This is a sentence-transformers model finetuned from microsoft/mpnet-base on the all-nli dataset. 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: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("nadshe/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
    'A dog is swimming.',
    'A dog with yellow fur swims, neck deep, in water.',
    'A white dog with a stick in his mouth standing next to a black dog.',
]
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

Information Retrieval

Dataset: SciFact
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.2067
cosine_accuracy@3	0.32
cosine_accuracy@5	0.3567
cosine_accuracy@10	0.4467
cosine_precision@20	0.0297
cosine_precision@100	0.0081
cosine_recall@20	0.5323
cosine_recall@100	0.7159
cosine_ndcg@5	0.2783
cosine_ndcg@10	0.306
cosine_mrr@10	0.2771
cosine_map@100	0.2776

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9275

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 100,000 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: 10.46 tokens
max: 46 tokens
min: 6 tokens
mean: 12.81 tokens
max: 40 tokens
min: 5 tokens
mean: 13.4 tokens
max: 50 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
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
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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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	SciFact_cosine_ndcg@10	all-nli-test_cosine_accuracy
0.0008	5	-	0.0808	-
0.0016	10	-	0.0808	-
0.0024	15	-	0.0808	-
0.0032	20	-	0.0809	-
0.004	25	-	0.0812	-
0.0048	30	-	0.0842	-
0.0056	35	-	0.0845	-
0.0064	40	-	0.0892	-
0.0072	45	-	0.0921	-
0.008	50	-	0.0963	-
0.0088	55	-	0.1006	-
0.0096	60	-	0.1050	-
0.0104	65	-	0.1098	-
0.0112	70	-	0.1108	-
0.012	75	-	0.1169	-
0.0128	80	-	0.1291	-
0.0136	85	-	0.1394	-
0.0144	90	-	0.1586	-
0.0152	95	-	0.1747	-
0.016	100	3.1887	0.1873	-
0.0168	105	-	0.1964	-
0.0176	110	-	0.2090	-
0.0184	115	-	0.2237	-
0.0192	120	-	0.2320	-
0.02	125	-	0.2380	-
0.0208	130	-	0.2433	-
0.0216	135	-	0.2513	-
0.0224	140	-	0.2553	-
0.0232	145	-	0.2615	-
0.024	150	-	0.2677	-
0.0248	155	-	0.2718	-
0.0256	160	-	0.2763	-
0.0264	165	-	0.2793	-
0.0272	170	-	0.2838	-
0.028	175	-	0.2883	-
0.0288	180	-	0.2939	-
0.0296	185	-	0.2965	-
0.0304	190	-	0.2998	-
0.0312	195	-	0.3050	-
0.032	200	1.5596	0.3083	-
0.0328	205	-	0.3118	-
0.0336	210	-	0.3177	-
0.0344	215	-	0.3184	-
0.0352	220	-	0.3211	-
0.036	225	-	0.3196	-
0.0368	230	-	0.3216	-
0.0376	235	-	0.3220	-
0.0384	240	-	0.3203	-
0.0392	245	-	0.3233	-
0.04	250	-	0.3228	-
0.0408	255	-	0.3307	-
0.0416	260	-	0.3321	-
0.0424	265	-	0.3357	-
0.0432	270	-	0.3318	-
0.044	275	-	0.3296	-
0.0448	280	-	0.3277	-
0.0456	285	-	0.3316	-
0.0464	290	-	0.3283	-
0.0472	295	-	0.3287	-
0.048	300	1.096	0.3248	-
0.0488	305	-	0.3228	-
0.0496	310	-	0.3227	-
0.0504	315	-	0.3207	-
0.0512	320	-	0.3244	-
0.052	325	-	0.3315	-
0.0528	330	-	0.3342	-
0.0536	335	-	0.3334	-
0.0544	340	-	0.3381	-
0.0552	345	-	0.3380	-
0.056	350	-	0.3464	-
0.0568	355	-	0.3449	-
0.0576	360	-	0.3453	-
0.0584	365	-	0.3423	-
0.0592	370	-	0.3393	-
0.06	375	-	0.3401	-
0.0608	380	-	0.3404	-
0.0616	385	-	0.3416	-
0.0624	390	-	0.3385	-
0.0632	395	-	0.3378	-
0.064	400	1.0	0.3375	-
0.0648	405	-	0.3397	-
0.0656	410	-	0.3362	-
0.0664	415	-	0.3305	-
0.0672	420	-	0.3311	-
0.068	425	-	0.3335	-
0.0688	430	-	0.3367	-
0.0696	435	-	0.3341	-
0.0704	440	-	0.3340	-
0.0712	445	-	0.3351	-
0.072	450	-	0.3373	-
0.0728	455	-	0.3420	-
0.0736	460	-	0.3426	-
0.0744	465	-	0.3410	-
0.0752	470	-	0.3385	-
0.076	475	-	0.3351	-
0.0768	480	-	0.3343	-
0.0776	485	-	0.3365	-
0.0784	490	-	0.3369	-
0.0792	495	-	0.3358	-
0.08	500	0.9284	0.3419	-
0.0808	505	-	0.3440	-
0.0816	510	-	0.3463	-
0.0824	515	-	0.3500	-
0.0832	520	-	0.3520	-
0.084	525	-	0.3555	-
0.0848	530	-	0.3635	-
0.0856	535	-	0.3604	-
0.0864	540	-	0.3620	-
0.0872	545	-	0.3567	-
0.088	550	-	0.3612	-
0.0888	555	-	0.3644	-
0.0896	560	-	0.3684	-
0.0904	565	-	0.3709	-
0.0912	570	-	0.3704	-
0.092	575	-	0.3706	-
0.0928	580	-	0.3716	-
0.0936	585	-	0.3699	-
0.0944	590	-	0.3656	-
0.0952	595	-	0.3662	-
0.096	600	0.8817	0.3647	-
0.0968	605	-	0.3627	-
0.0976	610	-	0.3661	-
0.0984	615	-	0.3755	-
0.0992	620	-	0.3722	-
0.1	625	-	0.3733	-
0.1008	630	-	0.3737	-
0.1016	635	-	0.3653	-
0.1024	640	-	0.3701	-
0.1032	645	-	0.3648	-
0.104	650	-	0.3609	-
0.1048	655	-	0.3570	-
0.1056	660	-	0.3537	-
0.1064	665	-	0.3513	-
0.1072	670	-	0.3595	-
0.108	675	-	0.3689	-
0.1088	680	-	0.3623	-
0.1096	685	-	0.3611	-
0.1104	690	-	0.3703	-
0.1112	695	-	0.3661	-
0.112	700	0.8336	0.3710	-
0.1128	705	-	0.3669	-
0.1136	710	-	0.3678	-
0.1144	715	-	0.3645	-
0.1152	720	-	0.3672	-
0.116	725	-	0.3612	-
0.1168	730	-	0.3633	-
0.1176	735	-	0.3590	-
0.1184	740	-	0.3548	-
0.1192	745	-	0.3446	-
0.12	750	-	0.3410	-
0.1208	755	-	0.3354	-
0.1216	760	-	0.3361	-
0.1224	765	-	0.3320	-
0.1232	770	-	0.3335	-
0.124	775	-	0.3335	-
0.1248	780	-	0.3388	-
0.1256	785	-	0.3351	-
0.1264	790	-	0.3259	-
0.1272	795	-	0.3235	-
0.128	800	0.8156	0.3217	-
0.1288	805	-	0.3264	-
0.1296	810	-	0.3278	-
0.1304	815	-	0.3243	-
0.1312	820	-	0.3256	-
0.132	825	-	0.3334	-
0.1328	830	-	0.3288	-
0.1336	835	-	0.3289	-
0.1344	840	-	0.3312	-
0.1352	845	-	0.3267	-
0.136	850	-	0.3263	-
0.1368	855	-	0.3285	-
0.1376	860	-	0.3285	-
0.1384	865	-	0.3330	-
0.1392	870	-	0.3367	-
0.14	875	-	0.3331	-
0.1408	880	-	0.3351	-
0.1416	885	-	0.3318	-
0.1424	890	-	0.3248	-
0.1432	895	-	0.3162	-
0.144	900	0.7558	0.3186	-
0.1448	905	-	0.3173	-
0.1456	910	-	0.3148	-
0.1464	915	-	0.3147	-
0.1472	920	-	0.3178	-
0.148	925	-	0.3205	-
0.1488	930	-	0.3262	-
0.1496	935	-	0.3231	-
0.1504	940	-	0.3230	-
0.1512	945	-	0.3143	-
0.152	950	-	0.3078	-
0.1528	955	-	0.3119	-
0.1536	960	-	0.3179	-
0.1544	965	-	0.3198	-
0.1552	970	-	0.3164	-
0.156	975	-	0.3114	-
0.1568	980	-	0.3137	-
0.1576	985	-	0.3083	-
0.1584	990	-	0.3077	-
0.1592	995	-	0.3117	-
0.16	1000	0.6718	0.3142	-
0.1608	1005	-	0.3125	-
0.1616	1010	-	0.3177	-
0.1624	1015	-	0.3171	-
0.1632	1020	-	0.3193	-
0.164	1025	-	0.3193	-
0.1648	1030	-	0.3200	-
0.1656	1035	-	0.3267	-
0.1664	1040	-	0.3259	-
0.1672	1045	-	0.3247	-
0.168	1050	-	0.3176	-
0.1688	1055	-	0.3135	-
0.1696	1060	-	0.3118	-
0.1704	1065	-	0.3102	-
0.1712	1070	-	0.3117	-
0.172	1075	-	0.3122	-
0.1728	1080	-	0.3073	-
0.1736	1085	-	0.3113	-
0.1744	1090	-	0.3065	-
0.1752	1095	-	0.3115	-
0.176	1100	0.7212	0.3157	-
0.1768	1105	-	0.3182	-
0.1776	1110	-	0.3198	-
0.1784	1115	-	0.3193	-
0.1792	1120	-	0.3197	-
0.18	1125	-	0.3192	-
0.1808	1130	-	0.3226	-
0.1816	1135	-	0.3247	-
0.1824	1140	-	0.3295	-
0.1832	1145	-	0.3266	-
0.184	1150	-	0.3265	-
0.1848	1155	-	0.3202	-
0.1856	1160	-	0.3207	-
0.1864	1165	-	0.3217	-
0.1872	1170	-	0.3184	-
0.188	1175	-	0.3216	-
0.1888	1180	-	0.3173	-
0.1896	1185	-	0.3197	-
0.1904	1190	-	0.3237	-
0.1912	1195	-	0.3198	-
0.192	1200	0.6501	0.3241	-
0.1928	1205	-	0.3215	-
0.1936	1210	-	0.3176	-
0.1944	1215	-	0.3019	-
0.1952	1220	-	0.2993	-
0.196	1225	-	0.2910	-
0.1968	1230	-	0.2743	-
0.1976	1235	-	0.2801	-
0.1984	1240	-	0.2953	-
0.1992	1245	-	0.2999	-
0.2	1250	-	0.2983	-
0.2008	1255	-	0.2993	-
0.2016	1260	-	0.3001	-
0.2024	1265	-	0.3038	-
0.2032	1270	-	0.3001	-
0.204	1275	-	0.2955	-
0.2048	1280	-	0.2902	-
0.2056	1285	-	0.2897	-
0.2064	1290	-	0.2913	-
0.2072	1295	-	0.2909	-
0.208	1300	0.6612	0.2796	-
0.2088	1305	-	0.2842	-
0.2096	1310	-	0.2841	-
0.2104	1315	-	0.2853	-
0.2112	1320	-	0.2912	-
0.212	1325	-	0.2932	-
0.2128	1330	-	0.2913	-
0.2136	1335	-	0.2950	-
0.2144	1340	-	0.2989	-
0.2152	1345	-	0.3037	-
0.216	1350	-	0.3063	-
0.2168	1355	-	0.3067	-
0.2176	1360	-	0.3024	-
0.2184	1365	-	0.3094	-
0.2192	1370	-	0.3065	-
0.22	1375	-	0.3071	-
0.2208	1380	-	0.3089	-
0.2216	1385	-	0.3133	-
0.2224	1390	-	0.3154	-
0.2232	1395	-	0.3167	-
0.224	1400	0.6329	0.3255	-
0.2248	1405	-	0.3219	-
0.2256	1410	-	0.3139	-
0.2264	1415	-	0.3211	-
0.2272	1420	-	0.3285	-
0.228	1425	-	0.3220	-
0.2288	1430	-	0.3133	-
0.2296	1435	-	0.3126	-
0.2304	1440	-	0.3120	-
0.2312	1445	-	0.3134	-
0.232	1450	-	0.3094	-
0.2328	1455	-	0.3065	-
0.2336	1460	-	0.3119	-
0.2344	1465	-	0.3121	-
0.2352	1470	-	0.3111	-
0.236	1475	-	0.3033	-
0.2368	1480	-	0.2948	-
0.2376	1485	-	0.2926	-
0.2384	1490	-	0.2841	-
0.2392	1495	-	0.2888	-
0.24	1500	0.6895	0.2879	-
0.2408	1505	-	0.2889	-
0.2416	1510	-	0.2943	-
0.2424	1515	-	0.2967	-
0.2432	1520	-	0.2939	-
0.244	1525	-	0.2963	-
0.2448	1530	-	0.2963	-
0.2456	1535	-	0.2967	-
0.2464	1540	-	0.2930	-
0.2472	1545	-	0.2880	-
0.248	1550	-	0.2810	-
0.2488	1555	-	0.2744	-
0.2496	1560	-	0.2779	-
0.2504	1565	-	0.2826	-
0.2512	1570	-	0.2810	-
0.252	1575	-	0.2865	-
0.2528	1580	-	0.2850	-
0.2536	1585	-	0.2894	-
0.2544	1590	-	0.2849	-
0.2552	1595	-	0.2839	-
0.256	1600	0.6213	0.2860	-
0.2568	1605	-	0.2890	-
0.2576	1610	-	0.2899	-
0.2584	1615	-	0.2927	-
0.2592	1620	-	0.2905	-
0.26	1625	-	0.2890	-
0.2608	1630	-	0.2931	-
0.2616	1635	-	0.2947	-
0.2624	1640	-	0.2915	-
0.2632	1645	-	0.2982	-
0.264	1650	-	0.3012	-
0.2648	1655	-	0.3043	-
0.2656	1660	-	0.3034	-
0.2664	1665	-	0.3003	-
0.2672	1670	-	0.2960	-
0.268	1675	-	0.2938	-
0.2688	1680	-	0.2892	-
0.2696	1685	-	0.2846	-
0.2704	1690	-	0.2855	-
0.2712	1695	-	0.2871	-
0.272	1700	0.6172	0.2957	-
0.2728	1705	-	0.3019	-
0.2736	1710	-	0.3011	-
0.2744	1715	-	0.3008	-
0.2752	1720	-	0.2987	-
0.276	1725	-	0.2989	-
0.2768	1730	-	0.3011	-
0.2776	1735	-	0.3032	-
0.2784	1740	-	0.3032	-
0.2792	1745	-	0.2986	-
0.28	1750	-	0.2989	-
0.2808	1755	-	0.2958	-
0.2816	1760	-	0.2974	-
0.2824	1765	-	0.2997	-
0.2832	1770	-	0.3005	-
0.284	1775	-	0.2919	-
0.2848	1780	-	0.2974	-
0.2856	1785	-	0.2990	-
0.2864	1790	-	0.3031	-
0.2872	1795	-	0.3066	-
0.288	1800	0.5874	0.3120	-
0.2888	1805	-	0.3076	-
0.2896	1810	-	0.3066	-
0.2904	1815	-	0.3083	-
0.2912	1820	-	0.3068	-
0.292	1825	-	0.3132	-
0.2928	1830	-	0.3157	-
0.2936	1835	-	0.3158	-
0.2944	1840	-	0.3179	-
0.2952	1845	-	0.3211	-
0.296	1850	-	0.3176	-
0.2968	1855	-	0.3198	-
0.2976	1860	-	0.3252	-
0.2984	1865	-	0.3277	-
0.2992	1870	-	0.3303	-
0.3	1875	-	0.3313	-
0.3008	1880	-	0.3302	-
0.3016	1885	-	0.3351	-
0.3024	1890	-	0.3348	-
0.3032	1895	-	0.3322	-
0.304	1900	0.548	0.3203	-
0.3048	1905	-	0.3118	-
0.3056	1910	-	0.3070	-
0.3064	1915	-	0.3054	-
0.3072	1920	-	0.3030	-
0.308	1925	-	0.3041	-
0.3088	1930	-	0.3064	-
0.3096	1935	-	0.3056	-
0.3104	1940	-	0.3055	-
0.3112	1945	-	0.3066	-
0.312	1950	-	0.3042	-
0.3128	1955	-	0.3055	-
0.3136	1960	-	0.3017	-
0.3144	1965	-	0.3018	-
0.3152	1970	-	0.3059	-
0.316	1975	-	0.3081	-
0.3168	1980	-	0.3073	-
0.3176	1985	-	0.3044	-
0.3184	1990	-	0.3022	-
0.3192	1995	-	0.3014	-
0.32	2000	0.5164	0.2995	-
0.3208	2005	-	0.2968	-
0.3216	2010	-	0.2942	-
0.3224	2015	-	0.2948	-
0.3232	2020	-	0.2946	-
0.324	2025	-	0.2957	-
0.3248	2030	-	0.2884	-
0.3256	2035	-	0.2874	-
0.3264	2040	-	0.2874	-
0.3272	2045	-	0.2901	-
0.328	2050	-	0.2865	-
0.3288	2055	-	0.2840	-
0.3296	2060	-	0.2933	-
0.3304	2065	-	0.3032	-
0.3312	2070	-	0.3053	-
0.332	2075	-	0.3062	-
0.3328	2080	-	0.3075	-
0.3336	2085	-	0.3065	-
0.3344	2090	-	0.3019	-
0.3352	2095	-	0.2960	-
0.336	2100	0.5244	0.2985	-
0.3368	2105	-	0.3055	-
0.3376	2110	-	0.3096	-
0.3384	2115	-	0.3095	-
0.3392	2120	-	0.3105	-
0.34	2125	-	0.3123	-
0.3408	2130	-	0.3130	-
0.3416	2135	-	0.3134	-
0.3424	2140	-	0.3074	-
0.3432	2145	-	0.3073	-
0.344	2150	-	0.3005	-
0.3448	2155	-	0.2961	-
0.3456	2160	-	0.2929	-
0.3464	2165	-	0.2886	-
0.3472	2170	-	0.2876	-
0.348	2175	-	0.2892	-
0.3488	2180	-	0.2875	-
0.3496	2185	-	0.2918	-
0.3504	2190	-	0.2876	-
0.3512	2195	-	0.2843	-
0.352	2200	0.5288	0.2842	-
0.3528	2205	-	0.2853	-
0.3536	2210	-	0.2817	-
0.3544	2215	-	0.2848	-
0.3552	2220	-	0.2820	-
0.356	2225	-	0.2852	-
0.3568	2230	-	0.2891	-
0.3576	2235	-	0.2931	-
0.3584	2240	-	0.2903	-
0.3592	2245	-	0.2921	-
0.36	2250	-	0.2895	-
0.3608	2255	-	0.2836	-
0.3616	2260	-	0.2885	-
0.3624	2265	-	0.2882	-
0.3632	2270	-	0.2877	-
0.364	2275	-	0.2898	-
0.3648	2280	-	0.2925	-
0.3656	2285	-	0.2938	-
0.3664	2290	-	0.3009	-
0.3672	2295	-	0.3021	-
0.368	2300	0.5118	0.3021	-
0.3688	2305	-	0.3014	-
0.3696	2310	-	0.3025	-
0.3704	2315	-	0.3008	-
0.3712	2320	-	0.3054	-
0.372	2325	-	0.3024	-
0.3728	2330	-	0.3023	-
0.3736	2335	-	0.2962	-
0.3744	2340	-	0.2913	-
0.3752	2345	-	0.2899	-
0.376	2350	-	0.2891	-
0.3768	2355	-	0.2936	-
0.3776	2360	-	0.3012	-
0.3784	2365	-	0.2994	-
0.3792	2370	-	0.2973	-
0.38	2375	-	0.2960	-
0.3808	2380	-	0.2949	-
0.3816	2385	-	0.2974	-
0.3824	2390	-	0.3047	-
0.3832	2395	-	0.3046	-
0.384	2400	0.5102	0.3100	-
0.3848	2405	-	0.3107	-
0.3856	2410	-	0.3100	-
0.3864	2415	-	0.3115	-
0.3872	2420	-	0.3038	-
0.388	2425	-	0.3011	-
0.3888	2430	-	0.2999	-
0.3896	2435	-	0.3033	-
0.3904	2440	-	0.3069	-
0.3912	2445	-	0.3010	-
0.392	2450	-	0.2930	-
0.3928	2455	-	0.2887	-
0.3936	2460	-	0.2870	-
0.3944	2465	-	0.2865	-
0.3952	2470	-	0.2903	-
0.396	2475	-	0.2956	-
0.3968	2480	-	0.3003	-
0.3976	2485	-	0.2986	-
0.3984	2490	-	0.3049	-
0.3992	2495	-	0.3099	-
0.4	2500	0.5109	0.3056	-
0.4008	2505	-	0.3057	-
0.4016	2510	-	0.3079	-
0.4024	2515	-	0.3152	-
0.4032	2520	-	0.3178	-
0.404	2525	-	0.3098	-
0.4048	2530	-	0.3030	-
0.4056	2535	-	0.3005	-
0.4064	2540	-	0.2978	-
0.4072	2545	-	0.3029	-
0.408	2550	-	0.3086	-
0.4088	2555	-	0.3082	-
0.4096	2560	-	0.3064	-
0.4104	2565	-	0.3042	-
0.4112	2570	-	0.3069	-
0.412	2575	-	0.3041	-
0.4128	2580	-	0.3049	-
0.4136	2585	-	0.3084	-
0.4144	2590	-	0.3076	-
0.4152	2595	-	0.3055	-
0.416	2600	0.5027	0.3031	-
0.4168	2605	-	0.2969	-
0.4176	2610	-	0.2962	-
0.4184	2615	-	0.2968	-
0.4192	2620	-	0.2986	-
0.42	2625	-	0.2966	-
0.4208	2630	-	0.2940	-
0.4216	2635	-	0.2896	-
0.4224	2640	-	0.2856	-
0.4232	2645	-	0.2872	-
0.424	2650	-	0.2925	-
0.4248	2655	-	0.2933	-
0.4256	2660	-	0.2924	-
0.4264	2665	-	0.2915	-
0.4272	2670	-	0.2913	-
0.428	2675	-	0.2930	-
0.4288	2680	-	0.2931	-
0.4296	2685	-	0.2882	-
0.4304	2690	-	0.2917	-
0.4312	2695	-	0.2934	-
0.432	2700	0.5856	0.2924	-
0.4328	2705	-	0.2934	-
0.4336	2710	-	0.2925	-
0.4344	2715	-	0.2916	-
0.4352	2720	-	0.2968	-
0.436	2725	-	0.2964	-
0.4368	2730	-	0.2993	-
0.4376	2735	-	0.3074	-
0.4384	2740	-	0.3133	-
0.4392	2745	-	0.3136	-
0.44	2750	-	0.3142	-
0.4408	2755	-	0.3128	-
0.4416	2760	-	0.3129	-
0.4424	2765	-	0.3098	-
0.4432	2770	-	0.3068	-
0.444	2775	-	0.3061	-
0.4448	2780	-	0.3100	-
0.4456	2785	-	0.3115	-
0.4464	2790	-	0.3097	-
0.4472	2795	-	0.3065	-
0.448	2800	0.4848	0.2986	-
0.4488	2805	-	0.2965	-
0.4496	2810	-	0.2952	-
0.4504	2815	-	0.2939	-
0.4512	2820	-	0.2896	-
0.452	2825	-	0.2875	-
0.4528	2830	-	0.2868	-
0.4536	2835	-	0.2880	-
0.4544	2840	-	0.2863	-
0.4552	2845	-	0.2841	-
0.456	2850	-	0.2838	-
0.4568	2855	-	0.2887	-
0.4576	2860	-	0.2904	-
0.4584	2865	-	0.2913	-
0.4592	2870	-	0.2901	-
0.46	2875	-	0.2885	-
0.4608	2880	-	0.2844	-
0.4616	2885	-	0.2840	-
0.4624	2890	-	0.2875	-
0.4632	2895	-	0.2890	-
0.464	2900	0.5037	0.2849	-
0.4648	2905	-	0.2841	-
0.4656	2910	-	0.2830	-
0.4664	2915	-	0.2883	-
0.4672	2920	-	0.2884	-
0.468	2925	-	0.2875	-
0.4688	2930	-	0.2874	-
0.4696	2935	-	0.2841	-
0.4704	2940	-	0.2830	-
0.4712	2945	-	0.2727	-
0.472	2950	-	0.2761	-
0.4728	2955	-	0.2796	-
0.4736	2960	-	0.2802	-
0.4744	2965	-	0.2790	-
0.4752	2970	-	0.2809	-
0.476	2975	-	0.2767	-
0.4768	2980	-	0.2782	-
0.4776	2985	-	0.2848	-
0.4784	2990	-	0.2834	-
0.4792	2995	-	0.2868	-
0.48	3000	0.5094	0.2943	-
0.4808	3005	-	0.3007	-
0.4816	3010	-	0.3016	-
0.4824	3015	-	0.3003	-
0.4832	3020	-	0.2994	-
0.484	3025	-	0.2926	-
0.4848	3030	-	0.2954	-
0.4856	3035	-	0.2879	-
0.4864	3040	-	0.2907	-
0.4872	3045	-	0.2937	-
0.488	3050	-	0.2977	-
0.4888	3055	-	0.3024	-
0.4896	3060	-	0.3057	-
0.4904	3065	-	0.3096	-
0.4912	3070	-	0.3142	-
0.492	3075	-	0.3188	-
0.4928	3080	-	0.3221	-
0.4936	3085	-	0.3197	-
0.4944	3090	-	0.3246	-
0.4952	3095	-	0.3210	-
0.496	3100	0.5108	0.3154	-
0.4968	3105	-	0.3100	-
0.4976	3110	-	0.3059	-
0.4984	3115	-	0.3065	-
0.4992	3120	-	0.3058	-
0.5	3125	-	0.3009	-
0.5008	3130	-	0.2962	-
0.5016	3135	-	0.2941	-
0.5024	3140	-	0.2923	-
0.5032	3145	-	0.2899	-
0.504	3150	-	0.2921	-
0.5048	3155	-	0.2904	-
0.5056	3160	-	0.2919	-
0.5064	3165	-	0.2934	-
0.5072	3170	-	0.2950	-
0.508	3175	-	0.2865	-
0.5088	3180	-	0.2802	-
0.5096	3185	-	0.2801	-
0.5104	3190	-	0.2801	-
0.5112	3195	-	0.2829	-
0.512	3200	0.4271	0.2851	-
0.5128	3205	-	0.2844	-
0.5136	3210	-	0.2849	-
0.5144	3215	-	0.2852	-
0.5152	3220	-	0.2867	-
0.516	3225	-	0.2896	-
0.5168	3230	-	0.2913	-
0.5176	3235	-	0.2895	-
0.5184	3240	-	0.2864	-
0.5192	3245	-	0.2799	-
0.52	3250	-	0.2726	-
0.5208	3255	-	0.2698	-
0.5216	3260	-	0.2628	-
0.5224	3265	-	0.2595	-
0.5232	3270	-	0.2574	-
0.524	3275	-	0.2549	-
0.5248	3280	-	0.2545	-
0.5256	3285	-	0.2548	-
0.5264	3290	-	0.2526	-
0.5272	3295	-	0.2558	-
0.528	3300	0.4451	0.2658	-
0.5288	3305	-	0.2670	-
0.5296	3310	-	0.2641	-
0.5304	3315	-	0.2622	-
0.5312	3320	-	0.2696	-
0.532	3325	-	0.2733	-
0.5328	3330	-	0.2711	-
0.5336	3335	-	0.2737	-
0.5344	3340	-	0.2765	-
0.5352	3345	-	0.2772	-
0.536	3350	-	0.2715	-
0.5368	3355	-	0.2716	-
0.5376	3360	-	0.2712	-
0.5384	3365	-	0.2718	-
0.5392	3370	-	0.2730	-
0.54	3375	-	0.2706	-
0.5408	3380	-	0.2737	-
0.5416	3385	-	0.2740	-
0.5424	3390	-	0.2763	-
0.5432	3395	-	0.2770	-
0.544	3400	0.4613	0.2751	-
0.5448	3405	-	0.2686	-
0.5456	3410	-	0.2760	-
0.5464	3415	-	0.2757	-
0.5472	3420	-	0.2739	-
0.548	3425	-	0.2684	-
0.5488	3430	-	0.2690	-
0.5496	3435	-	0.2718	-
0.5504	3440	-	0.2725	-
0.5512	3445	-	0.2757	-
0.552	3450	-	0.2769	-
0.5528	3455	-	0.2787	-
0.5536	3460	-	0.2836	-
0.5544	3465	-	0.2799	-
0.5552	3470	-	0.2861	-
0.556	3475	-	0.2848	-
0.5568	3480	-	0.2857	-
0.5576	3485	-	0.2886	-
0.5584	3490	-	0.2900	-
0.5592	3495	-	0.2878	-
0.56	3500	0.4464	0.2871	-
0.5608	3505	-	0.2880	-
0.5616	3510	-	0.2917	-
0.5624	3515	-	0.2946	-
0.5632	3520	-	0.2975	-
0.564	3525	-	0.3024	-
0.5648	3530	-	0.3040	-
0.5656	3535	-	0.3041	-
0.5664	3540	-	0.3018	-
0.5672	3545	-	0.3042	-
0.568	3550	-	0.3065	-
0.5688	3555	-	0.3033	-
0.5696	3560	-	0.3025	-
0.5704	3565	-	0.2974	-
0.5712	3570	-	0.2913	-
0.572	3575	-	0.2931	-
0.5728	3580	-	0.2912	-
0.5736	3585	-	0.2916	-
0.5744	3590	-	0.2905	-
0.5752	3595	-	0.2932	-
0.576	3600	0.3953	0.2975	-
0.5768	3605	-	0.3016	-
0.5776	3610	-	0.3042	-
0.5784	3615	-	0.3030	-
0.5792	3620	-	0.3001	-
0.58	3625	-	0.3038	-
0.5808	3630	-	0.3019	-
0.5816	3635	-	0.2984	-
0.5824	3640	-	0.3040	-
0.5832	3645	-	0.3044	-
0.584	3650	-	0.3106	-
0.5848	3655	-	0.3145	-
0.5856	3660	-	0.3146	-
0.5864	3665	-	0.3196	-
0.5872	3670	-	0.3146	-
0.588	3675	-	0.3112	-
0.5888	3680	-	0.3147	-
0.5896	3685	-	0.3157	-
0.5904	3690	-	0.3189	-
0.5912	3695	-	0.3219	-
0.592	3700	0.4502	0.3216	-
0.5928	3705	-	0.3222	-
0.5936	3710	-	0.3222	-
0.5944	3715	-	0.3211	-
0.5952	3720	-	0.3185	-
0.596	3725	-	0.3128	-
0.5968	3730	-	0.3086	-
0.5976	3735	-	0.3065	-
0.5984	3740	-	0.3077	-
0.5992	3745	-	0.3133	-
0.6	3750	-	0.3124	-
0.6008	3755	-	0.3110	-
0.6016	3760	-	0.3107	-
0.6024	3765	-	0.3106	-
0.6032	3770	-	0.3104	-
0.604	3775	-	0.3119	-
0.6048	3780	-	0.3123	-
0.6056	3785	-	0.3089	-
0.6064	3790	-	0.3054	-
0.6072	3795	-	0.3038	-
0.608	3800	0.4307	0.2960	-
0.6088	3805	-	0.2972	-
0.6096	3810	-	0.2969	-
0.6104	3815	-	0.2930	-
0.6112	3820	-	0.2930	-
0.612	3825	-	0.2946	-
0.6128	3830	-	0.2963	-
0.6136	3835	-	0.2953	-
0.6144	3840	-	0.2997	-
0.6152	3845	-	0.2998	-
0.616	3850	-	0.2997	-
0.6168	3855	-	0.3048	-
0.6176	3860	-	0.3087	-
0.6184	3865	-	0.3075	-
0.6192	3870	-	0.3091	-
0.62	3875	-	0.3099	-
0.6208	3880	-	0.3087	-
0.6216	3885	-	0.3115	-
0.6224	3890	-	0.3113	-
0.6232	3895	-	0.3041	-
0.624	3900	0.4364	0.3071	-
0.6248	3905	-	0.3065	-
0.6256	3910	-	0.3092	-
0.6264	3915	-	0.3088	-
0.6272	3920	-	0.3089	-
0.628	3925	-	0.3055	-
0.6288	3930	-	0.3051	-
0.6296	3935	-	0.3079	-
0.6304	3940	-	0.3090	-
0.6312	3945	-	0.3124	-
0.632	3950	-	0.3133	-
0.6328	3955	-	0.3125	-
0.6336	3960	-	0.3140	-
0.6344	3965	-	0.3158	-
0.6352	3970	-	0.3179	-
0.636	3975	-	0.3204	-
0.6368	3980	-	0.3171	-
0.6376	3985	-	0.3167	-
0.6384	3990	-	0.3124	-
0.6392	3995	-	0.3123	-
0.64	4000	0.4573	0.3126	-
0.6408	4005	-	0.3125	-
0.6416	4010	-	0.3078	-
0.6424	4015	-	0.3000	-
0.6432	4020	-	0.3003	-
0.644	4025	-	0.2964	-
0.6448	4030	-	0.3002	-
0.6456	4035	-	0.2960	-
0.6464	4040	-	0.2958	-
0.6472	4045	-	0.2967	-
0.648	4050	-	0.2923	-
0.6488	4055	-	0.2903	-
0.6496	4060	-	0.2927	-
0.6504	4065	-	0.2940	-
0.6512	4070	-	0.2934	-
0.652	4075	-	0.2928	-
0.6528	4080	-	0.2934	-
0.6536	4085	-	0.2959	-
0.6544	4090	-	0.2935	-
0.6552	4095	-	0.2979	-
0.656	4100	0.3874	0.2957	-
0.6568	4105	-	0.2982	-
0.6576	4110	-	0.2951	-
0.6584	4115	-	0.2949	-
0.6592	4120	-	0.2958	-
0.66	4125	-	0.2945	-
0.6608	4130	-	0.2992	-
0.6616	4135	-	0.3004	-
0.6624	4140	-	0.3005	-
0.6632	4145	-	0.3035	-
0.664	4150	-	0.3017	-
0.6648	4155	-	0.3013	-
0.6656	4160	-	0.3009	-
0.6664	4165	-	0.3042	-
0.6672	4170	-	0.3046	-
0.668	4175	-	0.3074	-
0.6688	4180	-	0.3086	-
0.6696	4185	-	0.3085	-
0.6704	4190	-	0.3075	-
0.6712	4195	-	0.3063	-
0.672	4200	0.3758	0.3062	-
0.6728	4205	-	0.3050	-
0.6736	4210	-	0.3039	-
0.6744	4215	-	0.3009	-
0.6752	4220	-	0.3005	-
0.676	4225	-	0.3010	-
0.6768	4230	-	0.3012	-
0.6776	4235	-	0.3018	-
0.6784	4240	-	0.2989	-
0.6792	4245	-	0.2989	-
0.68	4250	-	0.2993	-
0.6808	4255	-	0.2998	-
0.6816	4260	-	0.3003	-
0.6824	4265	-	0.3015	-
0.6832	4270	-	0.3020	-
0.684	4275	-	0.3013	-
0.6848	4280	-	0.3031	-
0.6856	4285	-	0.3021	-
0.6864	4290	-	0.3025	-
0.6872	4295	-	0.2973	-
0.688	4300	0.4139	0.2952	-
0.6888	4305	-	0.2916	-
0.6896	4310	-	0.2915	-
0.6904	4315	-	0.2951	-
0.6912	4320	-	0.2929	-
0.692	4325	-	0.2929	-
0.6928	4330	-	0.2949	-
0.6936	4335	-	0.2977	-
0.6944	4340	-	0.2991	-
0.6952	4345	-	0.3009	-
0.696	4350	-	0.2996	-
0.6968	4355	-	0.2991	-
0.6976	4360	-	0.2984	-
0.6984	4365	-	0.3014	-
0.6992	4370	-	0.3049	-
0.7	4375	-	0.3024	-
0.7008	4380	-	0.3047	-
0.7016	4385	-	0.3070	-
0.7024	4390	-	0.3075	-
0.7032	4395	-	0.3067	-
0.704	4400	0.3769	0.3050	-
0.7048	4405	-	0.3106	-
0.7056	4410	-	0.3107	-
0.7064	4415	-	0.3140	-
0.7072	4420	-	0.3121	-
0.708	4425	-	0.3121	-
0.7088	4430	-	0.3171	-
0.7096	4435	-	0.3189	-
0.7104	4440	-	0.3188	-
0.7112	4445	-	0.3181	-
0.712	4450	-	0.3188	-
0.7128	4455	-	0.3189	-
0.7136	4460	-	0.3185	-
0.7144	4465	-	0.3158	-
0.7152	4470	-	0.3128	-
0.716	4475	-	0.3072	-
0.7168	4480	-	0.3030	-
0.7176	4485	-	0.3044	-
0.7184	4490	-	0.3044	-
0.7192	4495	-	0.3034	-
0.72	4500	0.427	0.3025	-
0.7208	4505	-	0.3048	-
0.7216	4510	-	0.3053	-
0.7224	4515	-	0.3091	-
0.7232	4520	-	0.3091	-
0.724	4525	-	0.3083	-
0.7248	4530	-	0.3110	-
0.7256	4535	-	0.3081	-
0.7264	4540	-	0.3051	-
0.7272	4545	-	0.3051	-
0.728	4550	-	0.3027	-
0.7288	4555	-	0.3012	-
0.7296	4560	-	0.3024	-
0.7304	4565	-	0.3036	-
0.7312	4570	-	0.3042	-
0.732	4575	-	0.3066	-
0.7328	4580	-	0.3074	-
0.7336	4585	-	0.3081	-
0.7344	4590	-	0.3097	-
0.7352	4595	-	0.3098	-
0.736	4600	0.3701	0.3078	-
0.7368	4605	-	0.3095	-
0.7376	4610	-	0.3148	-
0.7384	4615	-	0.3189	-
0.7392	4620	-	0.3231	-
0.74	4625	-	0.3210	-
0.7408	4630	-	0.3210	-
0.7416	4635	-	0.3185	-
0.7424	4640	-	0.3180	-
0.7432	4645	-	0.3178	-
0.744	4650	-	0.3180	-
0.7448	4655	-	0.3163	-
0.7456	4660	-	0.3144	-
0.7464	4665	-	0.3168	-
0.7472	4670	-	0.3180	-
0.748	4675	-	0.3157	-
0.7488	4680	-	0.3146	-
0.7496	4685	-	0.3115	-
0.7504	4690	-	0.3173	-
0.7512	4695	-	0.3191	-
0.752	4700	0.4066	0.3157	-
0.7528	4705	-	0.3165	-
0.7536	4710	-	0.3150	-
0.7544	4715	-	0.3104	-
0.7552	4720	-	0.3097	-
0.756	4725	-	0.3108	-
0.7568	4730	-	0.3105	-
0.7576	4735	-	0.3096	-
0.7584	4740	-	0.3088	-
0.7592	4745	-	0.3098	-
0.76	4750	-	0.3107	-
0.7608	4755	-	0.3105	-
0.7616	4760	-	0.3056	-
0.7624	4765	-	0.3051	-
0.7632	4770	-	0.3039	-
0.764	4775	-	0.3018	-
0.7648	4780	-	0.3010	-
0.7656	4785	-	0.3043	-
0.7664	4790	-	0.3057	-
0.7672	4795	-	0.3052	-
0.768	4800	0.4148	0.3051	-
0.7688	4805	-	0.3027	-
0.7696	4810	-	0.3038	-
0.7704	4815	-	0.3053	-
0.7712	4820	-	0.3033	-
0.772	4825	-	0.3010	-
0.7728	4830	-	0.3012	-
0.7736	4835	-	0.3002	-
0.7744	4840	-	0.3012	-
0.7752	4845	-	0.3004	-
0.776	4850	-	0.3023	-
0.7768	4855	-	0.3035	-
0.7776	4860	-	0.3033	-
0.7784	4865	-	0.3039	-
0.7792	4870	-	0.3047	-
0.78	4875	-	0.3016	-
0.7808	4880	-	0.3015	-
0.7816	4885	-	0.3022	-
0.7824	4890	-	0.3036	-
0.7832	4895	-	0.3038	-
0.784	4900	0.4476	0.3046	-
0.7848	4905	-	0.3042	-
0.7856	4910	-	0.3056	-
0.7864	4915	-	0.3098	-
0.7872	4920	-	0.3131	-
0.788	4925	-	0.3145	-
0.7888	4930	-	0.3132	-
0.7896	4935	-	0.3127	-
0.7904	4940	-	0.3108	-
0.7912	4945	-	0.3097	-
0.792	4950	-	0.3120	-
0.7928	4955	-	0.3108	-
0.7936	4960	-	0.3100	-
0.7944	4965	-	0.3085	-
0.7952	4970	-	0.3084	-
0.796	4975	-	0.3114	-
0.7968	4980	-	0.3095	-
0.7976	4985	-	0.3087	-
0.7984	4990	-	0.3078	-
0.7992	4995	-	0.3076	-
0.8	5000	0.352	0.3080	-
0.8008	5005	-	0.3129	-
0.8016	5010	-	0.3111	-
0.8024	5015	-	0.3116	-
0.8032	5020	-	0.3061	-
0.804	5025	-	0.3081	-
0.8048	5030	-	0.3081	-
0.8056	5035	-	0.3068	-
0.8064	5040	-	0.3069	-
0.8072	5045	-	0.3063	-
0.808	5050	-	0.3044	-
0.8088	5055	-	0.3044	-
0.8096	5060	-	0.3045	-
0.8104	5065	-	0.3039	-
0.8112	5070	-	0.3045	-
0.812	5075	-	0.3045	-
0.8128	5080	-	0.3065	-
0.8136	5085	-	0.3065	-
0.8144	5090	-	0.3078	-
0.8152	5095	-	0.3075	-
0.816	5100	0.3847	0.3084	-
0.8168	5105	-	0.3080	-
0.8176	5110	-	0.3076	-
0.8184	5115	-	0.3072	-
0.8192	5120	-	0.3064	-
0.82	5125	-	0.3025	-
0.8208	5130	-	0.3020	-
0.8216	5135	-	0.3026	-
0.8224	5140	-	0.3023	-
0.8232	5145	-	0.3028	-
0.824	5150	-	0.3022	-
0.8248	5155	-	0.3026	-
0.8256	5160	-	0.3030	-
0.8264	5165	-	0.3031	-
0.8272	5170	-	0.3026	-
0.828	5175	-	0.3048	-
0.8288	5180	-	0.3029	-
0.8296	5185	-	0.3041	-
0.8304	5190	-	0.3061	-
0.8312	5195	-	0.3089	-
0.832	5200	0.3878	0.3092	-
0.8328	5205	-	0.3084	-
0.8336	5210	-	0.3098	-
0.8344	5215	-	0.3073	-
0.8352	5220	-	0.3069	-
0.836	5225	-	0.3081	-
0.8368	5230	-	0.3092	-
0.8376	5235	-	0.3077	-
0.8384	5240	-	0.3066	-
0.8392	5245	-	0.3078	-
0.84	5250	-	0.3108	-
0.8408	5255	-	0.3113	-
0.8416	5260	-	0.3110	-
0.8424	5265	-	0.3108	-
0.8432	5270	-	0.3090	-
0.844	5275	-	0.3089	-
0.8448	5280	-	0.3102	-
0.8456	5285	-	0.3095	-
0.8464	5290	-	0.3096	-
0.8472	5295	-	0.3097	-
0.848	5300	0.3869	0.3099	-
0.8488	5305	-	0.3099	-
0.8496	5310	-	0.3086	-
0.8504	5315	-	0.3089	-
0.8512	5320	-	0.3088	-
0.852	5325	-	0.3085	-
0.8528	5330	-	0.3084	-
0.8536	5335	-	0.3076	-
0.8544	5340	-	0.3076	-
0.8552	5345	-	0.3078	-
0.856	5350	-	0.3087	-
0.8568	5355	-	0.3090	-
0.8576	5360	-	0.3102	-
0.8584	5365	-	0.3106	-
0.8592	5370	-	0.3091	-
0.86	5375	-	0.3080	-
0.8608	5380	-	0.3072	-
0.8616	5385	-	0.3057	-
0.8624	5390	-	0.3039	-
0.8632	5395	-	0.3019	-
0.864	5400	0.3728	0.3016	-
0.8648	5405	-	0.3041	-
0.8656	5410	-	0.3017	-
0.8664	5415	-	0.3003	-
0.8672	5420	-	0.2972	-
0.868	5425	-	0.2953	-
0.8688	5430	-	0.2972	-
0.8696	5435	-	0.2979	-
0.8704	5440	-	0.2992	-
0.8712	5445	-	0.3000	-
0.872	5450	-	0.2999	-
0.8728	5455	-	0.3013	-
0.8736	5460	-	0.3037	-
0.8744	5465	-	0.3077	-
0.8752	5470	-	0.3096	-
0.876	5475	-	0.3105	-
0.8768	5480	-	0.3139	-
0.8776	5485	-	0.3161	-
0.8784	5490	-	0.3156	-
0.8792	5495	-	0.3157	-
0.88	5500	0.3655	0.3166	-
0.8808	5505	-	0.3153	-
0.8816	5510	-	0.3155	-
0.8824	5515	-	0.3143	-
0.8832	5520	-	0.3153	-
0.884	5525	-	0.3153	-
0.8848	5530	-	0.3162	-
0.8856	5535	-	0.3168	-
0.8864	5540	-	0.3167	-
0.8872	5545	-	0.3175	-
0.888	5550	-	0.3170	-
0.8888	5555	-	0.3165	-
0.8896	5560	-	0.3160	-
0.8904	5565	-	0.3160	-
0.8912	5570	-	0.3141	-
0.892	5575	-	0.3140	-
0.8928	5580	-	0.3121	-
0.8936	5585	-	0.3126	-
0.8944	5590	-	0.3112	-
0.8952	5595	-	0.3111	-
0.896	5600	0.3631	0.3104	-
0.8968	5605	-	0.3080	-
0.8976	5610	-	0.3075	-
0.8984	5615	-	0.3066	-
0.8992	5620	-	0.3067	-
0.9	5625	-	0.3061	-
0.9008	5630	-	0.3061	-
0.9016	5635	-	0.3094	-
0.9024	5640	-	0.3087	-
0.9032	5645	-	0.3089	-
0.904	5650	-	0.3083	-
0.9048	5655	-	0.3088	-
0.9056	5660	-	0.3090	-
0.9064	5665	-	0.3109	-
0.9072	5670	-	0.3098	-
0.908	5675	-	0.3095	-
0.9088	5680	-	0.3098	-
0.9096	5685	-	0.3103	-
0.9104	5690	-	0.3103	-
0.9112	5695	-	0.3081	-
0.912	5700	0.3801	0.3091	-
0.9128	5705	-	0.3081	-
0.9136	5710	-	0.3060	-
0.9144	5715	-	0.3069	-
0.9152	5720	-	0.3073	-
0.916	5725	-	0.3082	-
0.9168	5730	-	0.3092	-
0.9176	5735	-	0.3088	-
0.9184	5740	-	0.3079	-
0.9192	5745	-	0.3068	-
0.92	5750	-	0.3068	-
0.9208	5755	-	0.3101	-
0.9216	5760	-	0.3071	-
0.9224	5765	-	0.3074	-
0.9232	5770	-	0.3095	-
0.924	5775	-	0.3083	-
0.9248	5780	-	0.3071	-
0.9256	5785	-	0.3069	-
0.9264	5790	-	0.3070	-
0.9272	5795	-	0.3062	-
0.928	5800	0.3622	0.3054	-
0.9288	5805	-	0.3075	-
0.9296	5810	-	0.3076	-
0.9304	5815	-	0.3075	-
0.9312	5820	-	0.3069	-
0.932	5825	-	0.3059	-
0.9328	5830	-	0.3073	-
0.9336	5835	-	0.3087	-
0.9344	5840	-	0.3098	-
0.9352	5845	-	0.3115	-
0.936	5850	-	0.3101	-
0.9368	5855	-	0.3098	-
0.9376	5860	-	0.3095	-
0.9384	5865	-	0.3085	-
0.9392	5870	-	0.3103	-
0.94	5875	-	0.3104	-
0.9408	5880	-	0.3088	-
0.9416	5885	-	0.3069	-
0.9424	5890	-	0.3089	-
0.9432	5895	-	0.3082	-
0.944	5900	0.3554	0.3071	-
0.9448	5905	-	0.3062	-
0.9456	5910	-	0.3069	-
0.9464	5915	-	0.3059	-
0.9472	5920	-	0.3064	-
0.948	5925	-	0.3057	-
0.9488	5930	-	0.3079	-
0.9496	5935	-	0.3072	-
0.9504	5940	-	0.3085	-
0.9512	5945	-	0.3085	-
0.952	5950	-	0.3100	-
0.9528	5955	-	0.3076	-
0.9536	5960	-	0.3063	-
0.9544	5965	-	0.3060	-
0.9552	5970	-	0.3082	-
0.956	5975	-	0.3065	-
0.9568	5980	-	0.3069	-
0.9576	5985	-	0.3065	-
0.9584	5990	-	0.3059	-
0.9592	5995	-	0.3055	-
0.96	6000	0.3835	0.3055	-
0.9608	6005	-	0.3062	-
0.9616	6010	-	0.3045	-
0.9624	6015	-	0.3046	-
0.9632	6020	-	0.3037	-
0.964	6025	-	0.3036	-
0.9648	6030	-	0.3047	-
0.9656	6035	-	0.3049	-
0.9664	6040	-	0.3054	-
0.9672	6045	-	0.3056	-
0.968	6050	-	0.3058	-
0.9688	6055	-	0.3068	-
0.9696	6060	-	0.3061	-
0.9704	6065	-	0.3049	-
0.9712	6070	-	0.3055	-
0.972	6075	-	0.3051	-
0.9728	6080	-	0.3051	-
0.9736	6085	-	0.3054	-
0.9744	6090	-	0.3054	-
0.9752	6095	-	0.3049	-
0.976	6100	0.3748	0.3049	-
0.9768	6105	-	0.3032	-
0.9776	6110	-	0.3045	-
0.9784	6115	-	0.3045	-
0.9792	6120	-	0.3045	-
0.98	6125	-	0.3045	-
0.9808	6130	-	0.3059	-
0.9816	6135	-	0.3059	-
0.9824	6140	-	0.3049	-
0.9832	6145	-	0.3061	-
0.984	6150	-	0.3061	-
0.9848	6155	-	0.3061	-
0.9856	6160	-	0.3060	-
0.9864	6165	-	0.3060	-
0.9872	6170	-	0.3060	-
0.988	6175	-	0.3061	-
0.9888	6180	-	0.3059	-
0.9896	6185	-	0.3047	-
0.9904	6190	-	0.3060	-
0.9912	6195	-	0.3060	-
0.992	6200	0.1938	0.3059	-
0.9928	6205	-	0.3060	-
0.9936	6210	-	0.3060	-
0.9944	6215	-	0.3060	-
0.9952	6220	-	0.3060	-
0.996	6225	-	0.3060	-
0.9968	6230	-	0.3060	-
0.9976	6235	-	0.3060	-
0.9984	6240	-	0.3060	-
0.9992	6245	-	0.3047	-
1.0	6250	-	0.3060	-
-1	-1	-	-	0.9275

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 10.46 tokens max: 46 tokens	min: 6 tokens mean: 12.81 tokens max: 40 tokens	min: 5 tokens mean: 13.4 tokens max: 50 tokens