	-
2.6335	86000	0.029	1.4558	0.9576	0.7072	0.4448
2.6366	86100	0.0364	-	-	-	-
2.6396	86200	0.0365	-	-	-	-
2.6427	86300	0.0514	-	-	-	-
2.6458	86400	0.0417	-	-	-	-
2.6488	86500	0.0309	-	-	-	-
2.6519	86600	0.035	-	-	-	-
2.6549	86700	0.044	-	-	-	-
2.6580	86800	0.0694	-	-	-	-
2.6611	86900	0.0194	-	-	-	-
2.6641	87000	0.0373	1.5556	0.9819	0.7052	0.4339
2.6672	87100	0.0349	-	-	-	-
2.6703	87200	0.0561	-	-	-	-
2.6733	87300	0.0487	-	-	-	-
2.6764	87400	0.0722	-	-	-	-
2.6794	87500	0.0501	-	-	-	-
2.6825	87600	0.0404	-	-	-	-
2.6856	87700	0.0533	-	-	-	-
2.6886	87800	0.0371	-	-	-	-
2.6917	87900	0.0585	-	-	-	-
2.6948	88000	0.0482	1.4305	0.9550	0.7053	0.4397
2.6978	88100	0.0731	-	-	-	-
2.7009	88200	0.0312	-	-	-	-
2.7039	88300	0.0339	-	-	-	-
2.7070	88400	0.0348	-	-	-	-
2.7101	88500	0.0509	-	-	-	-
2.7131	88600	0.0343	-	-	-	-
2.7162	88700	0.0282	-	-	-	-
2.7193	88800	0.0518	-	-	-	-
2.7223	88900	0.0569	-	-	-	-
2.7254	89000	0.0427	1.4783	0.9810	0.7066	0.4263
2.7284	89100	0.0554	-	-	-	-
2.7315	89200	0.0368	-	-	-	-
2.7346	89300	0.0301	-	-	-	-
2.7376	89400	0.0469	-	-	-	-
2.7407	89500	0.0479	-	-	-	-
2.7438	89600	0.0586	-	-	-	-
2.7468	89700	0.0687	-	-	-	-
2.7499	89800	0.0427	-	-	-	-
2.7529	89900	0.0551	-	-	-	-
2.7560	90000	0.0255	1.4759	0.9758	0.7011	0.4434
2.7591	90100	0.0348	-	-	-	-
2.7621	90200	0.0536	-	-	-	-
2.7652	90300	0.0554	-	-	-	-
2.7683	90400	0.0367	-	-	-	-
2.7713	90500	0.0185	-	-	-	-
2.7744	90600	0.0498	-	-	-	-
2.7774	90700	0.0296	-	-	-	-
2.7805	90800	0.0132	-	-	-	-
2.7836	90900	0.0607	-	-	-	-
2.7866	91000	0.0303	1.4869	0.9817	0.704	0.4442
2.7897	91100	0.0463	-	-	-	-
2.7927	91200	0.0302	-	-	-	-
2.7958	91300	0.04	-	-	-	-
2.7989	91400	0.0406	-	-	-	-
2.8019	91500	0.0202	-	-	-	-
2.8050	91600	0.0397	-	-	-	-
2.8081	91700	0.0313	-	-	-	-
2.8111	91800	0.0419	-	-	-	-
2.8142	91900	0.055	-	-	-	-
2.8172	92000	0.0377	1.5091	0.9752	0.7071	0.4336
2.8203	92100	0.0386	-	-	-	-
2.8234	92200	0.0497	-	-	-	-
2.8264	92300	0.061	-	-	-	-
2.8295	92400	0.0683	-	-	-	-
2.8326	92500	0.0846	-	-	-	-
2.8356	92600	0.0121	-	-	-	-
2.8387	92700	0.0268	-	-	-	-
2.8417	92800	0.0205	-	-	-	-
2.8448	92900	0.0414	-	-	-	-
2.8479	93000	0.0443	1.5421	0.9798	0.708	0.4298
2.8509	93100	0.0365	-	-	-	-
2.8540	93200	0.0431	-	-	-	-
2.8571	93300	0.0254	-	-	-	-
2.8601	93400	0.0348	-	-	-	-
2.8632	93500	0.0408	-	-	-	-
2.8662	93600	0.0481	-	-	-	-
2.8693	93700	0.0303	-	-	-	-
2.8724	93800	0.0512	-	-	-	-
2.8754	93900	0.0563	-	-	-	-
2.8785	94000	0.0506	1.6061	0.9909	0.7043	0.4392
2.8816	94100	0.0224	-	-	-	-
2.8846	94200	0.0652	-	-	-	-
2.8877	94300	0.0313	-	-	-	-
2.8907	94400	0.0657	-	-	-	-
2.8938	94500	0.0605	-	-	-	-
2.8969	94600	0.0332	-	-	-	-
2.8999	94700	0.0126	-	-	-	-
2.9030	94800	0.0374	-	-	-	-
2.9061	94900	0.051	-	-	-	-
2.9091	95000	0.0477	1.5915	1.0124	0.702	0.4299

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.38.2
PyTorch: 2.1.2+cu121
Accelerate: 0.27.2
Datasets: 2.19.1
Tokenizers: 0.15.2

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

AnglELoss

@misc{li2023angleoptimized,
    title={AnglE-optimized Text Embeddings}, 
    author={Xianming Li and Jing Li},
    year={2023},
    eprint={2309.12871},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}