SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct

This is a sentence-transformers model finetuned from Qwen/Qwen2.5-0.5B-Instruct. It maps sentences & paragraphs to a 896-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: Qwen/Qwen2.5-0.5B-Instruct
Maximum Sequence Length: 1024 tokens
Output Dimensionality: 896 dimensions
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': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 896, '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("AlexWortega/qwen10k")
# Run inference
sentences = [
    'When was ABC formed?',
    "American Broadcasting Company\nABC launched as a radio network on October 12, 1943, serving as the successor to the NBC Blue Network, which had been purchased by Edward J. Noble. It extended its operations to television in 1948, following in the footsteps of established broadcast networks CBS and NBC. In the mid-1950s, ABC merged with United Paramount Theatres, a chain of movie theaters that formerly operated as a subsidiary of Paramount Pictures. Leonard Goldenson, who had been the head of UPT, made the new television network profitable by helping develop and greenlight many successful series. In the 1980s, after purchasing an 80% interest in cable sports channel ESPN, the network's corporate parent, American Broadcasting Companies, Inc., merged with Capital Cities Communications, owner of several print publications, and television and radio stations. In 1996, most of Capital Cities/ABC's assets were purchased by The Walt Disney Company.",
    'Americans Battling Communism\nAmericans Battling Communism, Inc. (ABC) was an anti-communist organization created following an October 1947 speech by Pennsylvania Judge Blair Gunther that called for an "ABC movement" to educate America about communism. Chartered in November 1947 by Harry Alan Sherman, a local lawyer active in various anti-communist organizations, the group took part in such activities as blacklisting by disclosing the names of people suspected of being communists. Its members included local judges and lawyers active in the McCarthy-era prosecution of communists.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

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

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev-896 and sts-dev-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev-896	sts-dev-768
pearson_cosine	0.7583	0.7551
spearman_cosine	0.7687	0.7657

Training Details

Training Dataset

Unnamed Dataset

Size: 1,077,240 training samples
Columns: query, response, and negative

Approximate statistics based on the first 1000 samples:

	query	response	negative
type	string	string	string
details	min: 4 tokens mean: 8.76 tokens max: 26 tokens	min: 23 tokens mean: 141.88 tokens max: 532 tokens	min: 4 tokens mean: 134.02 tokens max: 472 tokens

Samples:

query	response	negative
`Was there a year 0?`	`Year zero Year zero does not exist in the anno Domini system usually used to number years in the Gregorian calendar and in its predecessor, the Julian calendar. In this system, the year 1 BC is followed by AD 1. However, there is a year zero in astronomical year numbering (where it coincides with the Julian year 1 BC) and in ISO 8601:2004 (where it coincides with the Gregorian year 1 BC) as well as in all Buddhist and Hindu calendars.`	`504 Year 504 (DIV) was a leap year starting on Thursday (link will display the full calendar) of the Julian calendar. At the time, it was known as the Year of the Consulship of Nicomachus without colleague (or, less frequently, year 1257 "Ab urbe condita"). The denomination 504 for this year has been used since the early medieval period, when the Anno Domini calendar era became the prevalent method in Europe for naming years.`
`When is the dialectical method used?`	Dialectic Dialectic or dialectics (Greek: διαλεκτική, dialektikḗ; related to dialogue), also known as the dialectical method, is at base a discourse between two or more people holding different points of view about a subject but wishing to establish the truth through reasoned arguments. Dialectic resembles debate, but the concept excludes subjective elements such as emotional appeal and the modern pejorative sense of rhetoric.[1][2] Dialectic may be contrasted with the didactic method, wherein one side of the conversation teaches the other. Dialectic is alternatively known as minor logic, as opposed to major logic or critique.	Derek Bentley case Another factor in the posthumous defence was that a "confession" recorded by Bentley, which was claimed by the prosecution to be a "verbatim record of dictated monologue", was shown by forensic linguistics methods to have been largely edited by policemen. Linguist Malcolm Coulthard showed that certain patterns, such as the frequency of the word "then" and the grammatical use of "then" after the grammatical subject ("I then" rather than "then I"), were not consistent with Bentley's use of language (his idiolect), as evidenced in court testimony. These patterns fit better the recorded testimony of the policemen involved. This is one of the earliest uses of forensic linguistics on record.
`What do Grasshoppers eat?`	Grasshopper Grasshoppers are plant-eaters, with a few species at times becoming serious pests of cereals, vegetables and pasture, especially when they swarm in their millions as locusts and destroy crops over wide areas. They protect themselves from predators by camouflage; when detected, many species attempt to startle the predator with a brilliantly-coloured wing-flash while jumping and (if adult) launching themselves into the air, usually flying for only a short distance. Other species such as the rainbow grasshopper have warning coloration which deters predators. Grasshoppers are affected by parasites and various diseases, and many predatory creatures feed on both nymphs and adults. The eggs are the subject of attack by parasitoids and predators.	Groundhog Very often the dens of groundhogs provide homes for other animals including skunks, red foxes, and cottontail rabbits. The fox and skunk feed upon field mice, grasshoppers, beetles and other creatures that destroy farm crops. In aiding these animals, the groundhog indirectly helps the farmer. In addition to providing homes for itself and other animals, the groundhog aids in soil improvement by bringing subsoil to the surface. The groundhog is also a valuable game animal and is considered a difficult sport when hunted in a fair manner. In some parts of Appalachia, they are eaten.

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: 12
per_device_eval_batch_size: 12
gradient_accumulation_steps: 4
num_train_epochs: 1
warmup_ratio: 0.3
bf16: True
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: 12
per_device_eval_batch_size: 12
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.3
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: 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
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	sts-dev-896_spearman_cosine	sts-dev-768_spearman_cosine
0.0004	10	2.2049	-	-
0.0009	20	2.3168	-	-
0.0013	30	2.3544	-	-
0.0018	40	2.2519	-	-
0.0022	50	2.1809	-	-
0.0027	60	2.1572	-	-
0.0031	70	2.1855	-	-
0.0036	80	2.5887	-	-
0.0040	90	2.883	-	-
0.0045	100	2.8557	-	-
0.0049	110	2.9356	-	-
0.0053	120	2.8833	-	-
0.0058	130	2.8394	-	-
0.0062	140	2.923	-	-
0.0067	150	2.8191	-	-
0.0071	160	2.8658	-	-
0.0076	170	2.8252	-	-
0.0080	180	2.8312	-	-
0.0085	190	2.7761	-	-
0.0089	200	2.7193	-	-
0.0094	210	2.724	-	-
0.0098	220	2.7484	-	-
0.0102	230	2.7262	-	-
0.0107	240	2.6964	-	-
0.0111	250	2.6676	-	-
0.0116	260	2.6715	-	-
0.0120	270	2.6145	-	-
0.0125	280	2.6191	-	-
0.0129	290	1.9812	-	-
0.0134	300	1.6413	-	-
0.0138	310	1.6126	-	-
0.0143	320	1.3599	-	-
0.0147	330	1.2996	-	-
0.0151	340	1.2654	-	-
0.0156	350	1.9409	-	-
0.0160	360	2.1287	-	-
0.0165	370	1.8442	-	-
0.0169	380	1.6837	-	-
0.0174	390	1.5489	-	-
0.0178	400	1.4382	-	-
0.0183	410	1.4848	-	-
0.0187	420	1.3481	-	-
0.0192	430	1.3467	-	-
0.0196	440	1.3977	-	-
0.0201	450	1.26	-	-
0.0205	460	1.2412	-	-
0.0209	470	1.316	-	-
0.0214	480	1.3501	-	-
0.0218	490	1.2246	-	-
0.0223	500	1.2271	-	-
0.0227	510	1.1871	-	-
0.0232	520	1.1685	-	-
0.0236	530	1.1624	-	-
0.0241	540	1.1911	-	-
0.0245	550	1.1978	-	-
0.0250	560	1.1228	-	-
0.0254	570	1.1091	-	-
0.0258	580	1.1433	-	-
0.0263	590	1.0638	-	-
0.0267	600	1.0515	-	-
0.0272	610	1.175	-	-
0.0276	620	1.0943	-	-
0.0281	630	1.1226	-	-
0.0285	640	0.9871	-	-
0.0290	650	1.0171	-	-
0.0294	660	1.0169	-	-
0.0299	670	0.9643	-	-
0.0303	680	0.9563	-	-
0.0307	690	0.9841	-	-
0.0312	700	1.0349	-	-
0.0316	710	0.8958	-	-
0.0321	720	0.9225	-	-
0.0325	730	0.842	-	-
0.0330	740	0.9104	-	-
0.0334	750	0.8927	-	-
0.0339	760	0.8508	-	-
0.0343	770	0.8835	-	-
0.0348	780	0.9531	-	-
0.0352	790	0.926	-	-
0.0356	800	0.8718	-	-
0.0361	810	0.8261	-	-
0.0365	820	0.8169	-	-
0.0370	830	0.8525	-	-
0.0374	840	0.8504	-	-
0.0379	850	0.7625	-	-
0.0383	860	0.8259	-	-
0.0388	870	0.7558	-	-
0.0392	880	0.7898	-	-
0.0397	890	0.7694	-	-
0.0401	900	0.7429	-	-
0.0405	910	0.6666	-	-
0.0410	920	0.7407	-	-
0.0414	930	0.6665	-	-
0.0419	940	0.7597	-	-
0.0423	950	0.7035	-	-
0.0428	960	0.7166	-	-
0.0432	970	0.6889	-	-
0.0437	980	0.7541	-	-
0.0441	990	0.7175	-	-
0.0446	1000	0.7389	0.6420	0.6403
0.0450	1010	0.7142	-	-
0.0454	1020	0.7301	-	-
0.0459	1030	0.7299	-	-
0.0463	1040	0.6759	-	-
0.0468	1050	0.7036	-	-
0.0472	1060	0.6286	-	-
0.0477	1070	0.595	-	-
0.0481	1080	0.6099	-	-
0.0486	1090	0.6377	-	-
0.0490	1100	0.6309	-	-
0.0495	1110	0.6306	-	-
0.0499	1120	0.557	-	-
0.0504	1130	0.5898	-	-
0.0508	1140	0.5896	-	-
0.0512	1150	0.6399	-	-
0.0517	1160	0.5923	-	-
0.0521	1170	0.5787	-	-
0.0526	1180	0.591	-	-
0.0530	1190	0.5714	-	-
0.0535	1200	0.6047	-	-
0.0539	1210	0.5904	-	-
0.0544	1220	0.543	-	-
0.0548	1230	0.6033	-	-
0.0553	1240	0.5445	-	-
0.0557	1250	0.5217	-	-
0.0561	1260	0.5835	-	-
0.0566	1270	0.5353	-	-
0.0570	1280	0.5887	-	-
0.0575	1290	0.5967	-	-
0.0579	1300	0.5036	-	-
0.0584	1310	0.5915	-	-
0.0588	1320	0.5719	-	-
0.0593	1330	0.5238	-	-
0.0597	1340	0.5647	-	-
0.0602	1350	0.538	-	-
0.0606	1360	0.5457	-	-
0.0610	1370	0.5169	-	-
0.0615	1380	0.4967	-	-
0.0619	1390	0.4864	-	-
0.0624	1400	0.5133	-	-
0.0628	1410	0.5587	-	-
0.0633	1420	0.4691	-	-
0.0637	1430	0.5186	-	-
0.0642	1440	0.4907	-	-
0.0646	1450	0.5281	-	-
0.0651	1460	0.4741	-	-
0.0655	1470	0.4452	-	-
0.0659	1480	0.4771	-	-
0.0664	1490	0.4289	-	-
0.0668	1500	0.4551	-	-
0.0673	1510	0.4558	-	-
0.0677	1520	0.5159	-	-
0.0682	1530	0.4296	-	-
0.0686	1540	0.4548	-	-
0.0691	1550	0.4439	-	-
0.0695	1560	0.4295	-	-
0.0700	1570	0.4466	-	-
0.0704	1580	0.4717	-	-
0.0708	1590	0.492	-	-
0.0713	1600	0.4566	-	-
0.0717	1610	0.4451	-	-
0.0722	1620	0.4715	-	-
0.0726	1630	0.4573	-	-
0.0731	1640	0.3972	-	-
0.0735	1650	0.5212	-	-
0.0740	1660	0.4381	-	-
0.0744	1670	0.4552	-	-
0.0749	1680	0.4767	-	-
0.0753	1690	0.4398	-	-
0.0757	1700	0.4801	-	-
0.0762	1710	0.3751	-	-
0.0766	1720	0.4407	-	-
0.0771	1730	0.4305	-	-
0.0775	1740	0.3938	-	-
0.0780	1750	0.4748	-	-
0.0784	1760	0.428	-	-
0.0789	1770	0.404	-	-
0.0793	1780	0.4261	-	-
0.0798	1790	0.359	-	-
0.0802	1800	0.4422	-	-
0.0807	1810	0.4748	-	-
0.0811	1820	0.4352	-	-
0.0815	1830	0.4032	-	-
0.0820	1840	0.4124	-	-
0.0824	1850	0.4486	-	-
0.0829	1860	0.429	-	-
0.0833	1870	0.4189	-	-
0.0838	1880	0.3658	-	-
0.0842	1890	0.4297	-	-
0.0847	1900	0.4215	-	-
0.0851	1910	0.3726	-	-
0.0856	1920	0.3736	-	-
0.0860	1930	0.4287	-	-
0.0864	1940	0.4402	-	-
0.0869	1950	0.4353	-	-
0.0873	1960	0.3622	-	-
0.0878	1970	0.3557	-	-
0.0882	1980	0.4107	-	-
0.0887	1990	0.3982	-	-
0.0891	2000	0.453	0.7292	0.7261
0.0896	2010	0.3971	-	-
0.0900	2020	0.4374	-	-
0.0905	2030	0.4322	-	-
0.0909	2040	0.3945	-	-
0.0913	2050	0.356	-	-
0.0918	2060	0.4182	-	-
0.0922	2070	0.3694	-	-
0.0927	2080	0.3989	-	-
0.0931	2090	0.4237	-	-
0.0936	2100	0.3961	-	-
0.0940	2110	0.4264	-	-
0.0945	2120	0.3609	-	-
0.0949	2130	0.4154	-	-
0.0954	2140	0.3661	-	-
0.0958	2150	0.3328	-	-
0.0962	2160	0.3456	-	-
0.0967	2170	0.3478	-	-
0.0971	2180	0.3339	-	-
0.0976	2190	0.3833	-	-
0.0980	2200	0.3238	-	-
0.0985	2210	0.3871	-	-
0.0989	2220	0.4009	-	-
0.0994	2230	0.4115	-	-
0.0998	2240	0.4024	-	-
0.1003	2250	0.35	-	-
0.1007	2260	0.3649	-	-
0.1011	2270	0.3615	-	-
0.1016	2280	0.3898	-	-
0.1020	2290	0.3866	-	-
0.1025	2300	0.3904	-	-
0.1029	2310	0.3321	-	-
0.1034	2320	0.3803	-	-
0.1038	2330	0.3831	-	-
0.1043	2340	0.403	-	-
0.1047	2350	0.3803	-	-
0.1052	2360	0.3463	-	-
0.1056	2370	0.3987	-	-
0.1060	2380	0.3731	-	-
0.1065	2390	0.353	-	-
0.1069	2400	0.3166	-	-
0.1074	2410	0.3895	-	-
0.1078	2420	0.4025	-	-
0.1083	2430	0.3798	-	-
0.1087	2440	0.2991	-	-
0.1092	2450	0.3094	-	-
0.1096	2460	0.3669	-	-
0.1101	2470	0.3412	-	-
0.1105	2480	0.3697	-	-
0.1110	2490	0.369	-	-
0.1114	2500	0.3393	-	-
0.1118	2510	0.4232	-	-
0.1123	2520	0.3445	-	-
0.1127	2530	0.4165	-	-
0.1132	2540	0.3721	-	-
0.1136	2550	0.3476	-	-
0.1141	2560	0.2847	-	-
0.1145	2570	0.3609	-	-
0.1150	2580	0.3017	-	-
0.1154	2590	0.374	-	-
0.1159	2600	0.3365	-	-
0.1163	2610	0.393	-	-
0.1167	2620	0.3623	-	-
0.1172	2630	0.3538	-	-
0.1176	2640	0.3206	-	-
0.1181	2650	0.3962	-	-
0.1185	2660	0.3087	-	-
0.1190	2670	0.3482	-	-
0.1194	2680	0.3616	-	-
0.1199	2690	0.3955	-	-
0.1203	2700	0.3915	-	-
0.1208	2710	0.3782	-	-
0.1212	2720	0.3576	-	-
0.1216	2730	0.3544	-	-
0.1221	2740	0.3572	-	-
0.1225	2750	0.3107	-	-
0.1230	2760	0.3579	-	-
0.1234	2770	0.3571	-	-
0.1239	2780	0.3694	-	-
0.1243	2790	0.3674	-	-
0.1248	2800	0.3373	-	-
0.1252	2810	0.3362	-	-
0.1257	2820	0.3225	-	-
0.1261	2830	0.3609	-	-
0.1265	2840	0.3681	-	-
0.1270	2850	0.4059	-	-
0.1274	2860	0.3047	-	-
0.1279	2870	0.3446	-	-
0.1283	2880	0.3507	-	-
0.1288	2890	0.3124	-	-
0.1292	2900	0.3712	-	-
0.1297	2910	0.3394	-	-
0.1301	2920	0.3869	-	-
0.1306	2930	0.3449	-	-
0.1310	2940	0.3752	-	-
0.1314	2950	0.3341	-	-
0.1319	2960	0.3329	-	-
0.1323	2970	0.36	-	-
0.1328	2980	0.3788	-	-
0.1332	2990	0.3834	-	-
0.1337	3000	0.3426	0.7603	0.7590
0.1341	3010	0.3591	-	-
0.1346	3020	0.2923	-	-
0.1350	3030	0.332	-	-
0.1355	3040	0.3867	-	-
0.1359	3050	0.3778	-	-
0.1363	3060	0.3389	-	-
0.1368	3070	0.3069	-	-
0.1372	3080	0.3833	-	-
0.1377	3090	0.3497	-	-
0.1381	3100	0.3698	-	-
0.1386	3110	0.335	-	-
0.1390	3120	0.3578	-	-
0.1395	3130	0.3171	-	-
0.1399	3140	0.3073	-	-
0.1404	3150	0.3354	-	-
0.1408	3160	0.3338	-	-
0.1412	3170	0.367	-	-
0.1417	3180	0.3299	-	-
0.1421	3190	0.3622	-	-
0.1426	3200	0.3158	-	-
0.1430	3210	0.3242	-	-
0.1435	3220	0.388	-	-
0.1439	3230	0.3626	-	-
0.1444	3240	0.3371	-	-
0.1448	3250	0.3808	-	-
0.1453	3260	0.3375	-	-
0.1457	3270	0.352	-	-
0.1462	3280	0.3466	-	-
0.1466	3290	0.3355	-	-
0.1470	3300	0.3432	-	-
0.1475	3310	0.372	-	-
0.1479	3320	0.3501	-	-
0.1484	3330	0.3311	-	-
0.1488	3340	0.3312	-	-
0.1493	3350	0.3276	-	-
0.1497	3360	0.3218	-	-
0.1502	3370	0.4019	-	-
0.1506	3380	0.3132	-	-
0.1511	3390	0.3741	-	-
0.1515	3400	0.3359	-	-
0.1519	3410	0.381	-	-
0.1524	3420	0.3024	-	-
0.1528	3430	0.3238	-	-
0.1533	3440	0.2675	-	-
0.1537	3450	0.3568	-	-
0.1542	3460	0.3666	-	-
0.1546	3470	0.3307	-	-
0.1551	3480	0.3698	-	-
0.1555	3490	0.3668	-	-
0.1560	3500	0.385	-	-
0.1564	3510	0.3068	-	-
0.1568	3520	0.3015	-	-
0.1573	3530	0.3604	-	-
0.1577	3540	0.3592	-	-
0.1582	3550	0.3483	-	-
0.1586	3560	0.3131	-	-
0.1591	3570	0.3738	-	-
0.1595	3580	0.3719	-	-
0.1600	3590	0.3409	-	-
0.1604	3600	0.4082	-	-
0.1609	3610	0.2881	-	-
0.1613	3620	0.3214	-	-
0.1617	3630	0.4413	-	-
0.1622	3640	0.3706	-	-
0.1626	3650	0.3643	-	-
0.1631	3660	0.3493	-	-
0.1635	3670	0.3877	-	-
0.1640	3680	0.3278	-	-
0.1644	3690	0.3211	-	-
0.1649	3700	0.4104	-	-
0.1653	3710	0.4558	-	-
0.1658	3720	0.3602	-	-
0.1662	3730	0.3348	-	-
0.1666	3740	0.2922	-	-
0.1671	3750	0.329	-	-
0.1675	3760	0.3507	-	-
0.1680	3770	0.2853	-	-
0.1684	3780	0.3556	-	-
0.1689	3790	0.3138	-	-
0.1693	3800	0.3536	-	-
0.1698	3810	0.3762	-	-
0.1702	3820	0.3262	-	-
0.1707	3830	0.3571	-	-
0.1711	3840	0.3455	-	-
0.1715	3850	0.3283	-	-
0.1720	3860	0.3317	-	-
0.1724	3870	0.2984	-	-
0.1729	3880	0.2659	-	-
0.1733	3890	0.2844	-	-
0.1738	3900	0.2999	-	-
0.1742	3910	0.2991	-	-
0.1747	3920	0.2667	-	-
0.1751	3930	0.3529	-	-
0.1756	3940	0.3767	-	-
0.1760	3950	0.3909	-	-
0.1765	3960	0.3393	-	-
0.1769	3970	0.2918	-	-
0.1773	3980	0.3363	-	-
0.1778	3990	0.3694	-	-
0.1782	4000	0.3	0.7572	0.7542
0.1787	4010	0.3266	-	-
0.1791	4020	0.3059	-	-
0.1796	4030	0.3038	-	-
0.1800	4040	0.3415	-	-
0.1805	4050	0.3385	-	-
0.1809	4060	0.3145	-	-
0.1814	4070	0.2816	-	-
0.1818	4080	0.3272	-	-
0.1822	4090	0.3335	-	-
0.1827	4100	0.3412	-	-
0.1831	4110	0.3367	-	-
0.1836	4120	0.2754	-	-
0.1840	4130	0.298	-	-
0.1845	4140	0.3252	-	-
0.1849	4150	0.3613	-	-
0.1854	4160	0.3197	-	-
0.1858	4170	0.3578	-	-
0.1863	4180	0.3254	-	-
0.1867	4190	0.2993	-	-
0.1871	4200	0.3188	-	-
0.1876	4210	0.3217	-	-
0.1880	4220	0.2893	-	-
0.1885	4230	0.3223	-	-
0.1889	4240	0.3522	-	-
0.1894	4250	0.3489	-	-
0.1898	4260	0.3313	-	-
0.1903	4270	0.3612	-	-
0.1907	4280	0.3323	-	-
0.1912	4290	0.2971	-	-
0.1916	4300	0.3009	-	-
0.1920	4310	0.3336	-	-
0.1925	4320	0.3655	-	-
0.1929	4330	0.3414	-	-
0.1934	4340	0.2903	-	-
0.1938	4350	0.3732	-	-
0.1943	4360	0.3526	-	-
0.1947	4370	0.3424	-	-
0.1952	4380	0.3371	-	-
0.1956	4390	0.3407	-	-
0.1961	4400	0.3626	-	-
0.1965	4410	0.3104	-	-
0.1969	4420	0.3432	-	-
0.1974	4430	0.2897	-	-
0.1978	4440	0.2952	-	-
0.1983	4450	0.3032	-	-
0.1987	4460	0.3179	-	-
0.1992	4470	0.3364	-	-
0.1996	4480	0.2757	-	-
0.2001	4490	0.3775	-	-
0.2005	4500	0.2782	-	-
0.2010	4510	0.2787	-	-
0.2014	4520	0.3433	-	-
0.2018	4530	0.3348	-	-
0.2023	4540	0.295	-	-
0.2027	4550	0.3076	-	-
0.2032	4560	0.3489	-	-
0.2036	4570	0.3741	-	-
0.2041	4580	0.3121	-	-
0.2045	4590	0.2682	-	-
0.2050	4600	0.3106	-	-
0.2054	4610	0.312	-	-
0.2059	4620	0.3537	-	-
0.2063	4630	0.2801	-	-
0.2068	4640	0.3378	-	-
0.2072	4650	0.3417	-	-
0.2076	4660	0.4114	-	-
0.2081	4670	0.3325	-	-
0.2085	4680	0.3085	-	-
0.2090	4690	0.2875	-	-
0.2094	4700	0.3864	-	-
0.2099	4710	0.3235	-	-
0.2103	4720	0.3187	-	-
0.2108	4730	0.2956	-	-
0.2112	4740	0.3405	-	-
0.2117	4750	0.313	-	-
0.2121	4760	0.2865	-	-
0.2125	4770	0.3555	-	-
0.2130	4780	0.3089	-	-
0.2134	4790	0.3021	-	-
0.2139	4800	0.353	-	-
0.2143	4810	0.3356	-	-
0.2148	4820	0.338	-	-
0.2152	4830	0.3362	-	-
0.2157	4840	0.3152	-	-
0.2161	4850	0.3321	-	-
0.2166	4860	0.3087	-	-
0.2170	4870	0.3503	-	-
0.2174	4880	0.3841	-	-
0.2179	4890	0.333	-	-
0.2183	4900	0.3705	-	-
0.2188	4910	0.3121	-	-
0.2192	4920	0.3151	-	-
0.2197	4930	0.3138	-	-
0.2201	4940	0.3525	-	-
0.2206	4950	0.3233	-	-
0.2210	4960	0.2762	-	-
0.2215	4970	0.3679	-	-
0.2219	4980	0.3351	-	-
0.2223	4990	0.3733	-	-
0.2228	5000	0.366	0.7601	0.7577
0.2232	5010	0.2968	-	-
0.2237	5020	0.3618	-	-
0.2241	5030	0.3758	-	-
0.2246	5040	0.2664	-	-
0.2250	5050	0.3232	-	-
0.2255	5060	0.3452	-	-
0.2259	5070	0.4011	-	-
0.2264	5080	0.3521	-	-
0.2268	5090	0.3029	-	-
0.2272	5100	0.3058	-	-
0.2277	5110	0.3198	-	-
0.2281	5120	0.2958	-	-
0.2286	5130	0.3046	-	-
0.2290	5140	0.3284	-	-
0.2295	5150	0.333	-	-
0.2299	5160	0.3385	-	-
0.2304	5170	0.3359	-	-
0.2308	5180	0.3572	-	-
0.2313	5190	0.2992	-	-
0.2317	5200	0.318	-	-
0.2321	5210	0.3002	-	-
0.2326	5220	0.3194	-	-
0.2330	5230	0.3398	-	-
0.2335	5240	0.2675	-	-
0.2339	5250	0.312	-	-
0.2344	5260	0.3199	-	-
0.2348	5270	0.3446	-	-
0.2353	5280	0.3082	-	-
0.2357	5290	0.3522	-	-
0.2362	5300	0.3347	-	-
0.2366	5310	0.3571	-	-
0.2371	5320	0.3275	-	-
0.2375	5330	0.3524	-	-
0.2379	5340	0.3151	-	-
0.2384	5350	0.3338	-	-
0.2388	5360	0.3794	-	-
0.2393	5370	0.3591	-	-
0.2397	5380	0.3442	-	-
0.2402	5390	0.2927	-	-
0.2406	5400	0.3316	-	-
0.2411	5410	0.3152	-	-
0.2415	5420	0.3876	-	-
0.2420	5430	0.324	-	-
0.2424	5440	0.3296	-	-
0.2428	5450	0.3499	-	-
0.2433	5460	0.3552	-	-
0.2437	5470	0.3394	-	-
0.2442	5480	0.3083	-	-
0.2446	5490	0.3198	-	-
0.2451	5500	0.2887	-	-
0.2455	5510	0.2898	-	-
0.2460	5520	0.3092	-	-
0.2464	5530	0.3025	-	-
0.2469	5540	0.3253	-	-
0.2473	5550	0.3686	-	-
0.2477	5560	0.3205	-	-
0.2482	5570	0.3507	-	-
0.2486	5580	0.2809	-	-
0.2491	5590	0.3339	-	-
0.2495	5600	0.3261	-	-
0.2500	5610	0.2804	-	-
0.2504	5620	0.2856	-	-
0.2509	5630	0.3211	-	-
0.2513	5640	0.3126	-	-
0.2518	5650	0.3374	-	-
0.2522	5660	0.2957	-	-
0.2526	5670	0.3414	-	-
0.2531	5680	0.3219	-	-
0.2535	5690	0.3554	-	-
0.2540	5700	0.2738	-	-
0.2544	5710	0.361	-	-
0.2549	5720	0.336	-	-
0.2553	5730	0.3254	-	-
0.2558	5740	0.3453	-	-
0.2562	5750	0.2984	-	-
0.2567	5760	0.3224	-	-
0.2571	5770	0.2553	-	-
0.2575	5780	0.301	-	-
0.2580	5790	0.3767	-	-
0.2584	5800	0.3092	-	-
0.2589	5810	0.2676	-	-
0.2593	5820	0.3178	-	-
0.2598	5830	0.3117	-	-
0.2602	5840	0.3446	-	-
0.2607	5850	0.3347	-	-
0.2611	5860	0.3841	-	-
0.2616	5870	0.2847	-	-
0.2620	5880	0.3587	-	-
0.2624	5890	0.2812	-	-
0.2629	5900	0.3577	-	-
0.2633	5910	0.3011	-	-
0.2638	5920	0.3102	-	-
0.2642	5930	0.3297	-	-
0.2647	5940	0.2603	-	-
0.2651	5950	0.3575	-	-
0.2656	5960	0.3617	-	-
0.2660	5970	0.3587	-	-
0.2665	5980	0.3198	-	-
0.2669	5990	0.3536	-	-
0.2673	6000	0.3047	0.7725	0.7699
0.2678	6010	0.3211	-	-
0.2682	6020	0.392	-	-
0.2687	6030	0.3359	-	-
0.2691	6040	0.2903	-	-
0.2696	6050	0.286	-	-
0.2700	6060	0.3426	-	-
0.2705	6070	0.3406	-	-
0.2709	6080	0.2903	-	-
0.2714	6090	0.3175	-	-
0.2718	6100	0.2794	-	-
0.2723	6110	0.3232	-	-
0.2727	6120	0.3054	-	-
0.2731	6130	0.361	-	-
0.2736	6140	0.3524	-	-
0.2740	6150	0.3371	-	-
0.2745	6160	0.313	-	-
0.2749	6170	0.2713	-	-
0.2754	6180	0.3141	-	-
0.2758	6190	0.3197	-	-
0.2763	6200	0.2792	-	-
0.2767	6210	0.3169	-	-
0.2772	6220	0.307	-	-
0.2776	6230	0.2737	-	-
0.2780	6240	0.3348	-	-
0.2785	6250	0.2885	-	-
0.2789	6260	0.3416	-	-
0.2794	6270	0.3422	-	-
0.2798	6280	0.2758	-	-
0.2803	6290	0.3736	-	-
0.2807	6300	0.3036	-	-
0.2812	6310	0.3704	-	-
0.2816	6320	0.3312	-	-
0.2821	6330	0.3431	-	-
0.2825	6340	0.3502	-	-
0.2829	6350	0.2821	-	-
0.2834	6360	0.3097	-	-
0.2838	6370	0.3444	-	-
0.2843	6380	0.3349	-	-
0.2847	6390	0.2999	-	-
0.2852	6400	0.3149	-	-
0.2856	6410	0.3462	-	-
0.2861	6420	0.3337	-	-
0.2865	6430	0.3329	-	-
0.2870	6440	0.3294	-	-
0.2874	6450	0.2917	-	-
0.2878	6460	0.3007	-	-
0.2883	6470	0.2809	-	-
0.2887	6480	0.3745	-	-
0.2892	6490	0.3625	-	-
0.2896	6500	0.3123	-	-
0.2901	6510	0.3209	-	-
0.2905	6520	0.347	-	-
0.2910	6530	0.3084	-	-
0.2914	6540	0.2829	-	-
0.2919	6550	0.3569	-	-
0.2923	6560	0.2686	-	-
0.2927	6570	0.2929	-	-
0.2932	6580	0.3237	-	-
0.2936	6590	0.3451	-	-
0.2941	6600	0.3199	-	-
0.2945	6610	0.2848	-	-
0.2950	6620	0.2842	-	-
0.2954	6630	0.3168	-	-
0.2959	6640	0.3094	-	-
0.2963	6650	0.3239	-	-
0.2968	6660	0.357	-	-
0.2972	6670	0.3279	-	-
0.2976	6680	0.4015	-	-
0.2981	6690	0.2901	-	-
0.2985	6700	0.3387	-	-
0.2990	6710	0.3282	-	-
0.2994	6720	0.2909	-	-
0.2999	6730	0.3556	-	-
0.3003	6740	0.3008	-	-
0.3008	6750	0.3205	-	-
0.3012	6760	0.3132	-	-
0.3017	6770	0.3181	-	-
0.3021	6780	0.3752	-	-
0.3026	6790	0.317	-	-
0.3030	6800	0.3584	-	-
0.3034	6810	0.3475	-	-
0.3039	6820	0.2827	-	-
0.3043	6830	0.2925	-	-
0.3048	6840	0.2941	-	-
0.3052	6850	0.3154	-	-
0.3057	6860	0.3301	-	-
0.3061	6870	0.3492	-	-
0.3066	6880	0.3147	-	-
0.3070	6890	0.348	-	-
0.3075	6900	0.3577	-	-
0.3079	6910	0.2893	-	-
0.3083	6920	0.3298	-	-
0.3088	6930	0.3071	-	-
0.3092	6940	0.322	-	-
0.3097	6950	0.3055	-	-
0.3101	6960	0.3333	-	-
0.3106	6970	0.3329	-	-
0.3110	6980	0.3298	-	-
0.3115	6990	0.3061	-	-
0.3119	7000	0.3005	0.7686	0.7672
0.3124	7010	0.3463	-	-
0.3128	7020	0.3467	-	-
0.3132	7030	0.3104	-	-
0.3137	7040	0.3268	-	-
0.3141	7050	0.3222	-	-
0.3146	7060	0.3126	-	-
0.3150	7070	0.3121	-	-
0.3155	7080	0.2935	-	-
0.3159	7090	0.2897	-	-
0.3164	7100	0.3066	-	-
0.3168	7110	0.3363	-	-
0.3173	7120	0.3293	-	-
0.3177	7130	0.3161	-	-
0.3181	7140	0.3582	-	-
0.3186	7150	0.3345	-	-
0.3190	7160	0.3307	-	-
0.3195	7170	0.3269	-	-
0.3199	7180	0.3262	-	-
0.3204	7190	0.3115	-	-
0.3208	7200	0.3145	-	-
0.3213	7210	0.2816	-	-
0.3217	7220	0.3239	-	-
0.3222	7230	0.2825	-	-
0.3226	7240	0.3217	-	-
0.3230	7250	0.2913	-	-
0.3235	7260	0.3219	-	-
0.3239	7270	0.2968	-	-
0.3244	7280	0.2999	-	-
0.3248	7290	0.2924	-	-
0.3253	7300	0.3033	-	-
0.3257	7310	0.3521	-	-
0.3262	7320	0.3258	-	-
0.3266	7330	0.3724	-	-
0.3271	7340	0.3068	-	-
0.3275	7350	0.3095	-	-
0.3279	7360	0.2957	-	-
0.3284	7370	0.2741	-	-
0.3288	7380	0.3183	-	-
0.3293	7390	0.3409	-	-
0.3297	7400	0.3066	-	-
0.3302	7410	0.3139	-	-
0.3306	7420	0.3639	-	-
0.3311	7430	0.3333	-	-
0.3315	7440	0.276	-	-
0.3320	7450	0.3326	-	-
0.3324	7460	0.3239	-	-
0.3329	7470	0.3067	-	-
0.3333	7480	0.3213	-	-
0.3337	7490	0.3227	-	-
0.3342	7500	0.3027	-	-
0.3346	7510	0.3017	-	-
0.3351	7520	0.2797	-	-
0.3355	7530	0.3215	-	-
0.3360	7540	0.2713	-	-
0.3364	7550	0.3071	-	-
0.3369	7560	0.309	-	-
0.3373	7570	0.3145	-	-
0.3378	7580	0.2694	-	-
0.3382	7590	0.3036	-	-
0.3386	7600	0.2892	-	-
0.3391	7610	0.3227	-	-
0.3395	7620	0.3373	-	-
0.3400	7630	0.2584	-	-
0.3404	7640	0.232	-	-
0.3409	7650	0.311	-	-
0.3413	7660	0.3536	-	-
0.3418	7670	0.3279	-	-
0.3422	7680	0.3034	-	-
0.3427	7690	0.2916	-	-
0.3431	7700	0.2822	-	-
0.3435	7710	0.2871	-	-
0.3440	7720	0.3284	-	-
0.3444	7730	0.2909	-	-
0.3449	7740	0.3292	-	-
0.3453	7750	0.3393	-	-
0.3458	7760	0.2838	-	-
0.3462	7770	0.2686	-	-
0.3467	7780	0.318	-	-
0.3471	7790	0.3335	-	-
0.3476	7800	0.3017	-	-
0.3480	7810	0.2595	-	-
0.3484	7820	0.3008	-	-
0.3489	7830	0.2726	-	-
0.3493	7840	0.2938	-	-
0.3498	7850	0.2923	-	-
0.3502	7860	0.361	-	-
0.3507	7870	0.2689	-	-
0.3511	7880	0.3014	-	-
0.3516	7890	0.3169	-	-
0.3520	7900	0.3124	-	-
0.3525	7910	0.3367	-	-
0.3529	7920	0.276	-	-
0.3533	7930	0.3556	-	-
0.3538	7940	0.3036	-	-
0.3542	7950	0.2983	-	-
0.3547	7960	0.3393	-	-
0.3551	7970	0.3688	-	-
0.3556	7980	0.3391	-	-
0.3560	7990	0.3432	-	-
0.3565	8000	0.3061	0.7543	0.7526
0.3569	8010	0.293	-	-
0.3574	8020	0.2925	-	-
0.3578	8030	0.2852	-	-
0.3582	8040	0.396	-	-
0.3587	8050	0.2927	-	-
0.3591	8060	0.3028	-	-
0.3596	8070	0.3102	-	-
0.3600	8080	0.328	-	-
0.3605	8090	0.3194	-	-
0.3609	8100	0.2808	-	-
0.3614	8110	0.292	-	-
0.3618	8120	0.3232	-	-
0.3623	8130	0.3629	-	-
0.3627	8140	0.3222	-	-
0.3632	8150	0.3691	-	-
0.3636	8160	0.2965	-	-
0.3640	8170	0.293	-	-
0.3645	8180	0.3166	-	-
0.3649	8190	0.3021	-	-
0.3654	8200	0.2815	-	-
0.3658	8210	0.3089	-	-
0.3663	8220	0.2804	-	-
0.3667	8230	0.3011	-	-
0.3672	8240	0.27	-	-
0.3676	8250	0.361	-	-
0.3681	8260	0.3322	-	-
0.3685	8270	0.2741	-	-
0.3689	8280	0.3207	-	-
0.3694	8290	0.3437	-	-
0.3698	8300	0.3259	-	-
0.3703	8310	0.2473	-	-
0.3707	8320	0.2321	-	-
0.3712	8330	0.2699	-	-
0.3716	8340	0.2404	-	-
0.3721	8350	0.2586	-	-
0.3725	8360	0.295	-	-
0.3730	8370	0.3063	-	-
0.3734	8380	0.2551	-	-
0.3738	8390	0.2562	-	-
0.3743	8400	0.3062	-	-
0.3747	8410	0.3165	-	-
0.3752	8420	0.308	-	-
0.3756	8430	0.2976	-	-
0.3761	8440	0.284	-	-
0.3765	8450	0.3525	-	-
0.3770	8460	0.2639	-	-
0.3774	8470	0.3171	-	-
0.3779	8480	0.3367	-	-
0.3783	8490	0.2801	-	-
0.3787	8500	0.2957	-	-
0.3792	8510	0.3684	-	-
0.3796	8520	0.312	-	-
0.3801	8530	0.3703	-	-
0.3805	8540	0.2963	-	-
0.3810	8550	0.3032	-	-
0.3814	8560	0.3415	-	-
0.3819	8570	0.3011	-	-
0.3823	8580	0.33	-	-
0.3828	8590	0.2763	-	-
0.3832	8600	0.3295	-	-
0.3836	8610	0.3334	-	-
0.3841	8620	0.258	-	-
0.3845	8630	0.2626	-	-
0.3850	8640	0.2813	-	-
0.3854	8650	0.2845	-	-
0.3859	8660	0.2719	-	-
0.3863	8670	0.2898	-	-
0.3868	8680	0.3011	-	-
0.3872	8690	0.2914	-	-
0.3877	8700	0.3355	-	-
0.3881	8710	0.2678	-	-
0.3885	8720	0.2266	-	-
0.3890	8730	0.3016	-	-
0.3894	8740	0.3369	-	-
0.3899	8750	0.3558	-	-
0.3903	8760	0.2824	-	-
0.3908	8770	0.3201	-	-
0.3912	8780	0.2485	-	-
0.3917	8790	0.2603	-	-
0.3921	8800	0.3223	-	-
0.3926	8810	0.247	-	-
0.3930	8820	0.2766	-	-
0.3934	8830	0.3231	-	-
0.3939	8840	0.322	-	-
0.3943	8850	0.3039	-	-
0.3948	8860	0.2442	-	-
0.3952	8870	0.36	-	-
0.3957	8880	0.2551	-	-
0.3961	8890	0.2661	-	-
0.3966	8900	0.3001	-	-
0.3970	8910	0.2886	-	-
0.3975	8920	0.2856	-	-
0.3979	8930	0.2827	-	-
0.3984	8940	0.2652	-	-
0.3988	8950	0.3077	-	-
0.3992	8960	0.3094	-	-
0.3997	8970	0.3281	-	-
0.4001	8980	0.3399	-	-
0.4006	8990	0.3093	-	-
0.4010	9000	0.2586	0.7634	0.7607
0.4015	9010	0.2939	-	-
0.4019	9020	0.3022	-	-
0.4024	9030	0.2919	-	-
0.4028	9040	0.2524	-	-
0.4033	9050	0.2248	-	-
0.4037	9060	0.2759	-	-
0.4041	9070	0.2916	-	-
0.4046	9080	0.3006	-	-
0.4050	9090	0.2302	-	-
0.4055	9100	0.3001	-	-
0.4059	9110	0.3143	-	-
0.4064	9120	0.2544	-	-
0.4068	9130	0.3142	-	-
0.4073	9140	0.3364	-	-
0.4077	9150	0.2785	-	-
0.4082	9160	0.2948	-	-
0.4086	9170	0.2657	-	-
0.4090	9180	0.2722	-	-
0.4095	9190	0.3212	-	-
0.4099	9200	0.2952	-	-
0.4104	9210	0.2764	-	-
0.4108	9220	0.2744	-	-
0.4113	9230	0.2912	-	-
0.4117	9240	0.2676	-	-
0.4122	9250	0.2613	-	-
0.4126	9260	0.2905	-	-
0.4131	9270	0.3308	-	-
0.4135	9280	0.3311	-	-
0.4139	9290	0.2904	-	-
0.4144	9300	0.3367	-	-
0.4148	9310	0.2742	-	-
0.4153	9320	0.295	-	-
0.4157	9330	0.3034	-	-
0.4162	9340	0.3302	-	-
0.4166	9350	0.2883	-	-
0.4171	9360	0.2768	-	-
0.4175	9370	0.2953	-	-
0.4180	9380	0.3196	-	-
0.4184	9390	0.2731	-	-
0.4188	9400	0.3016	-	-
0.4193	9410	0.3325	-	-
0.4197	9420	0.2503	-	-
0.4202	9430	0.273	-	-
0.4206	9440	0.2784	-	-
0.4211	9450	0.2676	-	-
0.4215	9460	0.2891	-	-
0.4220	9470	0.2977	-	-
0.4224	9480	0.2673	-	-
0.4229	9490	0.2845	-	-
0.4233	9500	0.2825	-	-
0.4237	9510	0.2865	-	-
0.4242	9520	0.2451	-	-
0.4246	9530	0.2806	-	-
0.4251	9540	0.2629	-	-
0.4255	9550	0.3426	-	-
0.4260	9560	0.2453	-	-
0.4264	9570	0.3458	-	-
0.4269	9580	0.2392	-	-
0.4273	9590	0.2433	-	-
0.4278	9600	0.2481	-	-
0.4282	9610	0.3277	-	-
0.4287	9620	0.2609	-	-
0.4291	9630	0.2986	-	-
0.4295	9640	0.2712	-	-
0.4300	9650	0.3169	-	-
0.4304	9660	0.2638	-	-
0.4309	9670	0.2821	-	-
0.4313	9680	0.2969	-	-
0.4318	9690	0.2727	-	-
0.4322	9700	0.2858	-	-
0.4327	9710	0.2988	-	-
0.4331	9720	0.2628	-	-
0.4336	9730	0.3027	-	-
0.4340	9740	0.2502	-	-
0.4344	9750	0.3028	-	-
0.4349	9760	0.2381	-	-
0.4353	9770	0.2981	-	-
0.4358	9780	0.2208	-	-
0.4362	9790	0.2433	-	-
0.4367	9800	0.2672	-	-
0.4371	9810	0.3147	-	-
0.4376	9820	0.2655	-	-
0.4380	9830	0.273	-	-
0.4385	9840	0.3505	-	-
0.4389	9850	0.2822	-	-
0.4393	9860	0.2682	-	-
0.4398	9870	0.294	-	-
0.4402	9880	0.3002	-	-
0.4407	9890	0.2514	-	-
0.4411	9900	0.3193	-	-
0.4416	9910	0.2296	-	-
0.4420	9920	0.2209	-	-
0.4425	9930	0.2961	-	-
0.4429	9940	0.297	-	-
0.4434	9950	0.2734	-	-
0.4438	9960	0.2806	-	-
0.4442	9970	0.2634	-	-
0.4447	9980	0.3131	-	-
0.4451	9990	0.3007	-	-
0.4456	10000	0.3299	0.7687	0.7657

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.0
Transformers: 4.46.2
PyTorch: 2.1.0+cu118
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

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}
}