This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Devy1/MiniLM-cosqa-16")
# Run inference
sentences = [
    'bottom 5 rows in python',
    'def table_top_abs(self):\n        """Returns the absolute position of table top"""\n        table_height = np.array([0, 0, self.table_full_size[2]])\n        return string_to_array(self.floor.get("pos")) + table_height',
    'def refresh(self, document):\n\t\t""" Load a new copy of a document from the database.  does not\n\t\t\treplace the old one """\n\t\ttry:\n\t\t\told_cache_size = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\t\treturn obj',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5117, -0.0480],
#         [ 0.5117,  1.0000, -0.0416],
#         [-0.0480, -0.0416,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 9,020 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 6 tokens
mean: 9.67 tokens
max: 21 tokens
min: 40 tokens
mean: 86.17 tokens
max: 256 tokens

Samples:

anchor	positive
`1d array in char datatype in python`	`def _convert_to_array(array_like, dtype):[object Object] """[object Object] Convert Matrix attributes which are array-like or buffer to array.[object Object] """[object Object] if isinstance(array_like, bytes):[object Object] return np.frombuffer(array_like, dtype=dtype)[object Object] return np.asarray(array_like, dtype=dtype)`
`python condition non none`	def _not(condition=None, kwargs):[object Object] """[object Object] Return the opposite of input condition.[object Object][object Object] :param condition: condition to process.[object Object][object Object] :result: not condition.[object Object] :rtype: bool[object Object] """[object Object][object Object] result = True[object Object][object Object] if condition is not None:[object Object] result = not run(condition, kwargs)[object Object][object Object] return result
`accessing a column from a matrix in python`	def get_column(self, X, column):[object Object] """Return a column of the given matrix.[object Object][object Object] Args:[object Object] X: [object Object] or [object Object].[object Object] column: [object Object] or [object Object].[object Object][object Object] Returns:[object Object] np.ndarray: Selected column.[object Object] """[object Object] if isinstance(X, pd.DataFrame):[object Object] return X[column].values[object Object][object Object] return X[:, column]

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
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: 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: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0018	1	0.2852
0.0035	2	0.3503
0.0053	3	0.0422
0.0071	4	0.1306
0.0089	5	0.1997
0.0106	6	0.0307
0.0124	7	0.1194
0.0142	8	0.114
0.0160	9	0.0128
0.0177	10	0.0523
0.0195	11	0.0228
0.0213	12	0.056
0.0230	13	0.2108
0.0248	14	0.0856
0.0266	15	0.058
0.0284	16	0.0311
0.0301	17	0.015
0.0319	18	0.0098
0.0337	19	0.3063
0.0355	20	0.0124
0.0372	21	0.0151
0.0390	22	0.2788
0.0408	23	0.0228
0.0426	24	0.0634
0.0443	25	0.0482
0.0461	26	0.0647
0.0479	27	0.0045
0.0496	28	0.0315
0.0514	29	0.0316
0.0532	30	0.1654
0.0550	31	0.1931
0.0567	32	0.0157
0.0585	33	0.286
0.0603	34	0.1894
0.0621	35	0.0308
0.0638	36	0.0181
0.0656	37	0.126
0.0674	38	0.0258
0.0691	39	0.0669
0.0709	40	0.0979
0.0727	41	0.1078
0.0745	42	0.3883
0.0762	43	0.0341
0.0780	44	0.0439
0.0798	45	0.0733
0.0816	46	0.399
0.0833	47	0.1246
0.0851	48	0.0095
0.0869	49	0.3253
0.0887	50	0.0405
0.0904	51	0.1117
0.0922	52	0.0389
0.0940	53	0.1124
0.0957	54	0.118
0.0975	55	0.2116
0.0993	56	0.0721
0.1011	57	0.1326
0.1028	58	0.1217
0.1046	59	0.0216
0.1064	60	0.0798
0.1082	61	0.1676
0.1099	62	0.0314
0.1117	63	0.045
0.1135	64	0.0325
0.1152	65	0.0624
0.1170	66	0.0282
0.1188	67	0.0164
0.1206	68	0.0632
0.1223	69	0.1402
0.1241	70	0.0271
0.1259	71	0.0449
0.1277	72	0.0107
0.1294	73	0.0531
0.1312	74	0.0489
0.1330	75	0.1134
0.1348	76	0.0657
0.1365	77	0.0383
0.1383	78	0.0288
0.1401	79	0.0514
0.1418	80	0.0173
0.1436	81	0.1886
0.1454	82	0.0532
0.1472	83	0.0024
0.1489	84	0.0076
0.1507	85	0.1116
0.1525	86	0.0089
0.1543	87	0.4592
0.1560	88	0.0552
0.1578	89	0.0327
0.1596	90	0.1102
0.1613	91	0.0077
0.1631	92	0.0048
0.1649	93	0.003
0.1667	94	0.0042
0.1684	95	0.1047
0.1702	96	0.0212
0.1720	97	0.0287
0.1738	98	0.0233
0.1755	99	0.0319
0.1773	100	0.0206
0.1791	101	0.018
0.1809	102	0.059
0.1826	103	0.172
0.1844	104	0.1555
0.1862	105	0.0479
0.1879	106	0.0336
0.1897	107	0.0889
0.1915	108	0.0094
0.1933	109	0.053
0.1950	110	0.0944
0.1968	111	0.2731
0.1986	112	0.0315
0.2004	113	0.162
0.2021	114	0.1024
0.2039	115	0.013
0.2057	116	0.0998
0.2074	117	0.0035
0.2092	118	0.0035
0.2110	119	0.136
0.2128	120	0.0626
0.2145	121	0.0597
0.2163	122	0.1202
0.2181	123	0.1017
0.2199	124	0.0241
0.2216	125	0.0527
0.2234	126	0.0158
0.2252	127	0.005
0.2270	128	0.3728
0.2287	129	0.0049
0.2305	130	0.0426
0.2323	131	0.1093
0.2340	132	0.0607
0.2358	133	0.0387
0.2376	134	0.0672
0.2394	135	0.0187
0.2411	136	0.1737
0.2429	137	0.042
0.2447	138	0.0934
0.2465	139	0.0135
0.2482	140	0.1649
0.25	141	0.1029
0.2518	142	0.0183
0.2535	143	0.1689
0.2553	144	0.6752
0.2571	145	0.076
0.2589	146	0.0961
0.2606	147	0.127
0.2624	148	0.1866
0.2642	149	0.0652
0.2660	150	0.029
0.2677	151	0.0175
0.2695	152	0.0034
0.2713	153	0.2149
0.2730	154	0.0564
0.2748	155	0.0205
0.2766	156	0.0193
0.2784	157	0.1054
0.2801	158	0.0209
0.2819	159	0.1948
0.2837	160	0.0176
0.2855	161	0.1101
0.2872	162	0.003
0.2890	163	0.0373
0.2908	164	0.1793
0.2926	165	0.0878
0.2943	166	0.0346
0.2961	167	0.0051
0.2979	168	0.2891
0.2996	169	0.2409
0.3014	170	0.0056
0.3032	171	0.0051
0.3050	172	0.1651
0.3067	173	0.0802
0.3085	174	0.1191
0.3103	175	0.0453
0.3121	176	0.0972
0.3138	177	0.0157
0.3156	178	0.0339
0.3174	179	0.0759
0.3191	180	0.196
0.3209	181	0.1043
0.3227	182	0.0603
0.3245	183	0.0163
0.3262	184	0.0115
0.3280	185	0.1027
0.3298	186	0.0726
0.3316	187	0.089
0.3333	188	0.0385
0.3351	189	0.0082
0.3369	190	0.1135
0.3387	191	0.074
0.3404	192	0.1149
0.3422	193	0.1642
0.3440	194	0.0166
0.3457	195	0.0105
0.3475	196	0.0313
0.3493	197	0.1255
0.3511	198	0.0471
0.3528	199	0.067
0.3546	200	0.0227
0.3564	201	0.1239
0.3582	202	0.237
0.3599	203	0.0141
0.3617	204	0.0077
0.3635	205	0.0073
0.3652	206	0.0417
0.3670	207	0.0297
0.3688	208	0.0752
0.3706	209	0.0155
0.3723	210	0.0536
0.3741	211	0.0034
0.3759	212	0.0273
0.3777	213	0.2597
0.3794	214	0.0574
0.3812	215	0.0554
0.3830	216	0.0806
0.3848	217	0.018
0.3865	218	0.215
0.3883	219	0.0527
0.3901	220	0.0025
0.3918	221	0.0459
0.3936	222	0.0074
0.3954	223	0.0603
0.3972	224	0.0092
0.3989	225	0.0832
0.4007	226	0.0144
0.4025	227	0.1483
0.4043	228	0.4177
0.4060	229	0.0061
0.4078	230	0.0034
0.4096	231	0.0917
0.4113	232	0.0039
0.4131	233	0.0369
0.4149	234	0.0619
0.4167	235	0.1598
0.4184	236	0.0699
0.4202	237	0.0641
0.4220	238	0.0162
0.4238	239	0.1175
0.4255	240	0.0043
0.4273	241	0.0171
0.4291	242	0.005
0.4309	243	0.169
0.4326	244	0.0124
0.4344	245	0.1141
0.4362	246	0.0467
0.4379	247	0.0074
0.4397	248	0.2058
0.4415	249	0.0186
0.4433	250	0.0112
0.4450	251	0.2977
0.4468	252	0.0384
0.4486	253	0.1525
0.4504	254	0.2781
0.4521	255	0.1463
0.4539	256	0.1352
0.4557	257	0.0789
0.4574	258	0.013
0.4592	259	0.2722
0.4610	260	0.0701
0.4628	261	0.036
0.4645	262	0.0363
0.4663	263	0.1835
0.4681	264	0.2061
0.4699	265	0.0639
0.4716	266	0.0007
0.4734	267	0.0107
0.4752	268	0.1097
0.4770	269	0.2531
0.4787	270	0.0205
0.4805	271	0.1076
0.4823	272	0.0621
0.4840	273	0.0065
0.4858	274	0.0444
0.4876	275	0.0613
0.4894	276	0.0373
0.4911	277	0.4446
0.4929	278	0.071
0.4947	279	0.0839
0.4965	280	0.2712
0.4982	281	0.3855
0.5	282	0.02
0.5018	283	0.1209
0.5035	284	0.0428
0.5053	285	0.0859
0.5071	286	0.0076
0.5089	287	0.0137
0.5106	288	0.1124
0.5124	289	0.2544
0.5142	290	0.0029
0.5160	291	0.0142
0.5177	292	0.0709
0.5195	293	0.0418
0.5213	294	0.1344
0.5230	295	0.0105
0.5248	296	0.1553
0.5266	297	0.0281
0.5284	298	0.0122
0.5301	299	0.0383
0.5319	300	0.2396
0.5337	301	0.1094
0.5355	302	0.0929
0.5372	303	0.0312
0.5390	304	0.068
0.5408	305	0.0128
0.5426	306	0.127
0.5443	307	0.0414
0.5461	308	0.1497
0.5479	309	0.041
0.5496	310	0.0288
0.5514	311	0.0479
0.5532	312	0.0204
0.5550	313	0.0828
0.5567	314	0.0149
0.5585	315	0.1651
0.5603	316	0.0982
0.5621	317	0.0118
0.5638	318	0.1905
0.5656	319	0.0074
0.5674	320	0.1277
0.5691	321	0.0336
0.5709	322	0.037
0.5727	323	0.0228
0.5745	324	0.5044
0.5762	325	0.2475
0.5780	326	0.0389
0.5798	327	0.0035
0.5816	328	0.0812
0.5833	329	0.1005
0.5851	330	0.3384
0.5869	331	0.0345
0.5887	332	0.0903
0.5904	333	0.0144
0.5922	334	0.0853
0.5940	335	0.1661
0.5957	336	0.0339
0.5975	337	0.0749
0.5993	338	0.2761
0.6011	339	0.0036
0.6028	340	0.0843
0.6046	341	0.0963
0.6064	342	0.0261
0.6082	343	0.0305
0.6099	344	0.0076
0.6117	345	0.006
0.6135	346	0.0034
0.6152	347	0.0278
0.6170	348	0.01
0.6188	349	0.0059
0.6206	350	0.0663
0.6223	351	0.0198
0.6241	352	0.0134
0.6259	353	0.123
0.6277	354	0.0899
0.6294	355	0.0943
0.6312	356	0.011
0.6330	357	0.1238
0.6348	358	0.0283
0.6365	359	0.0248
0.6383	360	0.0365
0.6401	361	0.0349
0.6418	362	0.0183
0.6436	363	0.0106
0.6454	364	0.0523
0.6472	365	0.1742
0.6489	366	0.1366
0.6507	367	0.2887
0.6525	368	0.0802
0.6543	369	0.0532
0.6560	370	0.1194
0.6578	371	0.0648
0.6596	372	0.1022
0.6613	373	0.0596
0.6631	374	0.1083
0.6649	375	0.0121
0.6667	376	0.0448
0.6684	377	0.0261
0.6702	378	0.1448
0.6720	379	0.0822
0.6738	380	0.0141
0.6755	381	0.0187
0.6773	382	0.0639
0.6791	383	0.3279
0.6809	384	0.0084
0.6826	385	0.0256
0.6844	386	0.0886
0.6862	387	0.0671
0.6879	388	0.0365
0.6897	389	0.0112
0.6915	390	0.018
0.6933	391	0.2417
0.6950	392	0.1742
0.6968	393	0.0083
0.6986	394	0.0202
0.7004	395	0.0371
0.7021	396	0.0249
0.7039	397	0.019
0.7057	398	0.0546
0.7074	399	0.0287
0.7092	400	0.0234
0.7110	401	0.005
0.7128	402	0.0089
0.7145	403	0.0097
0.7163	404	0.0545
0.7181	405	0.0079
0.7199	406	0.2158
0.7216	407	0.0216
0.7234	408	0.0033
0.7252	409	0.0313
0.7270	410	0.0527
0.7287	411	0.1268
0.7305	412	0.0025
0.7323	413	0.0597
0.7340	414	0.0291
0.7358	415	0.0219
0.7376	416	0.0818
0.7394	417	0.1946
0.7411	418	0.5806
0.7429	419	0.0348
0.7447	420	0.0138
0.7465	421	0.0445
0.7482	422	0.639
0.75	423	0.028
0.7518	424	0.1737
0.7535	425	0.0038
0.7553	426	0.014
0.7571	427	0.1141
0.7589	428	0.0936
0.7606	429	0.0724
0.7624	430	0.0438
0.7642	431	0.0044
0.7660	432	0.003
0.7677	433	0.0147
0.7695	434	0.1538
0.7713	435	0.0203
0.7730	436	0.0223
0.7748	437	0.0056
0.7766	438	0.0114
0.7784	439	0.0097
0.7801	440	0.0169
0.7819	441	0.0453
0.7837	442	0.1687
0.7855	443	0.1222
0.7872	444	0.0091
0.7890	445	0.0155
0.7908	446	0.1198
0.7926	447	0.0922
0.7943	448	0.017
0.7961	449	0.0853
0.7979	450	0.0946
0.7996	451	0.0558
0.8014	452	0.0229
0.8032	453	0.0062
0.8050	454	0.0175
0.8067	455	0.0339
0.8085	456	0.0445
0.8103	457	0.0411
0.8121	458	0.0037
0.8138	459	0.0244
0.8156	460	0.0358
0.8174	461	0.062
0.8191	462	0.0201
0.8209	463	0.0055
0.8227	464	0.152
0.8245	465	0.0032
0.8262	466	0.2056
0.8280	467	0.0245
0.8298	468	0.0239
0.8316	469	0.0323
0.8333	470	0.2737
0.8351	471	0.0205
0.8369	472	0.0037
0.8387	473	0.2092
0.8404	474	0.0659
0.8422	475	0.0361
0.8440	476	0.0845
0.8457	477	0.015
0.8475	478	0.0055
0.8493	479	0.0012
0.8511	480	0.0241
0.8528	481	0.1986
0.8546	482	0.1794
0.8564	483	0.0477
0.8582	484	0.1216
0.8599	485	0.0423
0.8617	486	0.0124
0.8635	487	0.0724
0.8652	488	0.3665
0.8670	489	0.0338
0.8688	490	0.0327
0.8706	491	0.0875
0.8723	492	0.1198
0.8741	493	0.0959
0.8759	494	0.4752
0.8777	495	0.0248
0.8794	496	0.0955
0.8812	497	0.0988
0.8830	498	0.0053
0.8848	499	0.2546
0.8865	500	0.2137
0.8883	501	0.0013
0.8901	502	0.0053
0.8918	503	0.0021
0.8936	504	0.0357
0.8954	505	0.1408
0.8972	506	0.0475
0.8989	507	0.0041
0.9007	508	0.1138
0.9025	509	0.1568
0.9043	510	0.0094
0.9060	511	0.0015
0.9078	512	0.028
0.9096	513	0.2884
0.9113	514	0.0929
0.9131	515	0.2441
0.9149	516	0.0067
0.9167	517	0.0327
0.9184	518	0.029
0.9202	519	0.0835
0.9220	520	0.006
0.9238	521	0.0103
0.9255	522	0.1339
0.9273	523	0.0084
0.9291	524	0.0101
0.9309	525	0.0053
0.9326	526	0.0236
0.9344	527	0.0927
0.9362	528	0.0636
0.9379	529	0.1854
0.9397	530	0.117
0.9415	531	0.0115
0.9433	532	0.1472
0.9450	533	0.0226
0.9468	534	0.0531
0.9486	535	0.0272
0.9504	536	0.0213
0.9521	537	0.008
0.9539	538	0.0244
0.9557	539	0.0061
0.9574	540	0.0987
0.9592	541	0.021
0.9610	542	0.0556
0.9628	543	0.0214
0.9645	544	0.1886
0.9663	545	0.1871
0.9681	546	0.1497
0.9699	547	0.2943
0.9716	548	0.0207
0.9734	549	0.0032
0.9752	550	0.066
0.9770	551	0.0986
0.9787	552	0.0255
0.9805	553	0.1584
0.9823	554	0.0939
0.9840	555	0.0543
0.9858	556	0.0293
0.9876	557	0.1172
0.9894	558	0.0345
0.9911	559	0.0188
0.9929	560	0.0108
0.9947	561	0.0069
0.9965	562	0.0965
0.9982	563	0.1211
1.0	564	0.0011
1.0018	565	0.002
1.0035	566	0.0409
1.0053	567	0.0062
1.0071	568	0.0074
1.0089	569	0.0012
1.0106	570	0.0454
1.0124	571	0.0017
1.0142	572	0.0727
1.0160	573	0.0096
1.0177	574	0.1944
1.0195	575	0.0129
1.0213	576	0.0077
1.0230	577	0.0203
1.0248	578	0.046
1.0266	579	0.0011
1.0284	580	0.0014
1.0301	581	0.002
1.0319	582	0.0362
1.0337	583	0.0023
1.0355	584	0.0055
1.0372	585	0.1081
1.0390	586	0.1659
1.0408	587	0.012
1.0426	588	0.0225
1.0443	589	0.1943
1.0461	590	0.0045
1.0479	591	0.0024
1.0496	592	0.1368
1.0514	593	0.0895
1.0532	594	0.2384
1.0550	595	0.0842
1.0567	596	0.0669
1.0585	597	0.0039
1.0603	598	0.0031
1.0621	599	0.0044
1.0638	600	0.1103
1.0656	601	0.0232
1.0674	602	0.0644
1.0691	603	0.0104
1.0709	604	0.0383
1.0727	605	0.1454
1.0745	606	0.0123
1.0762	607	0.0094
1.0780	608	0.0247
1.0798	609	0.0473
1.0816	610	0.0212
1.0833	611	0.0506
1.0851	612	0.0854
1.0869	613	0.021
1.0887	614	0.012
1.0904	615	0.012
1.0922	616	0.1787
1.0940	617	0.0229
1.0957	618	0.0123
1.0975	619	0.0381
1.0993	620	0.1896
1.1011	621	0.1764
1.1028	622	0.0046
1.1046	623	0.0075
1.1064	624	0.013
1.1082	625	0.0592
1.1099	626	0.0127
1.1117	627	0.0952
1.1135	628	0.0051
1.1152	629	0.1906
1.1170	630	0.0105
1.1188	631	0.0526
1.1206	632	0.1145
1.1223	633	0.0086
1.1241	634	0.0669
1.1259	635	0.0183
1.1277	636	0.0424
1.1294	637	0.0444
1.1312	638	0.0085
1.1330	639	0.0057
1.1348	640	0.0067
1.1365	641	0.0007
1.1383	642	0.0052
1.1401	643	0.0066
1.1418	644	0.0005
1.1436	645	0.0011
1.1454	646	0.0872
1.1472	647	0.0125
1.1489	648	0.0985
1.1507	649	0.0628
1.1525	650	0.0313
1.1543	651	0.0083
1.1560	652	0.0379
1.1578	653	0.0314
1.1596	654	0.0029
1.1613	655	0.0078
1.1631	656	0.1272
1.1649	657	0.0167
1.1667	658	0.12
1.1684	659	0.0224
1.1702	660	0.0193
1.1720	661	0.0104
1.1738	662	0.022
1.1755	663	0.1915
1.1773	664	0.0466
1.1791	665	0.024
1.1809	666	0.0385
1.1826	667	0.0914
1.1844	668	0.0364
1.1862	669	0.0165
1.1879	670	0.003
1.1897	671	0.0111
1.1915	672	0.0097
1.1933	673	0.0354
1.1950	674	0.0496
1.1968	675	0.0767
1.1986	676	0.0138
1.2004	677	0.0441
1.2021	678	0.0036
1.2039	679	0.0078
1.2057	680	0.0104
1.2074	681	0.0121
1.2092	682	0.1018
1.2110	683	0.0146
1.2128	684	0.0025
1.2145	685	0.0145
1.2163	686	0.0205
1.2181	687	0.124
1.2199	688	0.0165
1.2216	689	0.1345
1.2234	690	0.0104
1.2252	691	0.0056
1.2270	692	0.001
1.2287	693	0.0047
1.2305	694	0.0218
1.2323	695	0.0161
1.2340	696	0.0163
1.2358	697	0.0214
1.2376	698	0.0059
1.2394	699	0.001
1.2411	700	0.0069
1.2429	701	0.0011
1.2447	702	0.0345
1.2465	703	0.0061
1.2482	704	0.1855
1.25	705	0.0193
1.2518	706	0.0076
1.2535	707	0.1165
1.2553	708	0.0278
1.2571	709	0.0039
1.2589	710	0.0241
1.2606	711	0.0419
1.2624	712	0.0079
1.2642	713	0.0148
1.2660	714	0.0333
1.2677	715	0.0133
1.2695	716	0.2561
1.2713	717	0.0353
1.2730	718	0.0035
1.2748	719	0.0142
1.2766	720	0.0843
1.2784	721	0.0074
1.2801	722	0.0117
1.2819	723	0.014
1.2837	724	0.0197
1.2855	725	0.0235
1.2872	726	0.0243
1.2890	727	0.0023
1.2908	728	0.0048
1.2926	729	0.056
1.2943	730	0.0517
1.2961	731	0.0073
1.2979	732	0.2383
1.2996	733	0.0165
1.3014	734	0.0703
1.3032	735	0.0091
1.3050	736	0.0447
1.3067	737	0.0504
1.3085	738	0.0279
1.3103	739	0.257
1.3121	740	0.0372
1.3138	741	0.0111
1.3156	742	0.0229
1.3174	743	0.062
1.3191	744	0.0186
1.3209	745	0.05
1.3227	746	0.0029
1.3245	747	0.0355
1.3262	748	0.097
1.3280	749	0.1409
1.3298	750	0.0811
1.3316	751	0.0475
1.3333	752	0.0023
1.3351	753	0.0034
1.3369	754	0.0022
1.3387	755	0.0307
1.3404	756	0.1478
1.3422	757	0.0311
1.3440	758	0.0016
1.3457	759	0.018
1.3475	760	0.0024
1.3493	761	0.0067
1.3511	762	0.0209
1.3528	763	0.0405
1.3546	764	0.093
1.3564	765	0.0069
1.3582	766	0.0552
1.3599	767	0.011
1.3617	768	0.0035
1.3635	769	0.014
1.3652	770	0.0235
1.3670	771	0.0304
1.3688	772	0.019
1.3706	773	0.0307
1.3723	774	0.0089
1.3741	775	0.0035
1.3759	776	0.0021
1.3777	777	0.0014
1.3794	778	0.0068
1.3812	779	0.0065
1.3830	780	0.0176
1.3848	781	0.0297
1.3865	782	0.0025
1.3883	783	0.0102
1.3901	784	0.0141
1.3918	785	0.0854
1.3936	786	0.0044
1.3954	787	0.0287
1.3972	788	0.0145
1.3989	789	0.0055
1.4007	790	0.0121
1.4025	791	0.0038
1.4043	792	0.1916
1.4060	793	0.0804
1.4078	794	0.1413
1.4096	795	0.0272
1.4113	796	0.0349
1.4131	797	0.0203
1.4149	798	0.0053
1.4167	799	0.0008
1.4184	800	0.0259
1.4202	801	0.0209
1.4220	802	0.1249
1.4238	803	0.4471
1.4255	804	0.012
1.4273	805	0.1615
1.4291	806	0.0353
1.4309	807	0.0295
1.4326	808	0.0089
1.4344	809	0.0033
1.4362	810	0.0012
1.4379	811	0.0091
1.4397	812	0.0327
1.4415	813	0.0829
1.4433	814	0.1153
1.4450	815	0.013
1.4468	816	0.041
1.4486	817	0.003
1.4504	818	0.2116
1.4521	819	0.0278
1.4539	820	0.0026
1.4557	821	0.1155
1.4574	822	0.0901
1.4592	823	0.0081
1.4610	824	0.0013
1.4628	825	0.0867
1.4645	826	0.0798
1.4663	827	0.0015
1.4681	828	0.0025
1.4699	829	0.0063
1.4716	830	0.0102
1.4734	831	0.0041
1.4752	832	0.021
1.4770	833	0.0392
1.4787	834	0.0058
1.4805	835	0.0086
1.4823	836	0.0084
1.4840	837	0.0568
1.4858	838	0.0127
1.4876	839	0.0653
1.4894	840	0.0042
1.4911	841	0.0164
1.4929	842	0.026
1.4947	843	0.0515
1.4965	844	0.0074
1.4982	845	0.0254
1.5	846	0.0906
1.5018	847	0.0311
1.5035	848	0.0096
1.5053	849	0.0909
1.5071	850	0.0124
1.5089	851	0.0373
1.5106	852	0.001
1.5124	853	0.0202
1.5142	854	0.1159
1.5160	855	0.0006
1.5177	856	0.0211
1.5195	857	0.0173
1.5213	858	0.0029
1.5230	859	0.0107
1.5248	860	0.0249
1.5266	861	0.0071
1.5284	862	0.0392
1.5301	863	0.0051
1.5319	864	0.0157
1.5337	865	0.2098
1.5355	866	0.1102
1.5372	867	0.0141
1.5390	868	0.0158
1.5408	869	0.0014
1.5426	870	0.0045
1.5443	871	0.0085
1.5461	872	0.0184
1.5479	873	0.0147
1.5496	874	0.0018
1.5514	875	0.0235
1.5532	876	0.0464
1.5550	877	0.0249
1.5567	878	0.0027
1.5585	879	0.0209
1.5603	880	0.0672
1.5621	881	0.0032
1.5638	882	0.0032
1.5656	883	0.0297
1.5674	884	0.0121
1.5691	885	0.0192
1.5709	886	0.0153
1.5727	887	0.0016
1.5745	888	0.041
1.5762	889	0.099
1.5780	890	0.1625
1.5798	891	0.0037
1.5816	892	0.1435
1.5833	893	0.2743
1.5851	894	0.0027
1.5869	895	0.01
1.5887	896	0.0556
1.5904	897	0.0019
1.5922	898	0.0127
1.5940	899	0.0183
1.5957	900	0.0128
1.5975	901	0.0136
1.5993	902	0.0423
1.6011	903	0.0053
1.6028	904	0.0356
1.6046	905	0.1253
1.6064	906	0.0055
1.6082	907	0.0966
1.6099	908	0.0426
1.6117	909	0.1751
1.6135	910	0.0049
1.6152	911	0.0591
1.6170	912	0.0198
1.6188	913	0.2293
1.6206	914	0.0449
1.6223	915	0.0107
1.6241	916	0.0974
1.6259	917	0.001
1.6277	918	0.0063
1.6294	919	0.0022
1.6312	920	0.1739
1.6330	921	0.005
1.6348	922	0.0028
1.6365	923	0.1195
1.6383	924	0.0656
1.6401	925	0.0033
1.6418	926	0.0253
1.6436	927	0.0222
1.6454	928	0.0102
1.6472	929	0.0006
1.6489	930	0.0021
1.6507	931	0.0111
1.6525	932	0.0087
1.6543	933	0.0154
1.6560	934	0.0225
1.6578	935	0.0215
1.6596	936	0.004
1.6613	937	0.0041
1.6631	938	0.0129
1.6649	939	0.0356
1.6667	940	0.0339
1.6684	941	0.0185
1.6702	942	0.0157
1.6720	943	0.0585
1.6738	944	0.0961
1.6755	945	0.0031
1.6773	946	0.004
1.6791	947	0.0169
1.6809	948	0.0555
1.6826	949	0.0052
1.6844	950	0.0065
1.6862	951	0.126
1.6879	952	0.0052
1.6897	953	0.0045
1.6915	954	0.0806
1.6933	955	0.0513
1.6950	956	0.1021
1.6968	957	0.0233
1.6986	958	0.0068
1.7004	959	0.0019
1.7021	960	0.0256
1.7039	961	0.06
1.7057	962	0.0452
1.7074	963	0.102
1.7092	964	0.0588
1.7110	965	0.1179
1.7128	966	0.0052
1.7145	967	0.0545
1.7163	968	0.0028
1.7181	969	0.0215
1.7199	970	0.0136
1.7216	971	0.0204
1.7234	972	0.0246
1.7252	973	0.0024
1.7270	974	0.1334
1.7287	975	0.0071
1.7305	976	0.001
1.7323	977	0.0013
1.7340	978	0.0065
1.7358	979	0.009
1.7376	980	0.0033
1.7394	981	0.0055
1.7411	982	0.0028
1.7429	983	0.0052
1.7447	984	0.0182
1.7465	985	0.0459
1.7482	986	0.0023
1.75	987	0.0823
1.7518	988	0.0758
1.7535	989	0.0186
1.7553	990	0.0198
1.7571	991	0.0043
1.7589	992	0.0077
1.7606	993	0.0606
1.7624	994	0.0368
1.7642	995	0.0061
1.7660	996	0.0142
1.7677	997	0.0049
1.7695	998	0.0074
1.7713	999	0.093
1.7730	1000	0.1129
1.7748	1001	0.0008
1.7766	1002	0.1378
1.7784	1003	0.0116
1.7801	1004	0.0024
1.7819	1005	0.0235
1.7837	1006	0.0134
1.7855	1007	0.0087
1.7872	1008	0.0445
1.7890	1009	0.0089
1.7908	1010	0.0395
1.7926	1011	0.001
1.7943	1012	0.0072
1.7961	1013	0.215
1.7979	1014	0.0008
1.7996	1015	0.0047
1.8014	1016	0.0195
1.8032	1017	0.0041
1.8050	1018	0.0934
1.8067	1019	0.0008
1.8085	1020	0.0302
1.8103	1021	0.1175
1.8121	1022	0.0717
1.8138	1023	0.0009
1.8156	1024	0.0016
1.8174	1025	0.2146
1.8191	1026	0.0139
1.8209	1027	0.0067
1.8227	1028	0.054
1.8245	1029	0.0097
1.8262	1030	0.0353
1.8280	1031	0.029
1.8298	1032	0.093
1.8316	1033	0.0028
1.8333	1034	0.1996
1.8351	1035	0.0838
1.8369	1036	0.0651
1.8387	1037	0.3878
1.8404	1038	0.0232
1.8422	1039	0.0141
1.8440	1040	0.0039
1.8457	1041	0.0456
1.8475	1042	0.0093
1.8493	1043	0.0142
1.8511	1044	0.0092
1.8528	1045	0.0492
1.8546	1046	0.0503
1.8564	1047	0.035
1.8582	1048	0.1337
1.8599	1049	0.0038
1.8617	1050	0.003
1.8635	1051	0.0156
1.8652	1052	0.0141
1.8670	1053	0.1854
1.8688	1054	0.0029
1.8706	1055	0.0523
1.8723	1056	0.0313
1.8741	1057	0.0539
1.8759	1058	0.0044
1.8777	1059	0.1037
1.8794	1060	0.1125
1.8812	1061	0.031
1.8830	1062	0.0187
1.8848	1063	0.1745
1.8865	1064	0.0048
1.8883	1065	0.0138
1.8901	1066	0.0112
1.8918	1067	0.0005
1.8936	1068	0.0133
1.8954	1069	0.0411
1.8972	1070	0.0063
1.8989	1071	0.0007
1.9007	1072	0.063
1.9025	1073	0.343
1.9043	1074	0.0014
1.9060	1075	0.0194
1.9078	1076	0.0085
1.9096	1077	0.0067
1.9113	1078	0.0204
1.9131	1079	0.0094
1.9149	1080	0.2565
1.9167	1081	0.0456
1.9184	1082	0.0695
1.9202	1083	0.0047
1.9220	1084	0.0246
1.9238	1085	0.0033
1.9255	1086	0.0121
1.9273	1087	0.0148
1.9291	1088	0.0058
1.9309	1089	0.0019
1.9326	1090	0.0012
1.9344	1091	0.0093
1.9362	1092	0.0081
1.9379	1093	0.2302
1.9397	1094	0.0187
1.9415	1095	0.0013
1.9433	1096	0.0545
1.9450	1097	0.0121
1.9468	1098	0.008
1.9486	1099	0.0114
1.9504	1100	0.0938
1.9521	1101	0.0557
1.9539	1102	0.0522
1.9557	1103	0.2804
1.9574	1104	0.0126
1.9592	1105	0.0515
1.9610	1106	0.0458
1.9628	1107	0.0226
1.9645	1108	0.009
1.9663	1109	0.0154
1.9681	1110	0.0059
1.9699	1111	0.0013
1.9716	1112	0.0274
1.9734	1113	0.0194
1.9752	1114	0.0015
1.9770	1115	0.0013
1.9787	1116	0.0509
1.9805	1117	0.0038
1.9823	1118	0.0144
1.9840	1119	0.0009
1.9858	1120	0.0161
1.9876	1121	0.0494
1.9894	1122	0.0037
1.9911	1123	0.0084
1.9929	1124	0.0304
1.9947	1125	0.1233
1.9965	1126	0.0128
1.9982	1127	0.0031
2.0	1128	0.0021
2.0018	1129	0.0326
2.0035	1130	0.0091
2.0053	1131	0.0197
2.0071	1132	0.0184
2.0089	1133	0.0785
2.0106	1134	0.0013
2.0124	1135	0.0203
2.0142	1136	0.0527
2.0160	1137	0.2003
2.0177	1138	0.0256
2.0195	1139	0.0348
2.0213	1140	0.0064
2.0230	1141	0.0192
2.0248	1142	0.0011
2.0266	1143	0.0166
2.0284	1144	0.0069
2.0301	1145	0.0012
2.0319	1146	0.0021
2.0337	1147	0.0111
2.0355	1148	0.0307
2.0372	1149	0.0553
2.0390	1150	0.0178
2.0408	1151	0.0214
2.0426	1152	0.0115
2.0443	1153	0.0836
2.0461	1154	0.0008
2.0479	1155	0.002
2.0496	1156	0.0013
2.0514	1157	0.1271
2.0532	1158	0.0169
2.0550	1159	0.0895
2.0567	1160	0.1264
2.0585	1161	0.0126
2.0603	1162	0.0033
2.0621	1163	0.0056
2.0638	1164	0.0095
2.0656	1165	0.0561
2.0674	1166	0.001
2.0691	1167	0.0119
2.0709	1168	0.0016
2.0727	1169	0.0184
2.0745	1170	0.1006
2.0762	1171	0.2481
2.0780	1172	0.0295
2.0798	1173	0.0054
2.0816	1174	0.0028
2.0833	1175	0.0251
2.0851	1176	0.0066
2.0869	1177	0.0915
2.0887	1178	0.0259
2.0904	1179	0.0038
2.0922	1180	0.0351
2.0940	1181	0.0073
2.0957	1182	0.0009
2.0975	1183	0.0026
2.0993	1184	0.0013
2.1011	1185	0.1223
2.1028	1186	0.0057
2.1046	1187	0.0056
2.1064	1188	0.004
2.1082	1189	0.0064
2.1099	1190	0.0951
2.1117	1191	0.0328
2.1135	1192	0.0422
2.1152	1193	0.003
2.1170	1194	0.0199
2.1188	1195	0.0024
2.1206	1196	0.0493
2.1223	1197	0.0532
2.1241	1198	0.0006
2.1259	1199	0.0039
2.1277	1200	0.0067
2.1294	1201	0.0169
2.1312	1202	0.0012
2.1330	1203	0.002
2.1348	1204	0.0787
2.1365	1205	0.032
2.1383	1206	0.0018
2.1401	1207	0.0014
2.1418	1208	0.0073
2.1436	1209	0.0256
2.1454	1210	0.0073
2.1472	1211	0.0006
2.1489	1212	0.0112
2.1507	1213	0.0116
2.1525	1214	0.0044
2.1543	1215	0.0033
2.1560	1216	0.0094
2.1578	1217	0.0823
2.1596	1218	0.0064
2.1613	1219	0.0052
2.1631	1220	0.0056
2.1649	1221	0.0205
2.1667	1222	0.0508
2.1684	1223	0.0069
2.1702	1224	0.0813
2.1720	1225	0.022
2.1738	1226	0.0254
2.1755	1227	0.0119
2.1773	1228	0.001
2.1791	1229	0.0074
2.1809	1230	0.0104
2.1826	1231	0.0034
2.1844	1232	0.003
2.1862	1233	0.0026
2.1879	1234	0.0005
2.1897	1235	0.0021
2.1915	1236	0.0034
2.1933	1237	0.1037
2.1950	1238	0.0067
2.1968	1239	0.0349
2.1986	1240	0.0699
2.2004	1241	0.0201
2.2021	1242	0.0079
2.2039	1243	0.0335
2.2057	1244	0.0465
2.2074	1245	0.0144
2.2092	1246	0.1061
2.2110	1247	0.0078
2.2128	1248	0.0027
2.2145	1249	0.0019
2.2163	1250	0.0019
2.2181	1251	0.0109
2.2199	1252	0.0029
2.2216	1253	0.0032
2.2234	1254	0.0039
2.2252	1255	0.0082
2.2270	1256	0.0157
2.2287	1257	0.0027
2.2305	1258	0.0025
2.2323	1259	0.0301
2.2340	1260	0.1471
2.2358	1261	0.0021
2.2376	1262	0.0087
2.2394	1263	0.0109
2.2411	1264	0.2735
2.2429	1265	0.0109
2.2447	1266	0.0042
2.2465	1267	0.0301
2.2482	1268	0.1398
2.25	1269	0.0137
2.2518	1270	0.0059
2.2535	1271	0.0076
2.2553	1272	0.0023
2.2571	1273	0.0281
2.2589	1274	0.0012
2.2606	1275	0.0032
2.2624	1276	0.0151
2.2642	1277	0.0021
2.2660	1278	0.001
2.2677	1279	0.0258
2.2695	1280	0.26
2.2713	1281	0.0036
2.2730	1282	0.0005
2.2748	1283	0.0038
2.2766	1284	0.0016
2.2784	1285	0.0401
2.2801	1286	0.0028
2.2819	1287	0.008
2.2837	1288	0.0077
2.2855	1289	0.0133
2.2872	1290	0.0578
2.2890	1291	0.0008
2.2908	1292	0.0051
2.2926	1293	0.0036
2.2943	1294	0.047
2.2961	1295	0.0026
2.2979	1296	0.0109
2.2996	1297	0.0432
2.3014	1298	0.0184
2.3032	1299	0.0483
2.3050	1300	0.0101
2.3067	1301	0.0098
2.3085	1302	0.0232
2.3103	1303	0.0105
2.3121	1304	0.0062
2.3138	1305	0.0541
2.3156	1306	0.0646
2.3174	1307	0.0084
2.3191	1308	0.0313
2.3209	1309	0.0081
2.3227	1310	0.012
2.3245	1311	0.0036
2.3262	1312	0.0518
2.3280	1313	0.0018
2.3298	1314	0.0044
2.3316	1315	0.0495
2.3333	1316	0.0733
2.3351	1317	0.0478
2.3369	1318	0.0408
2.3387	1319	0.0657
2.3404	1320	0.0007
2.3422	1321	0.0286
2.3440	1322	0.0145
2.3457	1323	0.0028
2.3475	1324	0.013
2.3493	1325	0.0088
2.3511	1326	0.0091
2.3528	1327	0.2375
2.3546	1328	0.0332
2.3564	1329	0.1036
2.3582	1330	0.0073
2.3599	1331	0.0177
2.3617	1332	0.0008
2.3635	1333	0.011
2.3652	1334	0.0228
2.3670	1335	0.0183
2.3688	1336	0.0011
2.3706	1337	0.0178
2.3723	1338	0.2155
2.3741	1339	0.0048
2.3759	1340	0.0854
2.3777	1341	0.0146
2.3794	1342	0.0034
2.3812	1343	0.0105
2.3830	1344	0.0181
2.3848	1345	0.0126
2.3865	1346	0.0555
2.3883	1347	0.1284
2.3901	1348	0.0071
2.3918	1349	0.0007
2.3936	1350	0.003
2.3954	1351	0.013
2.3972	1352	0.0023
2.3989	1353	0.0083
2.4007	1354	0.0217
2.4025	1355	0.2555
2.4043	1356	0.0171
2.4060	1357	0.0028
2.4078	1358	0.0796
2.4096	1359	0.0054
2.4113	1360	0.1113
2.4131	1361	0.0291
2.4149	1362	0.0186
2.4167	1363	0.0248
2.4184	1364	0.0281
2.4202	1365	0.0386
2.4220	1366	0.0049
2.4238	1367	0.0023
2.4255	1368	0.0229
2.4273	1369	0.0043
2.4291	1370	0.0351
2.4309	1371	0.003
2.4326	1372	0.0593
2.4344	1373	0.0746
2.4362	1374	0.1464
2.4379	1375	0.0143
2.4397	1376	0.0871
2.4415	1377	0.034
2.4433	1378	0.0096
2.4450	1379	0.0507
2.4468	1380	0.0248
2.4486	1381	0.0131
2.4504	1382	0.0123
2.4521	1383	0.0303
2.4539	1384	0.0013
2.4557	1385	0.0902
2.4574	1386	0.0375
2.4592	1387	0.0978
2.4610	1388	0.0151
2.4628	1389	0.0139
2.4645	1390	0.1327
2.4663	1391	0.0248
2.4681	1392	0.0086
2.4699	1393	0.0006
2.4716	1394	0.0153
2.4734	1395	0.3766
2.4752	1396	0.0252
2.4770	1397	0.1675
2.4787	1398	0.0018
2.4805	1399	0.0526
2.4823	1400	0.0191
2.4840	1401	0.0077
2.4858	1402	0.0011
2.4876	1403	0.0261
2.4894	1404	0.0028
2.4911	1405	0.0012
2.4929	1406	0.0011
2.4947	1407	0.0015
2.4965	1408	0.0183
2.4982	1409	0.0376
2.5	1410	0.0343
2.5018	1411	0.0184
2.5035	1412	0.0068
2.5053	1413	0.0044
2.5071	1414	0.04
2.5089	1415	0.1035
2.5106	1416	0.0018
2.5124	1417	0.0578
2.5142	1418	0.0039
2.5160	1419	0.0002
2.5177	1420	0.0022
2.5195	1421	0.0005
2.5213	1422	0.0064
2.5230	1423	0.0239
2.5248	1424	0.0209
2.5266	1425	0.0026
2.5284	1426	0.0019
2.5301	1427	0.1177
2.5319	1428	0.0007
2.5337	1429	0.0173
2.5355	1430	0.0744
2.5372	1431	0.0078
2.5390	1432	0.0025
2.5408	1433	0.003
2.5426	1434	0.0116
2.5443	1435	0.0016
2.5461	1436	0.0018
2.5479	1437	0.0636
2.5496	1438	0.0021
2.5514	1439	0.0008
2.5532	1440	0.0048
2.5550	1441	0.0116
2.5567	1442	0.0701
2.5585	1443	0.003
2.5603	1444	0.0051
2.5621	1445	0.0265
2.5638	1446	0.0297
2.5656	1447	0.0062
2.5674	1448	0.0193
2.5691	1449	0.0042
2.5709	1450	0.0075
2.5727	1451	0.0033
2.5745	1452	0.0078
2.5762	1453	0.0662
2.5780	1454	0.0103
2.5798	1455	0.0138
2.5816	1456	0.0049
2.5833	1457	0.0023
2.5851	1458	0.0463
2.5869	1459	0.0539
2.5887	1460	0.0112
2.5904	1461	0.0088
2.5922	1462	0.0096
2.5940	1463	0.0063
2.5957	1464	0.004
2.5975	1465	0.0753
2.5993	1466	0.0013
2.6011	1467	0.0052
2.6028	1468	0.0162
2.6046	1469	0.0015
2.6064	1470	0.0194
2.6082	1471	0.0166
2.6099	1472	0.0015
2.6117	1473	0.0045
2.6135	1474	0.0275
2.6152	1475	0.192
2.6170	1476	0.0113
2.6188	1477	0.0165
2.6206	1478	0.0037
2.6223	1479	0.0031
2.6241	1480	0.0522
2.6259	1481	0.0251
2.6277	1482	0.0531
2.6294	1483	0.0165
2.6312	1484	0.0087
2.6330	1485	0.0982
2.6348	1486	0.0813
2.6365	1487	0.0023
2.6383	1488	0.0656
2.6401	1489	0.0128
2.6418	1490	0.053
2.6436	1491	0.0023
2.6454	1492	0.0314
2.6472	1493	0.0018
2.6489	1494	0.2133
2.6507	1495	0.02
2.6525	1496	0.0149
2.6543	1497	0.0045
2.6560	1498	0.2646
2.6578	1499	0.007
2.6596	1500	0.0031
2.6613	1501	0.0681
2.6631	1502	0.0075
2.6649	1503	0.0009
2.6667	1504	0.0212
2.6684	1505	0.0013
2.6702	1506	0.0118
2.6720	1507	0.0002
2.6738	1508	0.0069
2.6755	1509	0.0119
2.6773	1510	0.0193
2.6791	1511	0.0015
2.6809	1512	0.0486
2.6826	1513	0.156
2.6844	1514	0.02
2.6862	1515	0.0225
2.6879	1516	0.0024
2.6897	1517	0.0272
2.6915	1518	0.0115
2.6933	1519	0.0141
2.6950	1520	0.0155
2.6968	1521	0.0239
2.6986	1522	0.0088
2.7004	1523	0.0131
2.7021	1524	0.0035
2.7039	1525	0.3601
2.7057	1526	0.0384
2.7074	1527	0.0054
2.7092	1528	0.0023
2.7110	1529	0.0008
2.7128	1530	0.0622
2.7145	1531	0.0068
2.7163	1532	0.005
2.7181	1533	0.0466
2.7199	1534	0.0025
2.7216	1535	0.0124
2.7234	1536	0.0059
2.7252	1537	0.0068
2.7270	1538	0.0418
2.7287	1539	0.0108
2.7305	1540	0.0112
2.7323	1541	0.0085
2.7340	1542	0.0032
2.7358	1543	0.052
2.7376	1544	0.0423
2.7394	1545	0.0096
2.7411	1546	0.0291
2.7429	1547	0.0444
2.7447	1548	0.0047
2.7465	1549	0.0273
2.7482	1550	0.0106
2.75	1551	0.1274
2.7518	1552	0.0065
2.7535	1553	0.0033
2.7553	1554	0.0012
2.7571	1555	0.009
2.7589	1556	0.1048
2.7606	1557	0.0149
2.7624	1558	0.0807
2.7642	1559	0.0807
2.7660	1560	0.0103
2.7677	1561	0.038
2.7695	1562	0.0068
2.7713	1563	0.0529
2.7730	1564	0.1415
2.7748	1565	0.0168
2.7766	1566	0.0016
2.7784	1567	0.0017
2.7801	1568	0.0223
2.7819	1569	0.0137
2.7837	1570	0.0051
2.7855	1571	0.0054
2.7872	1572	0.0206
2.7890	1573	0.0465
2.7908	1574	0.0031
2.7926	1575	0.0006
2.7943	1576	0.0047
2.7961	1577	0.0086
2.7979	1578	0.0443
2.7996	1579	0.0099
2.8014	1580	0.0878
2.8032	1581	0.0042
2.8050	1582	0.1406
2.8067	1583	0.0034
2.8085	1584	0.0085
2.8103	1585	0.0118
2.8121	1586	0.0182
2.8138	1587	0.0013
2.8156	1588	0.0049
2.8174	1589	0.0104
2.8191	1590	0.0068
2.8209	1591	0.0017
2.8227	1592	0.004
2.8245	1593	0.0048
2.8262	1594	0.0253
2.8280	1595	0.0672
2.8298	1596	0.0008
2.8316	1597	0.0086
2.8333	1598	0.01
2.8351	1599	0.0165
2.8369	1600	0.1176
2.8387	1601	0.0025
2.8404	1602	0.0068
2.8422	1603	0.0829
2.8440	1604	0.0037
2.8457	1605	0.0347
2.8475	1606	0.0046
2.8493	1607	0.0129
2.8511	1608	0.0325
2.8528	1609	0.0039
2.8546	1610	0.0414
2.8564	1611	0.102
2.8582	1612	0.0935
2.8599	1613	0.0031
2.8617	1614	0.0125
2.8635	1615	0.0011
2.8652	1616	0.0041
2.8670	1617	0.0411
2.8688	1618	0.0029
2.8706	1619	0.1064
2.8723	1620	0.0229
2.8741	1621	0.0142
2.8759	1622	0.0847
2.8777	1623	0.0743
2.8794	1624	0.0019
2.8812	1625	0.0194
2.8830	1626	0.0019
2.8848	1627	0.0143
2.8865	1628	0.0011
2.8883	1629	0.0144
2.8901	1630	0.154
2.8918	1631	0.0092
2.8936	1632	0.0086
2.8954	1633	0.0052
2.8972	1634	0.1818
2.8989	1635	0.0022
2.9007	1636	0.003
2.9025	1637	0.0021
2.9043	1638	0.0091
2.9060	1639	0.0369
2.9078	1640	0.0007
2.9096	1641	0.007
2.9113	1642	0.0071
2.9131	1643	0.0345
2.9149	1644	0.0068
2.9167	1645	0.0063
2.9184	1646	0.0039
2.9202	1647	0.0262
2.9220	1648	0.0653
2.9238	1649	0.0144
2.9255	1650	0.014
2.9273	1651	0.0014
2.9291	1652	0.011
2.9309	1653	0.0104
2.9326	1654	0.0073
2.9344	1655	0.0245
2.9362	1656	0.1735
2.9379	1657	0.0188
2.9397	1658	0.0149
2.9415	1659	0.0186
2.9433	1660	0.0397
2.9450	1661	0.0529
2.9468	1662	0.0345
2.9486	1663	0.0121
2.9504	1664	0.0802
2.9521	1665	0.0051
2.9539	1666	0.0734
2.9557	1667	0.0739
2.9574	1668	0.0191
2.9592	1669	0.0362
2.9610	1670	0.007
2.9628	1671	0.0064
2.9645	1672	0.2386
2.9663	1673	0.0224
2.9681	1674	0.0007
2.9699	1675	0.0019
2.9716	1676	0.0333
2.9734	1677	0.0067
2.9752	1678	0.0052
2.9770	1679	0.0028
2.9787	1680	0.0462
2.9805	1681	0.0072
2.9823	1682	0.0023
2.9840	1683	0.01
2.9858	1684	0.0208
2.9876	1685	0.0189
2.9894	1686	0.002
2.9911	1687	0.0021
2.9929	1688	0.0479
2.9947	1689	0.0159
2.9965	1690	0.0618
2.9982	1691	0.0267
3.0	1692	0.3215

Framework Versions

Python: 3.10.14
Sentence Transformers: 5.1.1
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.1.1
Tokenizers: 0.22.1

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
type	string	string
details	min: 6 tokens mean: 9.67 tokens max: 21 tokens	min: 40 tokens mean: 86.17 tokens max: 256 tokens