metadata

base_model: Snowflake/snowflake-arctic-embed-s
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1306883
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: 'query: 06/05/07 teach Children (Age 3 to 9)'
    sentences:
      - >
        passage: 04/13/84 you might want to think of your kidneys as the filters
        of your body. In addition to other important functions, your kidneys and
        nephrons (the smaller filtering units) remove waste from your blood and
        maintain minerals like electrolytes.
      - >
        passage: 06/05/07 children do a lot of growing and developing between
        the ages of 3 and 9. At 3, children are moving out of babyhood and into
        childhood. They have rich imaginations, may have strong fears, and love
        to play physically.
      - >
        passage: 2024/08/20 bothersome phone calls from telemarketers, unknown
        numbers, and unwanted callers can be easily blocked on a Samsung Galaxy
        device. You can block the incoming numbers from the Settings app on your
        Samsung Galaxy device.
  - source_sentence: 'query: Buy Lingerie As a Gift Summer ''45'
    sentences:
      - >
        passage:  Not sure how to buy lingerie for your girlfriend or wife? Read
        this article with step by step instructions on how to select the perfect
        gift! August '45
      - >
        passage: " This guide will lead you through all the starting of
        Half-life, up to ""Unforeseen Consequences"". Apr 17"
      - >
        passage: Making your significant other feel significant is vital for a
        healthy relationship. While every man is different, there are a few
        common gestures, words, and basic actions you should consider using when
        you really want to make sure your man feels loved by you. Dec 3
  - source_sentence: 'query: today:2016-02-29 5 days ago develop an Avatar in Second Life'
    sentences:
      - >
        passage: Sometimes it can be hard to tell if someone is asking you just
        to hang out or if they're actually asking you on a date. It can be
        difficult to read people, and asking if it's a date may seem
        uncomfortable; however, this is the best and most straight-forward way
        to find out the answer to this question. 10/09
      - >
        passage: Winter '15 do you want to join Second Life but don't want to
        look like a newb? Here are some steps to join this virtual world and
        appear as if you've been there forever.
      - >
        passage: 09/30/35 This wikiHow teaches you how to delete a group chat
        from WhatsApp on your iPhone or iPad. 
  - source_sentence: 'query: Enter a Beauty Pageant 02/18'
    sentences:
      - >
        passage: On the outside looking in, the world of beauty pageants can
        feel daunting to try to enter into. However, with proper preparation and
        consistency, the benefits that beauty pageants have to offer are well
        worth the effort. February 18
      - >
        passage: In this high-tech era where technology begins to (gulp!)
        overpower people, human activity is starting to take its toll on the
        environment. Although many organizations have been developed to help
        restore mother earth, most of us still neglect this as we go on about
        our daily lives. 03/23
      - >
        passage: Mar 12 2022 the bench press is a great way to get muscles in
        your chest and arms, but you don't want to immediately start to use one
        after you arrive at the gym.
  - source_sentence: 'query: Seduce Anyone (for Females)'
    sentences:
      - >
        passage: A spiritual journey is a journey you would take to find out who
        you are, what your problems are in life, and how to come to peace with
        the world. The purpose of a spiritual journey is rarely to find an
        answer; rather, it is a process of continually asking questions. 2034
        Nov 30
      - >
        passage: Being a caregiver is an important responsibility, but it can be
        draining. Many caregivers ignore their own mental and physical health in
        favor of taking care of other people. Dec 31 2019
      - >
        passage: Have you ever wondered how to make that amazing guy across the
        room look at you? You have to feel seductive . Before you can succeed in
        seduction you have to be confident, and secure within yourself.

Github and Technical Report

To view our technical report and for access to other model and dataset related scripts visit our github Github

Timely: An Embeddings Model For Temporal Reasoning

At Khoj, we develop open-source personal AI to simplify how people engage with machines. The RAG component in modern AI systems commonly uses an embedding model to retrieve relevant documents for a user query. This retrieved-context enables accurate and personalized responses.

However, most of these models struggle with temporal reasoning. For instance, if asked "Where was I last summer?", the model would struggle to understand the framing of that question. It requires us to understand the relativity of time (that 2010 is before 2011), and when summer might be (between May - September).

When we express dates, we often use shorthands like ‘back in June’, ‘on summer break’, and ‘06/15’; all syntaxes that models don’t presently handle well. As such, your embedding model may not find documents with dates within that specific period. This limitation is significant, given the importance of time and date in language and daily life.

To address this problem, we propose Timely, a comprehensive pipeline for date-aware dataset generation, model fine-tuning, and benchmarking. Specifically, our goal is to create models that can:

Identify natural language dates in queries and documents better
Can handle relative and soft data filters more naturally (e.g. discerning that June is closer than November when talking about Spring).

SentenceTransformer based on Snowflake/snowflake-arctic-embed-s

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-s. It maps sentences & paragraphs to a 384-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: Snowflake/snowflake-arctic-embed-s
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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': 384, '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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: Seduce Anyone (for Females)',
    'passage: Have you ever wondered how to make that amazing guy across the room look at you? You have to feel seductive . Before you can succeed in seduction you have to be confident, and secure within yourself.\n',
    'passage: Being a caregiver is an important responsibility, but it can be draining. Many caregivers ignore their own mental and physical health in favor of taking care of other people. Dec 31 2019\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 1,306,883 training samples
Columns: anchors and positive
Approximate statistics based on the first 1000 samples:
anchors positive
type string string
details
min: 8 tokens
mean: 18.75 tokens
max: 32 tokens

min: 23 tokens
mean: 53.2 tokens
max: 111 tokens

	anchors	positive
type	string	string
details	min: 8 tokens mean: 18.75 tokens max: 32 tokens	min: 23 tokens mean: 53.2 tokens max: 111 tokens

Samples:

anchors	positive
`query: today:2022-04-21 Sell Fine Art Online this month`	`passage: So you're a new or aspiring artist and your creativity has spawned something unique and interesting. Now you're ready to display and sell this creation and perhaps make some money, but you're venturing into the unknown. Spring 2022`
`query: today:2015-01-16 5 days ago be Well Read`	`passage: "jan 11 2015 if you want to be well-read, then, in the words of William Faulkner, you'll have to ""Read, read, read. Read everything..."" You can start at the very beginning, or just make your way down an eclectic list of books that you'd like to read."`
`query: today:2046-07-13 Pick a Stage Name early last year`	`passage: Stage names are used by all types of performers, from musicians, actors and roller derby athletes to burlesque dancers, belly dancers, and exotic dancers. 01/01/45`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 1,000 evaluation samples
Columns: anchors and positive
Approximate statistics based on the first 1000 samples:
anchors positive
type string string
details
min: 6 tokens
mean: 16.48 tokens
max: 32 tokens

min: 16 tokens
mean: 50.03 tokens
max: 108 tokens

	anchors	positive
type	string	string
details	min: 6 tokens mean: 16.48 tokens max: 32 tokens	min: 16 tokens mean: 50.03 tokens max: 108 tokens

Samples:

anchors	positive
`query: 05/15/56 live an Active Life with COPD`	`passage: 56/05/15 cOPD or chronic obstructive pulmonary disease is a progressive respiratory disease that makes it very difficult and uncomfortable to breathe. [1] Long-term smoking is the most common cause of COPD; however, long-term exposure to lung irritants or having untreated asthma can also cause this disease.`
`query: 03/03/44 know if You're Being Used for Sex`	`passage: Mar 3 1944 sometimes you can't put your finger on it, but there's that sinking feeling in your gut that your partner may be using you. The following steps will help you tell if you may be onto something.`
`query: 08/13/22 leave a Job on Good Terms`	`passage: 22/08/13 if you are considering leaving your job or have already been offered a new position that you plan to accept, then it is important to leave your job on good terms.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 1e-05
weight_decay: 0.01
num_train_epochs: 1
warmup_ratio: 0.1
warmup_steps: 400
bf16: True
torch_compile: True
torch_compile_backend: inductor
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: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 1e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 400
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: 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: 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: True
torch_compile_backend: inductor
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss
0.0000	1	2.463	-
0.0005	10	2.6839	-
0.0010	20	2.508	-
0.0015	30	2.6201	-
0.0020	40	2.5629	-
0.0024	50	2.3702	-
0.0029	60	1.9032	-
0.0034	70	1.775	-
0.0039	80	1.736	-
0.0044	90	1.3106	-
0.0049	100	1.2472	-
0.0054	110	1.1614	-
0.0059	120	1.1732	-
0.0064	130	0.778	-
0.0069	140	0.5371	-
0.0073	150	0.6528	-
0.0078	160	0.9433	-
0.0083	170	0.7124	-
0.0088	180	1.021	-
0.0093	190	1.1586	-
0.0098	200	0.5525	-
0.0103	210	0.789	-
0.0108	220	0.7137	-
0.0113	230	0.6174	-
0.0118	240	0.659	-
0.0122	250	0.4407	-
0.0127	260	1.0248	-
0.0132	270	0.9114	-
0.0137	280	0.6073	-
0.0142	290	0.5134	-
0.0147	300	0.7451	-
0.0152	310	0.4924	-
0.0157	320	0.3737	-
0.0162	330	0.3891	-
0.0166	340	0.3237	-
0.0171	350	0.3809	-
0.0176	360	0.5291	-
0.0181	370	0.3295	-
0.0186	380	0.3892	-
0.0191	390	0.3487	-
0.0196	400	0.5052	-
0.0201	410	0.5255	-
0.0206	420	0.5241	-
0.0211	430	0.5539	-
0.0215	440	0.5765	-
0.0220	450	0.782	-
0.0225	460	0.5509	-
0.0230	470	0.5461	-
0.0235	480	0.3992	-
0.0240	490	0.5266	-
0.0245	500	0.4417	-
0.0250	510	0.475	-
0.0255	520	0.4147	-
0.0260	530	0.4742	-
0.0264	540	0.6276	-
0.0269	550	0.7173	-
0.0274	560	0.5529	-
0.0279	570	0.3989	-
0.0284	580	0.3173	-
0.0289	590	0.4824	-
0.0294	600	0.3999	-
0.0299	610	0.4352	-
0.0304	620	0.3562	-
0.0309	630	0.3013	-
0.0313	640	0.408	-
0.0318	650	0.402	-
0.0323	660	0.3746	-
0.0328	670	0.4296	-
0.0333	680	0.316	-
0.0338	690	0.4734	-
0.0343	700	0.3815	-
0.0348	710	0.2987	-
0.0353	720	0.3378	-
0.0357	730	0.2679	-
0.0362	740	0.4536	-
0.0367	750	0.2811	-
0.0372	760	0.2905	-
0.0377	770	0.2114	-
0.0382	780	0.2976	-
0.0387	790	0.332	-
0.0392	800	0.372	-
0.0397	810	0.2444	-
0.0402	820	0.2573	-
0.0406	830	0.3671	-
0.0411	840	0.2718	-
0.0416	850	0.2031	-
0.0421	860	0.2042	-
0.0426	870	0.2193	-
0.0431	880	0.3475	-
0.0436	890	0.2437	-
0.0441	900	0.3298	-
0.0446	910	0.2931	-
0.0451	920	0.3192	-
0.0455	930	0.2971	-
0.0460	940	0.4587	-
0.0465	950	0.5086	-
0.0470	960	0.329	-
0.0475	970	0.3566	-
0.0480	980	0.3455	-
0.0485	990	0.3493	-
0.0490	1000	0.2451	-
0.0495	1010	0.3684	-
0.0499	1020	0.2962	-
0.0504	1030	0.2247	-
0.0509	1040	0.2534	-
0.0514	1050	0.2776	-
0.0519	1060	0.3058	-
0.0524	1070	0.3028	-
0.0529	1080	0.3614	-
0.0534	1090	0.3121	-
0.0539	1100	0.2559	-
0.0544	1110	0.2181	-
0.0548	1120	0.2943	-
0.0553	1130	0.2633	-
0.0558	1140	0.2553	-
0.0563	1150	0.2447	-
0.0568	1160	0.2513	-
0.0573	1170	0.2897	-
0.0578	1180	0.2192	-
0.0583	1190	0.2154	-
0.0588	1200	0.2074	-
0.0593	1210	0.2642	-
0.0597	1220	0.1651	-
0.0602	1230	0.0866	-
0.0607	1240	0.1186	-
0.0612	1250	0.2331	-
0.0617	1260	0.1336	-
0.0622	1270	0.2066	-
0.0627	1280	0.32	-
0.0632	1290	0.2097	-
0.0637	1300	0.1575	-
0.0641	1310	0.183	-
0.0646	1320	0.2042	-
0.0651	1330	0.1569	-
0.0656	1340	0.1899	-
0.0661	1350	0.3229	-
0.0666	1360	0.3262	-
0.0671	1370	0.2069	-
0.0676	1380	0.1655	-
0.0681	1390	0.2683	-
0.0686	1400	0.1987	-
0.0690	1410	0.111	-
0.0695	1420	0.0907	-
0.0700	1430	0.1338	-
0.0705	1440	0.1067	-
0.0710	1450	0.1744	-
0.0715	1460	0.1548	-
0.0720	1470	0.1563	-
0.0725	1480	0.1201	-
0.0730	1490	0.1532	-
0.0735	1500	0.2005	-
0.0739	1510	0.1777	-
0.0744	1520	0.199	-
0.0749	1530	0.2057	-
0.0754	1540	0.2625	-
0.0759	1550	0.2276	-
0.0764	1560	0.1848	-
0.0769	1570	0.2018	-
0.0774	1580	0.2076	-
0.0779	1590	0.1291	-
0.0784	1600	0.1532	-
0.0788	1610	0.14	-
0.0793	1620	0.2032	-
0.0798	1630	0.2156	-
0.0803	1640	0.3091	-
0.0808	1650	0.2326	-
0.0813	1660	0.187	-
0.0818	1670	0.1056	-
0.0823	1680	0.1877	-
0.0828	1690	0.1804	-
0.0832	1700	0.1747	-
0.0837	1710	0.152	-
0.0842	1720	0.1435	-
0.0847	1730	0.1364	-
0.0852	1740	0.1725	-
0.0857	1750	0.1817	-
0.0862	1760	0.1946	-
0.0867	1770	0.078	-
0.0872	1780	0.2161	-
0.0877	1790	0.1929	-
0.0881	1800	0.137	-
0.0886	1810	0.1466	-
0.0891	1820	0.1125	-
0.0896	1830	0.1821	-
0.0901	1840	0.092	-
0.0906	1850	0.1607	-
0.0911	1860	0.0921	-
0.0916	1870	0.0807	-
0.0921	1880	0.1701	-
0.0926	1890	0.1482	-
0.0930	1900	0.1454	-
0.0935	1910	0.1237	-
0.0940	1920	0.1142	-
0.0945	1930	0.1774	-
0.0950	1940	0.0916	-
0.0955	1950	0.0612	-
0.0960	1960	0.0947	-
0.0965	1970	0.1624	-
0.0970	1980	0.1287	-
0.0974	1990	0.1481	-
0.0979	2000	0.1567	0.1126
0.0984	2010	0.1438	-
0.0989	2020	0.1507	-
0.0994	2030	0.1584	-
0.0999	2040	0.2504	-
0.1004	2050	0.1681	-
0.1009	2060	0.1474	-
0.1014	2070	0.2216	-
0.1019	2080	0.1597	-
0.1023	2090	0.1108	-
0.1028	2100	0.1798	-
0.1033	2110	0.1676	-
0.1038	2120	0.12	-
0.1043	2130	0.1385	-
0.1048	2140	0.127	-
0.1053	2150	0.1516	-
0.1058	2160	0.1192	-
0.1063	2170	0.1803	-
0.1068	2180	0.176	-
0.1072	2190	0.1377	-
0.1077	2200	0.1108	-
0.1082	2210	0.1599	-
0.1087	2220	0.1772	-
0.1092	2230	0.1554	-
0.1097	2240	0.1394	-
0.1102	2250	0.1254	-
0.1107	2260	0.1319	-
0.1112	2270	0.1671	-
0.1116	2280	0.1127	-
0.1121	2290	0.1128	-
0.1126	2300	0.158	-
0.1131	2310	0.1223	-
0.1136	2320	0.0667	-
0.1141	2330	0.057	-
0.1146	2340	0.0929	-
0.1151	2350	0.1422	-
0.1156	2360	0.1032	-
0.1161	2370	0.229	-
0.1165	2380	0.1188	-
0.1170	2390	0.1007	-
0.1175	2400	0.141	-
0.1180	2410	0.1351	-
0.1185	2420	0.1037	-
0.1190	2430	0.1403	-
0.1195	2440	0.1239	-
0.1200	2450	0.2462	-
0.1205	2460	0.1647	-
0.1210	2470	0.0821	-
0.1214	2480	0.1487	-
0.1219	2490	0.1804	-
0.1224	2500	0.0706	-
0.1229	2510	0.0556	-
0.1234	2520	0.0689	-
0.1239	2530	0.0844	-
0.1244	2540	0.0738	-
0.1249	2550	0.1449	-
0.1254	2560	0.0679	-
0.1259	2570	0.0915	-
0.1263	2580	0.1139	-
0.1268	2590	0.1547	-
0.1273	2600	0.1336	-
0.1278	2610	0.148	-
0.1283	2620	0.1186	-
0.1288	2630	0.1699	-
0.1293	2640	0.1443	-
0.1298	2650	0.1672	-
0.1303	2660	0.1639	-
0.1307	2670	0.1202	-
0.1312	2680	0.1277	-
0.1317	2690	0.1115	-
0.1322	2700	0.111	-
0.1327	2710	0.1377	-
0.1332	2720	0.1548	-
0.1337	2730	0.2035	-
0.1342	2740	0.203	-
0.1347	2750	0.1508	-
0.1352	2760	0.0766	-
0.1356	2770	0.117	-
0.1361	2780	0.1575	-
0.1366	2790	0.1008	-
0.1371	2800	0.1341	-
0.1376	2810	0.1129	-
0.1381	2820	0.1076	-
0.1386	2830	0.1101	-
0.1391	2840	0.1116	-
0.1396	2850	0.1342	-
0.1401	2860	0.0751	-
0.1405	2870	0.1598	-
0.1410	2880	0.1592	-
0.1415	2890	0.0995	-
0.1420	2900	0.0775	-
0.1425	2910	0.1304	-
0.1430	2920	0.1205	-
0.1435	2930	0.0874	-
0.1440	2940	0.1047	-
0.1445	2950	0.0817	-
0.1449	2960	0.0621	-
0.1454	2970	0.1177	-
0.1459	2980	0.1072	-
0.1464	2990	0.1218	-
0.1469	3000	0.1067	-
0.1474	3010	0.0913	-
0.1479	3020	0.1535	-
0.1484	3030	0.0547	-
0.1489	3040	0.0641	-
0.1494	3050	0.0607	-
0.1498	3060	0.104	-
0.1503	3070	0.1177	-
0.1508	3080	0.094	-
0.1513	3090	0.1327	-
0.1518	3100	0.085	-
0.1523	3110	0.13	-
0.1528	3120	0.1419	-
0.1533	3130	0.1822	-
0.1538	3140	0.1135	-
0.1543	3150	0.1285	-
0.1547	3160	0.1457	-
0.1552	3170	0.1358	-
0.1557	3180	0.1231	-
0.1562	3190	0.1164	-
0.1567	3200	0.1533	-
0.1572	3210	0.1205	-
0.1577	3220	0.1388	-
0.1582	3230	0.0905	-
0.1587	3240	0.1202	-
0.1591	3250	0.1067	-
0.1596	3260	0.1322	-
0.1601	3270	0.1497	-
0.1606	3280	0.1142	-
0.1611	3290	0.0952	-
0.1616	3300	0.0863	-
0.1621	3310	0.1551	-
0.1626	3320	0.1349	-
0.1631	3330	0.1363	-
0.1636	3340	0.0953	-
0.1640	3350	0.1257	-
0.1645	3360	0.1533	-
0.1650	3370	0.0983	-
0.1655	3380	0.0832	-
0.1660	3390	0.1221	-
0.1665	3400	0.1186	-
0.1670	3410	0.0682	-
0.1675	3420	0.0433	-
0.1680	3430	0.0518	-
0.1685	3440	0.1377	-
0.1689	3450	0.0737	-
0.1694	3460	0.1448	-
0.1699	3470	0.1294	-
0.1704	3480	0.0623	-
0.1709	3490	0.1302	-
0.1714	3500	0.1022	-
0.1719	3510	0.111	-
0.1724	3520	0.1022	-
0.1729	3530	0.0718	-
0.1734	3540	0.1988	-
0.1738	3550	0.1589	-
0.1743	3560	0.0929	-
0.1748	3570	0.0916	-
0.1753	3580	0.1248	-
0.1758	3590	0.0794	-
0.1763	3600	0.0566	-
0.1768	3610	0.0427	-
0.1773	3620	0.0656	-
0.1778	3630	0.0537	-
0.1782	3640	0.1139	-
0.1787	3650	0.0535	-
0.1792	3660	0.0753	-
0.1797	3670	0.073	-
0.1802	3680	0.1053	-
0.1807	3690	0.1138	-
0.1812	3700	0.1173	-
0.1817	3710	0.0983	-
0.1822	3720	0.1168	-
0.1827	3730	0.1732	-
0.1831	3740	0.1568	-
0.1836	3750	0.1222	-
0.1841	3760	0.1029	-
0.1846	3770	0.129	-
0.1851	3780	0.1033	-
0.1856	3790	0.1062	-
0.1861	3800	0.0901	-
0.1866	3810	0.1113	-
0.1871	3820	0.1488	-
0.1876	3830	0.1517	-
0.1880	3840	0.1567	-
0.1885	3850	0.0574	-
0.1890	3860	0.0765	-
0.1895	3870	0.1653	-
0.1900	3880	0.0785	-
0.1905	3890	0.112	-
0.1910	3900	0.09	-
0.1915	3910	0.0962	-
0.1920	3920	0.1064	-
0.1924	3930	0.106	-
0.1929	3940	0.1154	-
0.1934	3950	0.078	-
0.1939	3960	0.0996	-
0.1944	3970	0.1127	-
0.1949	3980	0.1	-
0.1954	3990	0.0762	-
0.1959	4000	0.0765	0.1027
0.1964	4010	0.1032	-
0.1969	4020	0.1264	-
0.1973	4030	0.0933	-
0.1978	4040	0.0706	-
0.1983	4050	0.0653	-
0.1988	4060	0.0756	-
0.1993	4070	0.0738	-
0.1998	4080	0.1048	-
0.2003	4090	0.0912	-
0.2008	4100	0.0727	-
0.2013	4110	0.1189	-
0.2018	4120	0.0525	-
0.2022	4130	0.0517	-
0.2027	4140	0.0482	-
0.2032	4150	0.0655	-
0.2037	4160	0.099	-
0.2042	4170	0.0904	-
0.2047	4180	0.085	-
0.2052	4190	0.0637	-
0.2057	4200	0.1223	-
0.2062	4210	0.092	-
0.2067	4220	0.1483	-
0.2071	4230	0.1489	-
0.2076	4240	0.1015	-
0.2081	4250	0.155	-
0.2086	4260	0.1409	-
0.2091	4270	0.1087	-
0.2096	4280	0.0783	-
0.2101	4290	0.1459	-
0.2106	4300	0.1029	-
0.2111	4310	0.0817	-
0.2115	4320	0.0847	-
0.2120	4330	0.0982	-
0.2125	4340	0.09	-
0.2130	4350	0.1161	-
0.2135	4360	0.1129	-
0.2140	4370	0.1239	-
0.2145	4380	0.0613	-
0.2150	4390	0.0938	-
0.2155	4400	0.113	-
0.2160	4410	0.129	-
0.2164	4420	0.1036	-
0.2169	4430	0.1028	-
0.2174	4440	0.1011	-
0.2179	4450	0.1215	-
0.2184	4460	0.0671	-
0.2189	4470	0.0797	-
0.2194	4480	0.0833	-
0.2199	4490	0.1169	-
0.2204	4500	0.044	-
0.2209	4510	0.0371	-
0.2213	4520	0.0519	-
0.2218	4530	0.1075	-
0.2223	4540	0.0655	-
0.2228	4550	0.0887	-
0.2233	4560	0.1477	-
0.2238	4570	0.0595	-
0.2243	4580	0.0799	-
0.2248	4590	0.0878	-
0.2253	4600	0.0929	-
0.2257	4610	0.0926	-
0.2262	4620	0.0641	-
0.2267	4630	0.1503	-
0.2272	4640	0.137	-
0.2277	4650	0.0828	-
0.2282	4660	0.0622	-
0.2287	4670	0.1166	-
0.2292	4680	0.1228	-
0.2297	4690	0.0365	-
0.2302	4700	0.0441	-
0.2306	4710	0.0397	-
0.2311	4720	0.0598	-
0.2316	4730	0.0721	-
0.2321	4740	0.0529	-
0.2326	4750	0.0757	-
0.2331	4760	0.0719	-
0.2336	4770	0.0787	-
0.2341	4780	0.1102	-
0.2346	4790	0.0847	-
0.2351	4800	0.0931	-
0.2355	4810	0.0981	-
0.2360	4820	0.1705	-
0.2365	4830	0.1054	-
0.2370	4840	0.1323	-
0.2375	4850	0.0833	-
0.2380	4860	0.1127	-
0.2385	4870	0.0978	-
0.2390	4880	0.0965	-
0.2395	4890	0.0646	-
0.2399	4900	0.1204	-
0.2404	4910	0.1308	-
0.2409	4920	0.1481	-
0.2414	4930	0.1441	-
0.2419	4940	0.0793	-
0.2424	4950	0.0685	-
0.2429	4960	0.1135	-
0.2434	4970	0.0989	-
0.2439	4980	0.1099	-
0.2444	4990	0.0746	-
0.2448	5000	0.083	-
0.2453	5010	0.0797	-
0.2458	5020	0.0817	-
0.2463	5030	0.0914	-
0.2468	5040	0.0864	-
0.2473	5050	0.082	-
0.2478	5060	0.1238	-
0.2483	5070	0.0922	-
0.2488	5080	0.0656	-
0.2493	5090	0.076	-
0.2497	5100	0.0536	-
0.2502	5110	0.1061	-
0.2507	5120	0.0491	-
0.2512	5130	0.0779	-
0.2517	5140	0.0326	-
0.2522	5150	0.0422	-
0.2527	5160	0.0849	-
0.2532	5170	0.0916	-
0.2537	5180	0.0717	-
0.2542	5190	0.0669	-
0.2546	5200	0.0675	-
0.2551	5210	0.0914	-
0.2556	5220	0.0461	-
0.2561	5230	0.037	-
0.2566	5240	0.0532	-
0.2571	5250	0.1071	-
0.2576	5260	0.0764	-
0.2581	5270	0.08	-
0.2586	5280	0.086	-
0.2590	5290	0.0795	-
0.2595	5300	0.101	-
0.2600	5310	0.1379	-
0.2605	5320	0.144	-
0.2610	5330	0.0848	-
0.2615	5340	0.1016	-
0.2620	5350	0.1004	-
0.2625	5360	0.0911	-
0.2630	5370	0.0811	-
0.2635	5380	0.1014	-
0.2639	5390	0.0808	-
0.2644	5400	0.0682	-
0.2649	5410	0.0661	-
0.2654	5420	0.0722	-
0.2659	5430	0.0744	-
0.2664	5440	0.0935	-
0.2669	5450	0.1014	-
0.2674	5460	0.1087	-
0.2679	5470	0.0704	-
0.2684	5480	0.0487	-
0.2688	5490	0.1111	-
0.2693	5500	0.0939	-
0.2698	5510	0.0834	-
0.2703	5520	0.0951	-
0.2708	5530	0.0797	-
0.2713	5540	0.097	-
0.2718	5550	0.1229	-
0.2723	5560	0.1156	-
0.2728	5570	0.1173	-
0.2732	5580	0.1665	-
0.2737	5590	0.1172	-
0.2742	5600	0.1169	-
0.2747	5610	0.0926	-
0.2752	5620	0.0896	-
0.2757	5630	0.1396	-
0.2762	5640	0.132	-
0.2767	5650	0.0862	-
0.2772	5660	0.1048	-
0.2777	5670	0.1336	-
0.2781	5680	0.0799	-
0.2786	5690	0.0557	-
0.2791	5700	0.0345	-
0.2796	5710	0.0831	-
0.2801	5720	0.0759	-
0.2806	5730	0.083	-
0.2811	5740	0.1425	-
0.2816	5750	0.114	-
0.2821	5760	0.085	-
0.2826	5770	0.088	-
0.2830	5780	0.0976	-
0.2835	5790	0.0945	-
0.2840	5800	0.0699	-
0.2845	5810	0.1009	-
0.2850	5820	0.192	-
0.2855	5830	0.1152	-
0.2860	5840	0.0767	-
0.2865	5850	0.1052	-
0.2870	5860	0.1044	-
0.2874	5870	0.0515	-
0.2879	5880	0.0456	-
0.2884	5890	0.0538	-
0.2889	5900	0.0484	-
0.2894	5910	0.0508	-
0.2899	5920	0.1131	-
0.2904	5930	0.0678	-
0.2909	5940	0.0416	-
0.2914	5950	0.0743	-
0.2919	5960	0.0891	-
0.2923	5970	0.1063	-
0.2928	5980	0.0888	-
0.2933	5990	0.0775	-
0.2938	6000	0.1278	0.0782
0.2943	6010	0.1088	-
0.2948	6020	0.0928	-
0.2953	6030	0.123	-
0.2958	6040	0.0955	-
0.2963	6050	0.0788	-
0.2968	6060	0.0754	-
0.2972	6070	0.0973	-
0.2977	6080	0.0754	-
0.2982	6090	0.1176	-
0.2987	6100	0.1307	-
0.2992	6110	0.1045	-
0.2997	6120	0.1078	-
0.3002	6130	0.0372	-
0.3007	6140	0.0789	-
0.3012	6150	0.1295	-
0.3017	6160	0.0692	-
0.3021	6170	0.0815	-
0.3026	6180	0.0655	-
0.3031	6190	0.0717	-
0.3036	6200	0.0728	-
0.3041	6210	0.0748	-
0.3046	6220	0.0971	-
0.3051	6230	0.0448	-
0.3056	6240	0.0889	-
0.3061	6250	0.1217	-
0.3065	6260	0.0586	-
0.3070	6270	0.0765	-
0.3075	6280	0.0601	-
0.3080	6290	0.116	-
0.3085	6300	0.0626	-
0.3090	6310	0.0618	-
0.3095	6320	0.0426	-
0.3100	6330	0.0473	-
0.3105	6340	0.0819	-
0.3110	6350	0.0797	-
0.3114	6360	0.0859	-
0.3119	6370	0.0794	-
0.3124	6380	0.0396	-
0.3129	6390	0.1117	-
0.3134	6400	0.0382	-
0.3139	6410	0.0412	-
0.3144	6420	0.0567	-
0.3149	6430	0.0772	-
0.3154	6440	0.065	-
0.3159	6450	0.0796	-
0.3163	6460	0.0565	-
0.3168	6470	0.0604	-
0.3173	6480	0.0882	-
0.3178	6490	0.0787	-
0.3183	6500	0.1398	-
0.3188	6510	0.0567	-
0.3193	6520	0.0901	-
0.3198	6530	0.1118	-
0.3203	6540	0.0952	-
0.3207	6550	0.063	-
0.3212	6560	0.0842	-
0.3217	6570	0.1133	-
0.3222	6580	0.0586	-
0.3227	6590	0.0759	-
0.3232	6600	0.0681	-
0.3237	6610	0.0823	-
0.3242	6620	0.0541	-
0.3247	6630	0.0714	-
0.3252	6640	0.1007	-
0.3256	6650	0.0655	-
0.3261	6660	0.0506	-
0.3266	6670	0.0662	-
0.3271	6680	0.1119	-
0.3276	6690	0.0681	-
0.3281	6700	0.0675	-
0.3286	6710	0.0697	-
0.3291	6720	0.0652	-
0.3296	6730	0.0885	-
0.3301	6740	0.0618	-
0.3305	6750	0.0554	-
0.3310	6760	0.0851	-
0.3315	6770	0.0835	-
0.3320	6780	0.0357	-
0.3325	6790	0.031	-
0.3330	6800	0.0462	-
0.3335	6810	0.0906	-
0.3340	6820	0.0513	-
0.3345	6830	0.0832	-
0.3349	6840	0.1112	-
0.3354	6850	0.0429	-
0.3359	6860	0.0711	-
0.3364	6870	0.0754	-
0.3369	6880	0.0678	-
0.3374	6890	0.0674	-
0.3379	6900	0.0554	-
0.3384	6910	0.1501	-
0.3389	6920	0.1062	-
0.3394	6930	0.0529	-
0.3398	6940	0.0607	-
0.3403	6950	0.1078	-
0.3408	6960	0.0495	-
0.3413	6970	0.0374	-
0.3418	6980	0.0286	-
0.3423	6990	0.0323	-
0.3428	7000	0.0402	-
0.3433	7010	0.0705	-
0.3438	7020	0.0278	-
0.3443	7030	0.0678	-
0.3447	7040	0.0536	-
0.3452	7050	0.0711	-
0.3457	7060	0.0715	-
0.3462	7070	0.0639	-
0.3467	7080	0.0602	-
0.3472	7090	0.0841	-
0.3477	7100	0.108	-
0.3482	7110	0.11	-
0.3487	7120	0.0801	-
0.3492	7130	0.0693	-
0.3496	7140	0.0939	-
0.3501	7150	0.0498	-
0.3506	7160	0.0556	-
0.3511	7170	0.0741	-
0.3516	7180	0.0702	-
0.3521	7190	0.1051	-
0.3526	7200	0.1164	-
0.3531	7210	0.0927	-
0.3536	7220	0.0462	-
0.3540	7230	0.0595	-
0.3545	7240	0.1006	-
0.3550	7250	0.0497	-
0.3555	7260	0.0816	-
0.3560	7270	0.054	-
0.3565	7280	0.0837	-
0.3570	7290	0.063	-
0.3575	7300	0.067	-
0.3580	7310	0.0736	-
0.3585	7320	0.0441	-
0.3589	7330	0.0626	-
0.3594	7340	0.0875	-
0.3599	7350	0.0578	-
0.3604	7360	0.0421	-
0.3609	7370	0.0595	-
0.3614	7380	0.0412	-
0.3619	7390	0.0768	-
0.3624	7400	0.042	-
0.3629	7410	0.0544	-
0.3634	7420	0.0463	-
0.3638	7430	0.0561	-
0.3643	7440	0.052	-
0.3648	7450	0.0736	-
0.3653	7460	0.0552	-
0.3658	7470	0.0391	-
0.3663	7480	0.0849	-
0.3668	7490	0.0345	-
0.3673	7500	0.0359	-
0.3678	7510	0.0322	-
0.3682	7520	0.0534	-
0.3687	7530	0.0703	-
0.3692	7540	0.061	-
0.3697	7550	0.07	-
0.3702	7560	0.0479	-
0.3707	7570	0.0683	-
0.3712	7580	0.0686	-
0.3717	7590	0.1208	-
0.3722	7600	0.1042	-
0.3727	7610	0.087	-
0.3731	7620	0.0845	-
0.3736	7630	0.0654	-
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0.8756	17880	0.0233	-
0.8761	17890	0.0073	-
0.8765	17900	0.0086	-
0.8770	17910	0.0106	-
0.8775	17920	0.0127	-
0.8780	17930	0.0051	-
0.8785	17940	0.0044	-
0.8790	17950	0.0051	-
0.8795	17960	0.007	-
0.8800	17970	0.0067	-
0.8805	17980	0.0067	-
0.8810	17990	0.0077	-
0.8814	18000	0.0081	0.1016
0.8819	18010	0.0108	-
0.8824	18020	0.0083	-
0.8829	18030	0.0081	-
0.8834	18040	0.0108	-
0.8839	18050	0.0063	-
0.8844	18060	0.017	-
0.8849	18070	0.0099	-
0.8854	18080	0.0086	-
0.8859	18090	0.0081	-
0.8863	18100	0.011	-
0.8868	18110	0.0109	-
0.8873	18120	0.009	-
0.8878	18130	0.0077	-
0.8883	18140	0.0107	-
0.8888	18150	0.0073	-
0.8893	18160	0.0119	-
0.8898	18170	0.0114	-
0.8903	18180	0.0064	-
0.8907	18190	0.0081	-
0.8912	18200	0.0129	-
0.8917	18210	0.0116	-
0.8922	18220	0.0085	-
0.8927	18230	0.0093	-
0.8932	18240	0.2955	-
0.8937	18250	0.4554	-
0.8942	18260	0.762	-
0.8947	18270	0.7374	-
0.8952	18280	0.7161	-
0.8956	18290	0.6563	-
0.8961	18300	0.616	-
0.8966	18310	0.6997	-
0.8971	18320	0.7376	-
0.8976	18330	0.541	-
0.8981	18340	0.5408	-
0.8986	18350	0.709	-
0.8991	18360	0.4876	-
0.8996	18370	0.3197	-
0.9001	18380	0.3545	-
0.9005	18390	0.4687	-
0.9010	18400	0.5751	-
0.9015	18410	0.5152	-
0.9020	18420	0.9007	-
0.9025	18430	0.548	-
0.9030	18440	0.4942	-
0.9035	18450	0.7123	-
0.9040	18460	0.6689	-
0.9045	18470	0.5752	-
0.9050	18480	0.5242	-
0.9054	18490	0.6176	-
0.9059	18500	0.9321	-
0.9064	18510	0.7373	-
0.9069	18520	0.4825	-
0.9074	18530	0.7664	-
0.9079	18540	0.7425	-
0.9084	18550	0.3581	-
0.9089	18560	0.3735	-
0.9094	18570	0.314	-
0.9098	18580	0.3325	-
0.9103	18590	0.3967	-
0.9108	18600	0.5676	-
0.9113	18610	0.4315	-
0.9118	18620	0.3517	-
0.9123	18630	0.3892	-
0.9128	18640	0.6173	-
0.9133	18650	0.6591	-
0.9138	18660	0.6593	-
0.9143	18670	0.5862	-
0.9147	18680	0.6172	-
0.9152	18690	0.6425	-
0.9157	18700	0.6433	-
0.9162	18710	0.7671	-
0.9167	18720	0.5128	-
0.9172	18730	0.5309	-
0.9177	18740	0.4731	-
0.9182	18750	0.5708	-
0.9187	18760	0.6416	-
0.9192	18770	0.6998	-
0.9196	18780	0.9397	-
0.9201	18790	0.9068	-
0.9206	18800	0.7433	-
0.9211	18810	0.3458	-
0.9216	18820	0.5704	-
0.9221	18830	0.7769	-
0.9226	18840	0.5343	-
0.9231	18850	0.6232	-
0.9236	18860	0.5208	-
0.9240	18870	0.5814	-
0.9245	18880	0.5802	-
0.9250	18890	0.6375	-
0.9255	18900	0.717	-
0.9260	18910	0.348	-
0.9265	18920	0.7098	-
0.9270	18930	0.895	-
0.9275	18940	0.4557	-
0.9280	18950	0.521	-
0.9285	18960	0.4958	-
0.9289	18970	0.6477	-
0.9294	18980	0.5101	-
0.9299	18990	0.5748	-
0.9304	19000	0.4297	-
0.9309	19010	0.3849	-
0.9314	19020	0.5418	-
0.9319	19030	0.4313	-
0.9324	19040	0.5116	-
0.9329	19050	0.5796	-
0.9334	19060	0.3022	-
0.9338	19070	0.5954	-
0.9343	19080	0.3133	-
0.9348	19090	0.3182	-
0.9353	19100	0.3919	-
0.9358	19110	0.5255	-
0.9363	19120	0.4708	-
0.9368	19130	0.5831	-
0.9373	19140	0.5358	-
0.9378	19150	0.4393	-
0.9382	19160	0.75	-
0.9387	19170	0.6024	-
0.9392	19180	0.9313	-
0.9397	19190	0.5139	-
0.9402	19200	0.6867	-
0.9407	19210	0.636	-
0.9412	19220	0.6668	-
0.9417	19230	0.571	-
0.9422	19240	0.5811	-
0.9427	19250	0.6481	-
0.9431	19260	0.4462	-
0.9436	19270	0.5433	-
0.9441	19280	0.4351	-
0.9446	19290	0.5578	-
0.9451	19300	0.4605	-
0.9456	19310	0.6328	-
0.9461	19320	0.7166	-
0.9466	19330	0.6318	-
0.9471	19340	0.5794	-
0.9476	19350	0.7647	-
0.9480	19360	0.8804	-
0.9485	19370	0.7286	-
0.9490	19380	0.7321	-
0.9495	19390	0.7864	-
0.9500	19400	0.5689	-
0.9505	19410	0.9057	-
0.9510	19420	0.497	-
0.9515	19430	0.5787	-
0.9520	19440	0.7097	-
0.9525	19450	0.6303	-
0.9529	19460	0.394	-
0.9534	19470	0.3503	-
0.9539	19480	0.5089	-
0.9544	19490	0.5855	-
0.9549	19500	0.4965	-
0.9554	19510	0.8147	-
0.9559	19520	0.6399	-
0.9564	19530	0.5019	-
0.9569	19540	0.6137	-
0.9573	19550	0.6428	-
0.9578	19560	0.6273	-
0.9583	19570	0.5076	-
0.9588	19580	0.4252	-
0.9593	19590	1.0695	-
0.9598	19600	0.8253	-
0.9603	19610	0.5121	-
0.9608	19620	0.6426	-
0.9613	19630	0.8301	-
0.9618	19640	0.4727	-
0.9622	19650	0.3053	-
0.9627	19660	0.3459	-
0.9632	19670	0.3102	-
0.9637	19680	0.3296	-
0.9642	19690	0.5441	-
0.9647	19700	0.2707	-
0.9652	19710	0.4685	-
0.9657	19720	0.4003	-
0.9662	19730	0.5628	-
0.9667	19740	0.7177	-
0.9671	19750	0.5882	-
0.9676	19760	0.5677	-
0.9681	19770	0.5707	-
0.9686	19780	0.6003	-
0.9691	19790	0.7366	-
0.9696	19800	0.6806	-
0.9701	19810	0.4734	-
0.9706	19820	0.673	-
0.9711	19830	0.5724	-
0.9715	19840	0.5511	-
0.9720	19850	0.6837	-
0.9725	19860	0.6004	-
0.9730	19870	0.9446	-
0.9735	19880	0.9844	-
0.9740	19890	0.7493	-
0.9745	19900	0.4858	-
0.9750	19910	0.5202	-
0.9755	19920	0.7779	-
0.9760	19930	0.5717	-
0.9764	19940	0.678	-
0.9769	19950	0.4574	-
0.9774	19960	0.5835	-
0.9779	19970	0.596	-
0.9784	19980	0.5762	-
0.9789	19990	0.7357	-
0.9794	20000	0.4642	0.0178
0.9799	20010	0.5685	-
0.9804	20020	0.921	-
0.9809	20030	0.4972	-
0.9813	20040	0.4969	-
0.9818	20050	0.5403	-
0.9823	20060	0.4712	-
0.9828	20070	0.6109	-
0.9833	20080	0.6189	-
0.9838	20090	0.4534	-
0.9843	20100	0.3921	-
0.9848	20110	0.5543	-
0.9853	20120	0.4145	-
0.9857	20130	0.5284	-
0.9862	20140	0.4588	-
0.9867	20150	0.3422	-
0.9872	20160	0.5918	-
0.9877	20170	0.2886	-
0.9882	20180	0.3263	-
0.9887	20190	0.3831	-
0.9892	20200	0.5617	-
0.9897	20210	0.5091	-
0.9902	20220	0.5814	-
0.9906	20230	0.5548	-
0.9911	20240	0.3641	-
0.9916	20250	0.6875	-
0.9921	20260	0.5777	-
0.9926	20270	0.9048	-
0.9931	20280	0.7485	-
0.9936	20290	0.6309	-
0.9941	20300	0.6553	-
0.9946	20310	0.6533	-
0.9951	20320	0.6127	-
0.9955	20330	0.4696	-
0.9960	20340	0.7702	-
0.9965	20350	0.5024	-
0.9970	20360	0.4828	-
0.9975	20370	0.4745	-
0.9980	20380	0.476	-
0.9985	20390	0.5527	-
0.9990	20400	0.6704	-
0.9995	20410	0.6962	-
1.0000	20420	0.656	-

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.3
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
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",
}

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}
}