metadata

base_model: Alibaba-NLP/gte-multilingual-base
language:
  - hu
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1044013
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: Emberek várnak a lámpánál kerékpárral.
    sentences:
      - Az emberek piros lámpánál haladnak.
      - Az emberek a kerékpárjukon vannak.
      - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában
  - source_sentence: A kutya a vízben van.
    sentences:
      - >-
        Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik
        pedig a tetőn.
      - A macska a vízben van, és dühös.
      - Egy kutya van a vízben, a szájában egy faág.
  - source_sentence: A nő feketét visel.
    sentences:
      - Egy barna kutya fröcsköl, ahogy úszik a vízben.
      - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre.
      - >-
        Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A
        képen:
  - source_sentence: Az emberek alszanak.
    sentences:
      - Három ember beszélget egy városi utcán.
      - A nő fehéret visel.
      - Egy apa és a fia ölelgeti alvás közben.
  - source_sentence: Az emberek alszanak.
    sentences:
      - >-
        Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében,
        miközben egy idősebb nő átmegy az utcán.
      - >-
        Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy
        bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép
        jobb oldalán lévő erős elmosódás tesz kivehetetlenné.
      - Egy apa és a fia ölelgeti alvás közben.
model-index:
  - name: gte-multilingual-base-hu
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9676
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.0324
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9688
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9676
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9688
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.9718181818181818
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.028181818181818183
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9725757575757575
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9718181818181818
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9725757575757575
            name: Max Accuracy

gte-multilingual-base-hu

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base on the train 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: Alibaba-NLP/gte-multilingual-base
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- train
Language: hu
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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("karsar/gte-multilingual-base-hu")
# Run inference
sentences = [
    'Az emberek alszanak.',
    'Egy apa és a fia ölelgeti alvás közben.',
    'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Triplet

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9676
dot_accuracy	0.0324
manhattan_accuracy	0.9688
euclidean_accuracy	0.9676
max_accuracy	0.9688

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9718
dot_accuracy	0.0282
manhattan_accuracy	0.9726
euclidean_accuracy	0.9718
max_accuracy	0.9726

Training Details

Training Dataset

train

Dataset: train
Size: 1,044,013 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 11.73 tokens max: 56 tokens	min: 6 tokens mean: 15.24 tokens max: 47 tokens	min: 7 tokens mean: 16.07 tokens max: 53 tokens

Samples:

anchor	positive	negative
`Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.`	`Egy ember a szabadban, lóháton.`	`Egy ember egy étteremben van, és omlettet rendel.`
`Gyerekek mosolyogva és integetett a kamera`	`Gyermekek vannak jelen`	`A gyerekek homlokot rántanak`
`Egy fiú ugrál a gördeszkát a közepén egy piros híd.`	`A fiú gördeszkás trükköt csinál.`	`A fiú korcsolyázik a járdán.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

train

Dataset: train
Size: 5,000 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 11.73 tokens max: 56 tokens	min: 6 tokens mean: 15.24 tokens max: 47 tokens	min: 7 tokens mean: 16.07 tokens max: 53 tokens

Samples:

anchor	positive	negative
`Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.`	`Egy ember a szabadban, lóháton.`	`Egy ember egy étteremben van, és omlettet rendel.`
`Gyerekek mosolyogva és integetett a kamera`	`Gyermekek vannak jelen`	`A gyerekek homlokot rántanak`
`Egy fiú ugrál a gördeszkát a közepén egy piros híd.`	`A fiú gördeszkás trükköt csinál.`	`A fiú korcsolyázik a járdán.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
num_train_epochs: 1
warmup_ratio: 0.1
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: 8
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: 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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: 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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	train loss	all-nli-dev_max_accuracy	all-nli-test_max_accuracy
0	0	-	-	0.7578	-
0.0008	100	0.8531	-	-	-
0.0015	200	0.938	-	-	-
0.0023	300	0.8788	-	-	-
0.0031	400	0.9619	-	-	-
0.0038	500	0.9634	-	-	-
0.0046	600	1.0995	-	-	-
0.0054	700	0.8266	-	-	-
0.0061	800	0.8647	-	-	-
0.0069	900	0.8123	-	-	-
0.0077	1000	0.7149	-	-	-
0.0084	1100	0.8852	-	-	-
0.0092	1200	0.9001	-	-	-
0.0100	1300	0.8113	-	-	-
0.0107	1400	0.756	-	-	-
0.0115	1500	0.6729	-	-	-
0.0123	1600	0.5763	-	-	-
0.0130	1700	0.6413	-	-	-
0.0138	1800	1.0721	-	-	-
0.0146	1900	0.9248	-	-	-
0.0153	2000	0.9313	0.1873	0.9518	-
0.0161	2100	0.8165	-	-	-
0.0169	2200	0.7051	-	-	-
0.0176	2300	0.8373	-	-	-
0.0184	2400	0.8337	-	-	-
0.0192	2500	0.6224	-	-	-
0.0199	2600	0.4977	-	-	-
0.0207	2700	0.6843	-	-	-
0.0215	2800	0.4773	-	-	-
0.0222	2900	0.5113	-	-	-
0.0230	3000	0.2415	-	-	-
0.0238	3100	0.2441	-	-	-
0.0245	3200	0.3309	-	-	-
0.0253	3300	0.4765	-	-	-
0.0261	3400	0.4781	-	-	-
0.0268	3500	0.2184	-	-	-
0.0276	3600	0.3596	-	-	-
0.0284	3700	0.655	-	-	-
0.0291	3800	0.6108	-	-	-
0.0299	3900	0.4897	-	-	-
0.0307	4000	0.3217	0.3629	0.9146	-
0.0314	4100	0.2678	-	-	-
0.0322	4200	0.4772	-	-	-
0.0329	4300	0.46	-	-	-
0.0337	4400	0.3363	-	-	-
0.0345	4500	0.2244	-	-	-
0.0352	4600	0.2708	-	-	-
0.0360	4700	0.288	-	-	-
0.0368	4800	0.4095	-	-	-
0.0375	4900	0.3836	-	-	-
0.0383	5000	0.3999	-	-	-
0.0391	5100	0.2303	-	-	-
0.0398	5200	0.232	-	-	-
0.0406	5300	0.2001	-	-	-
0.0414	5400	0.2552	-	-	-
0.0421	5500	0.2658	-	-	-
0.0429	5600	0.3652	-	-	-
0.0437	5700	0.6644	-	-	-
0.0444	5800	0.4616	-	-	-
0.0452	5900	0.459	-	-	-
0.0460	6000	0.4053	0.6328	0.8806	-
0.0467	6100	0.3715	-	-	-
0.0475	6200	0.5301	-	-	-
0.0483	6300	0.4412	-	-	-
0.0490	6400	0.3733	-	-	-
0.0498	6500	0.4258	-	-	-
0.0506	6600	0.4896	-	-	-
0.0513	6700	0.4275	-	-	-
0.0521	6800	0.4419	-	-	-
0.0529	6900	0.4671	-	-	-
0.0536	7000	0.4209	-	-	-
0.0544	7100	0.406	-	-	-
0.0552	7200	0.3265	-	-	-
0.0559	7300	0.2712	-	-	-
0.0567	7400	0.3408	-	-	-
0.0575	7500	0.4078	-	-	-
0.0582	7600	0.3304	-	-	-
0.0590	7700	0.2874	-	-	-
0.0598	7800	0.357	-	-	-
0.0605	7900	0.3936	-	-	-
0.0613	8000	0.3239	0.5266	0.8706	-
0.0621	8100	0.3486	-	-	-
0.0628	8200	0.4123	-	-	-
0.0636	8300	0.7267	-	-	-
0.0644	8400	0.6765	-	-	-
0.0651	8500	0.7502	-	-	-
0.0659	8600	0.8435	-	-	-
0.0667	8700	0.4286	-	-	-
0.0674	8800	0.2898	-	-	-
0.0682	8900	0.4943	-	-	-
0.0690	9000	0.3998	-	-	-
0.0697	9100	0.4484	-	-	-
0.0705	9200	0.4421	-	-	-
0.0713	9300	0.3331	-	-	-
0.0720	9400	0.3354	-	-	-
0.0728	9500	0.5536	-	-	-
0.0736	9600	0.4695	-	-	-
0.0743	9700	0.4275	-	-	-
0.0751	9800	0.4075	-	-	-
0.0759	9900	0.5394	-	-	-
0.0766	10000	0.4852	0.4733	0.9202	-
0.0774	10100	0.3679	-	-	-
0.0782	10200	0.4251	-	-	-
0.0789	10300	0.262	-	-	-
0.0797	10400	0.384	-	-	-
0.0805	10500	0.3438	-	-	-
0.0812	10600	0.3618	-	-	-
0.0820	10700	0.4057	-	-	-
0.0828	10800	0.5303	-	-	-
0.0835	10900	0.5121	-	-	-
0.0843	11000	0.4173	-	-	-
0.0851	11100	0.409	-	-	-
0.0858	11200	0.6285	-	-	-
0.0866	11300	0.5373	-	-	-
0.0874	11400	0.3423	-	-	-
0.0881	11500	0.5681	-	-	-
0.0889	11600	0.4172	-	-	-
0.0897	11700	0.5511	-	-	-
0.0904	11800	0.4482	-	-	-
0.0912	11900	0.5888	-	-	-
0.0920	12000	0.4315	0.8177	0.8496	-
0.0927	12100	0.5085	-	-	-
0.0935	12200	0.7179	-	-	-
0.0943	12300	0.72	-	-	-
0.0950	12400	0.4522	-	-	-
0.0958	12500	0.6524	-	-	-
0.0966	12600	0.5518	-	-	-
0.0973	12700	0.5112	-	-	-
0.0981	12800	0.4752	-	-	-
0.0988	12900	0.4075	-	-	-
0.0996	13000	0.7106	-	-	-
0.1004	13100	0.7369	-	-	-
0.1011	13200	0.6002	-	-	-
0.1019	13300	0.3983	-	-	-
0.1027	13400	0.4522	-	-	-
0.1034	13500	0.5373	-	-	-
0.1042	13600	0.6317	-	-	-
0.1050	13700	0.4904	-	-	-
0.1057	13800	0.5027	-	-	-
0.1065	13900	0.4386	-	-	-
0.1073	14000	0.571	0.4533	0.9182	-
0.1080	14100	0.4935	-	-	-
0.1088	14200	0.494	-	-	-
0.1096	14300	0.7545	-	-	-
0.1103	14400	0.64	-	-	-
0.1111	14500	0.7364	-	-	-
0.1119	14600	0.5552	-	-	-
0.1126	14700	0.4282	-	-	-
0.1134	14800	0.8343	-	-	-
0.1142	14900	0.5264	-	-	-
0.1149	15000	0.2682	-	-	-
0.1157	15100	0.4011	-	-	-
0.1165	15200	0.39	-	-	-
0.1172	15300	0.2813	-	-	-
0.1180	15400	0.3316	-	-	-
0.1188	15500	0.2424	-	-	-
0.1195	15600	0.3001	-	-	-
0.1203	15700	0.2728	-	-	-
0.1211	15800	0.366	-	-	-
0.1218	15900	0.4103	-	-	-
0.1226	16000	0.1548	0.7131	0.876	-
0.1234	16100	0.3768	-	-	-
0.1241	16200	0.6659	-	-	-
0.1249	16300	0.5738	-	-	-
0.1257	16400	0.4899	-	-	-
0.1264	16500	0.2281	-	-	-
0.1272	16600	0.2406	-	-	-
0.1280	16700	0.3569	-	-	-
0.1287	16800	0.3862	-	-	-
0.1295	16900	0.3531	-	-	-
0.1303	17000	0.1497	-	-	-
0.1310	17100	0.2125	-	-	-
0.1318	17200	0.3563	-	-	-
0.1326	17300	0.4138	-	-	-
0.1333	17400	0.3434	-	-	-
0.1341	17500	0.351	-	-	-
0.1349	17600	0.1777	-	-	-
0.1356	17700	0.2335	-	-	-
0.1364	17800	0.1479	-	-	-
0.1372	17900	0.2382	-	-	-
0.1379	18000	0.2306	0.5838	0.898	-
0.1387	18100	0.3028	-	-	-
0.1395	18200	0.6886	-	-	-
0.1402	18300	0.4118	-	-	-
0.1410	18400	0.463	-	-	-
0.1418	18500	0.3672	-	-	-
0.1425	18600	0.2931	-	-	-
0.1433	18700	0.4141	-	-	-
0.1441	18800	0.3775	-	-	-
0.1448	18900	0.297	-	-	-
0.1456	19000	0.3659	-	-	-
0.1464	19100	0.4638	-	-	-
0.1471	19200	0.4008	-	-	-
0.1479	19300	0.344	-	-	-
0.1487	19400	0.3902	-	-	-
0.1494	19500	0.3392	-	-	-
0.1502	19600	0.4313	-	-	-
0.1510	19700	0.2827	-	-	-
0.1517	19800	0.2602	-	-	-
0.1525	19900	0.2954	-	-	-
0.1533	20000	0.3626	0.3532	0.9126	-
0.1540	20100	0.3205	-	-	-
0.1548	20200	0.2095	-	-	-
0.1556	20300	0.2758	-	-	-
0.1563	20400	0.3855	-	-	-
0.1571	20500	0.3173	-	-	-
0.1579	20600	0.2858	-	-	-
0.1586	20700	0.3655	-	-	-
0.1594	20800	0.5513	-	-	-
0.1602	20900	0.4995	-	-	-
0.1609	21000	0.5949	-	-	-
0.1617	21100	0.7629	-	-	-
0.1624	21200	0.3139	-	-	-
0.1632	21300	0.1827	-	-	-
0.1640	21400	0.4238	-	-	-
0.1647	21500	0.311	-	-	-
0.1655	21600	0.3881	-	-	-
0.1663	21700	0.4073	-	-	-
0.1670	21800	0.2609	-	-	-
0.1678	21900	0.2442	-	-	-
0.1686	22000	0.4434	0.3622	0.9238	-
0.1693	22100	0.3899	-	-	-
0.1701	22200	0.3822	-	-	-
0.1709	22300	0.2892	-	-	-
0.1716	22400	0.4078	-	-	-
0.1724	22500	0.3758	-	-	-
0.1732	22600	0.2714	-	-	-
0.1739	22700	0.304	-	-	-
0.1747	22800	0.2074	-	-	-
0.1755	22900	0.2447	-	-	-
0.1762	23000	0.2148	-	-	-
0.1770	23100	0.2565	-	-	-
0.1778	23200	0.3164	-	-	-
0.1785	23300	0.4486	-	-	-
0.1793	23400	0.4001	-	-	-
0.1801	23500	0.3374	-	-	-
0.1808	23600	0.2576	-	-	-
0.1816	23700	0.4531	-	-	-
0.1824	23800	0.3501	-	-	-
0.1831	23900	0.2755	-	-	-
0.1839	24000	0.4571	0.5006	0.9296	-
0.1847	24100	0.3371	-	-	-
0.1854	24200	0.4287	-	-	-
0.1862	24300	0.3217	-	-	-
0.1870	24400	0.3464	-	-	-
0.1877	24500	0.3257	-	-	-
0.1885	24600	0.3412	-	-	-
0.1893	24700	0.569	-	-	-
0.1900	24800	0.4851	-	-	-
0.1908	24900	0.2667	-	-	-
0.1916	25000	0.5093	-	-	-
0.1923	25100	0.3305	-	-	-
0.1931	25200	0.3199	-	-	-
0.1939	25300	0.3103	-	-	-
0.1946	25400	0.3189	-	-	-
0.1954	25500	0.6199	-	-	-
0.1962	25600	0.6001	-	-	-
0.1969	25700	0.416	-	-	-
0.1977	25800	0.2765	-	-	-
0.1985	25900	0.3523	-	-	-
0.1992	26000	0.4098	0.3070	0.961	-
0.2000	26100	0.3526	-	-	-
0.2008	26200	0.3409	-	-	-
0.2015	26300	0.2826	-	-	-
0.2023	26400	0.3161	-	-	-
0.2031	26500	0.3768	-	-	-
0.2038	26600	0.2398	-	-	-
0.2046	26700	0.3281	-	-	-
0.2054	26800	0.5103	-	-	-
0.2061	26900	0.3619	-	-	-
0.2069	27000	0.4818	-	-	-
0.2077	27100	0.3793	-	-	-
0.2084	27200	0.3713	-	-	-
0.2092	27300	0.5628	-	-	-
0.2100	27400	0.4162	-	-	-
0.2107	27500	0.1791	-	-	-
0.2115	27600	0.2212	-	-	-
0.2123	27700	0.227	-	-	-
0.2130	27800	0.1547	-	-	-
0.2138	27900	0.1532	-	-	-
0.2146	28000	0.1684	0.2016	0.9732	-
0.2153	28100	0.1512	-	-	-
0.2161	28200	0.1525	-	-	-
0.2169	28300	0.2272	-	-	-
0.2176	28400	0.3624	-	-	-
0.2184	28500	0.1039	-	-	-
0.2192	28600	0.2833	-	-	-
0.2199	28700	0.5507	-	-	-
0.2207	28800	0.3969	-	-	-
0.2215	28900	0.3477	-	-	-
0.2222	29000	0.135	-	-	-
0.2230	29100	0.1454	-	-	-
0.2238	29200	0.2475	-	-	-
0.2245	29300	0.2538	-	-	-
0.2253	29400	0.2197	-	-	-
0.2261	29500	0.057	-	-	-
0.2268	29600	0.1312	-	-	-
0.2276	29700	0.213	-	-	-
0.2283	29800	0.3195	-	-	-
0.2291	29900	0.2358	-	-	-
0.2299	30000	0.273	0.2934	0.9392	-
0.2306	30100	0.1181	-	-	-
0.2314	30200	0.1874	-	-	-
0.2322	30300	0.0743	-	-	-
0.2329	30400	0.1617	-	-	-
0.2337	30500	0.1573	-	-	-
0.2345	30600	0.141	-	-	-
0.2352	30700	0.4947	-	-	-
0.2360	30800	0.2698	-	-	-
0.2368	30900	0.2668	-	-	-
0.2375	31000	0.1834	-	-	-
0.2383	31100	0.1813	-	-	-
0.2391	31200	0.2274	-	-	-
0.2398	31300	0.2553	-	-	-
0.2406	31400	0.2441	-	-	-
0.2414	31500	0.2376	-	-	-
0.2421	31600	0.366	-	-	-
0.2429	31700	0.3248	-	-	-
0.2437	31800	0.2314	-	-	-
0.2444	31900	0.2665	-	-	-
0.2452	32000	0.2388	0.1915	0.9654	-
0.2460	32100	0.2911	-	-	-
0.2467	32200	0.1602	-	-	-
0.2475	32300	0.1294	-	-	-
0.2483	32400	0.2687	-	-	-
0.2490	32500	0.2579	-	-	-
0.2498	32600	0.1988	-	-	-
0.2506	32700	0.1212	-	-	-
0.2513	32800	0.2145	-	-	-
0.2521	32900	0.2485	-	-	-
0.2529	33000	0.2353	-	-	-
0.2536	33100	0.1729	-	-	-
0.2544	33200	0.2498	-	-	-
0.2552	33300	0.3091	-	-	-
0.2559	33400	0.252	-	-	-
0.2567	33500	0.3321	-	-	-
0.2575	33600	0.5145	-	-	-
0.2582	33700	0.2102	-	-	-
0.2590	33800	0.0869	-	-	-
0.2598	33900	0.2779	-	-	-
0.2605	34000	0.1935	0.1556	0.9716	-
0.2613	34100	0.2646	-	-	-
0.2621	34200	0.2464	-	-	-
0.2628	34300	0.214	-	-	-
0.2636	34400	0.1875	-	-	-
0.2644	34500	0.3016	-	-	-
0.2651	34600	0.2721	-	-	-
0.2659	34700	0.215	-	-	-
0.2667	34800	0.1895	-	-	-
0.2674	34900	0.2684	-	-	-
0.2682	35000	0.2721	-	-	-
0.2690	35100	0.1945	-	-	-
0.2697	35200	0.1581	-	-	-
0.2705	35300	0.1269	-	-	-
0.2713	35400	0.2101	-	-	-
0.2720	35500	0.1388	-	-	-
0.2728	35600	0.1664	-	-	-
0.2736	35700	0.1861	-	-	-
0.2743	35800	0.3073	-	-	-
0.2751	35900	0.2723	-	-	-
0.2759	36000	0.2002	0.1500	0.9746	-
0.2766	36100	0.1583	-	-	-
0.2774	36200	0.2918	-	-	-
0.2782	36300	0.1913	-	-	-
0.2789	36400	0.1701	-	-	-
0.2797	36500	0.3122	-	-	-
0.2805	36600	0.2068	-	-	-
0.2812	36700	0.2807	-	-	-
0.2820	36800	0.2398	-	-	-
0.2828	36900	0.2264	-	-	-
0.2835	37000	0.1756	-	-	-
0.2843	37100	0.2027	-	-	-
0.2851	37200	0.4277	-	-	-
0.2858	37300	0.3126	-	-	-
0.2866	37400	0.1836	-	-	-
0.2874	37500	0.3447	-	-	-
0.2881	37600	0.1742	-	-	-
0.2889	37700	0.2391	-	-	-
0.2897	37800	0.1672	-	-	-
0.2904	37900	0.2821	-	-	-
0.2912	38000	0.3924	0.2273	0.9704	-
0.2919	38100	0.3842	-	-	-
0.2927	38200	0.3022	-	-	-
0.2935	38300	0.0748	-	-	-
0.2942	38400	0.2131	-	-	-
0.2950	38500	0.1604	-	-	-
0.2958	38600	0.1645	-	-	-
0.2965	38700	0.1753	-	-	-
0.2973	38800	0.0634	-	-	-
0.2981	38900	0.1199	-	-	-
0.2988	39000	0.1586	-	-	-
0.2996	39100	0.1119	-	-	-
0.3004	39200	0.106	-	-	-
0.3011	39300	0.2754	-	-	-
0.3019	39400	0.2172	-	-	-
0.3027	39500	0.2081	-	-	-
0.3034	39600	0.1237	-	-	-
0.3042	39700	0.1699	-	-	-
0.3050	39800	0.3101	-	-	-
0.3057	39900	0.2217	-	-	-
0.3065	40000	0.0641	0.1541	0.9764	-
0.3073	40100	0.1466	-	-	-
0.3080	40200	0.1468	-	-	-
0.3088	40300	0.0891	-	-	-
0.3096	40400	0.0694	-	-	-
0.3103	40500	0.0993	-	-	-
0.3111	40600	0.0895	-	-	-
0.3119	40700	0.1036	-	-	-
0.3126	40800	0.1358	-	-	-
0.3134	40900	0.1809	-	-	-
0.3142	41000	0.0739	-	-	-
0.3149	41100	0.1942	-	-	-
0.3157	41200	0.5035	-	-	-
0.3165	41300	0.1967	-	-	-
0.3172	41400	0.2337	-	-	-
0.3180	41500	0.0589	-	-	-
0.3188	41600	0.0559	-	-	-
0.3195	41700	0.1349	-	-	-
0.3203	41800	0.1641	-	-	-
0.3211	41900	0.1014	-	-	-
0.3218	42000	0.0307	0.1494	0.9808	-
0.3226	42100	0.0804	-	-	-
0.3234	42200	0.1525	-	-	-
0.3241	42300	0.217	-	-	-
0.3249	42400	0.1217	-	-	-
0.3257	42500	0.1793	-	-	-
0.3264	42600	0.0749	-	-	-
0.3272	42700	0.1164	-	-	-
0.3280	42800	0.0354	-	-	-
0.3287	42900	0.0907	-	-	-
0.3295	43000	0.0859	-	-	-
0.3303	43100	0.0452	-	-	-
0.3310	43200	0.2408	-	-	-
0.3318	43300	0.1326	-	-	-
0.3326	43400	0.1982	-	-	-
0.3333	43500	0.0987	-	-	-
0.3341	43600	0.1097	-	-	-
0.3349	43700	0.1461	-	-	-
0.3356	43800	0.1902	-	-	-
0.3364	43900	0.1091	-	-	-
0.3372	44000	0.1655	0.2016	0.9634	-
0.3379	44100	0.2503	-	-	-
0.3387	44200	0.2033	-	-	-
0.3395	44300	0.1312	-	-	-
0.3402	44400	0.175	-	-	-
0.3410	44500	0.1357	-	-	-
0.3418	44600	0.1589	-	-	-
0.3425	44700	0.1093	-	-	-
0.3433	44800	0.0593	-	-	-
0.3441	44900	0.14	-	-	-
0.3448	45000	0.1669	-	-	-
0.3456	45100	0.0919	-	-	-
0.3464	45200	0.0479	-	-	-
0.3471	45300	0.1151	-	-	-
0.3479	45400	0.1353	-	-	-
0.3487	45500	0.1457	-	-	-
0.3494	45600	0.0952	-	-	-
0.3502	45700	0.149	-	-	-
0.3510	45800	0.1253	-	-	-
0.3517	45900	0.1249	-	-	-
0.3525	46000	0.1592	0.1187	0.98	-
0.3533	46100	0.3452	-	-	-
0.3540	46200	0.1351	-	-	-
0.3548	46300	0.0551	-	-	-
0.3556	46400	0.1676	-	-	-
0.3563	46500	0.1227	-	-	-
0.3571	46600	0.1381	-	-	-
0.3578	46700	0.177	-	-	-
0.3586	46800	0.1239	-	-	-
0.3594	46900	0.1014	-	-	-
0.3601	47000	0.1724	-	-	-
0.3609	47100	0.1838	-	-	-
0.3617	47200	0.1259	-	-	-
0.3624	47300	0.1161	-	-	-
0.3632	47400	0.1746	-	-	-
0.3640	47500	0.1764	-	-	-
0.3647	47600	0.1176	-	-	-
0.3655	47700	0.1461	-	-	-
0.3663	47800	0.0837	-	-	-
0.3670	47900	0.0984	-	-	-
0.3678	48000	0.0783	0.1882	0.9822	-
0.3686	48100	0.1031	-	-	-
0.3693	48200	0.1257	-	-	-
0.3701	48300	0.1874	-	-	-
0.3709	48400	0.1645	-	-	-
0.3716	48500	0.1352	-	-	-
0.3724	48600	0.1158	-	-	-
0.3732	48700	0.1724	-	-	-
0.3739	48800	0.0974	-	-	-
0.3747	48900	0.0827	-	-	-
0.3755	49000	0.2194	-	-	-
0.3762	49100	0.1576	-	-	-
0.3770	49200	0.1702	-	-	-
0.3778	49300	0.151	-	-	-
0.3785	49400	0.1416	-	-	-
0.3793	49500	0.1263	-	-	-
0.3801	49600	0.1186	-	-	-
0.3808	49700	0.241	-	-	-
0.3816	49800	0.1737	-	-	-
0.3824	49900	0.122	-	-	-
0.3831	50000	0.2243	0.0826	0.986	-
0.3839	50100	0.077	-	-	-
0.3847	50200	0.1728	-	-	-
0.3854	50300	0.0942	-	-	-
0.3862	50400	0.1689	-	-	-
0.3870	50500	0.2525	-	-	-
0.3877	50600	0.2081	-	-	-
0.3885	50700	0.0778	-	-	-
0.3893	50800	0.0302	-	-	-
0.3900	50900	0.0821	-	-	-
0.3908	51000	0.0442	-	-	-
0.3916	51100	0.0426	-	-	-
0.3923	51200	0.0611	-	-	-
0.3931	51300	0.0078	-	-	-
0.3939	51400	0.0823	-	-	-
0.3946	51500	0.1089	-	-	-
0.3954	51600	0.0427	-	-	-
0.3962	51700	0.0808	-	-	-
0.3969	51800	0.1833	-	-	-
0.3977	51900	0.1553	-	-	-
0.3985	52000	0.1002	0.2288	0.9742	-
0.3992	52100	0.0833	-	-	-
0.4000	52200	0.1126	-	-	-
0.4008	52300	0.1907	-	-	-
0.4015	52400	0.1434	-	-	-
0.4023	52500	0.0357	-	-	-
0.4031	52600	0.1061	-	-	-
0.4038	52700	0.0674	-	-	-
0.4046	52800	0.056	-	-	-
0.4054	52900	0.0328	-	-	-
0.4061	53000	0.0457	-	-	-
0.4069	53100	0.0608	-	-	-
0.4077	53200	0.0311	-	-	-
0.4084	53300	0.0597	-	-	-
0.4092	53400	0.0809	-	-	-
0.4100	53500	0.0371	-	-	-
0.4107	53600	0.1224	-	-	-
0.4115	53700	0.3256	-	-	-
0.4123	53800	0.1202	-	-	-
0.4130	53900	0.1193	-	-	-
0.4138	54000	0.0382	0.1721	0.9852	-
0.4146	54100	0.0395	-	-	-
0.4153	54200	0.1023	-	-	-
0.4161	54300	0.0929	-	-	-
0.4169	54400	0.0419	-	-	-
0.4176	54500	0.0178	-	-	-
0.4184	54600	0.0398	-	-	-
0.4192	54700	0.0949	-	-	-
0.4199	54800	0.1276	-	-	-
0.4207	54900	0.0598	-	-	-
0.4214	55000	0.1563	-	-	-
0.4222	55100	0.0404	-	-	-
0.4230	55200	0.0684	-	-	-
0.4237	55300	0.0203	-	-	-
0.4245	55400	0.0499	-	-	-
0.4253	55500	0.0574	-	-	-
0.4260	55600	0.0175	-	-	-
0.4268	55700	0.1218	-	-	-
0.4276	55800	0.0674	-	-	-
0.4283	55900	0.0784	-	-	-
0.4291	56000	0.0509	2.1277	0.861	-
0.4299	56100	0.0557	-	-	-
0.4306	56200	0.0875	-	-	-
0.4314	56300	0.089	-	-	-
0.4322	56400	0.081	-	-	-
0.4329	56500	0.051	-	-	-
0.4337	56600	0.1965	-	-	-
0.4345	56700	0.1703	-	-	-
0.4352	56800	1.0721	-	-	-
0.4360	56900	0.9794	-	-	-
0.4368	57000	1.0224	-	-	-
0.4375	57100	1.0802	-	-	-
0.4383	57200	1.0966	-	-	-
0.4391	57300	1.0528	-	-	-
0.4398	57400	1.0647	-	-	-
0.4406	57500	0.9738	-	-	-
0.4414	57600	1.0226	-	-	-
0.4421	57700	1.0012	-	-	-
0.4429	57800	1.0331	-	-	-
0.4437	57900	0.9854	-	-	-
0.4444	58000	1.0047	0.0744	0.9918	-
0.4452	58100	0.9554	-	-	-
0.4460	58200	0.9855	-	-	-
0.4467	58300	0.9454	-	-	-
0.4475	58400	0.9855	-	-	-
0.4483	58500	0.9331	-	-	-
0.4490	58600	0.933	-	-	-
0.4498	58700	0.9431	-	-	-
0.4506	58800	0.9434	-	-	-
0.4513	58900	0.9583	-	-	-
0.4521	59000	0.9204	-	-	-
0.4529	59100	0.9574	-	-	-
0.4536	59200	0.9714	-	-	-
0.4544	59300	0.9263	-	-	-
0.4552	59400	0.8828	-	-	-
0.4559	59500	0.9343	-	-	-
0.4567	59600	0.8743	-	-	-
0.4575	59700	0.9266	-	-	-
0.4582	59800	0.9097	-	-	-
0.4590	59900	0.9303	-	-	-
0.4598	60000	0.9452	0.1016	0.988	-
0.4605	60100	0.9241	-	-	-
0.4613	60200	0.8899	-	-	-
0.4621	60300	0.9122	-	-	-
0.4628	60400	0.8831	-	-	-
0.4636	60500	0.8785	-	-	-
0.4644	60600	0.9588	-	-	-
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0.6598	86100	0.8495	-	-	-
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0.7042	91900	0.8099	-	-	-
0.7050	92000	0.8047	0.1948	0.9554	-
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0.7203	94000	0.8212	0.1774	0.9622	-
0.7211	94100	0.7563	-	-	-
0.7218	94200	0.8104	-	-	-
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0.7280	95000	0.8314	-	-	-
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0.7356	96000	0.7869	0.1820	0.9574	-
0.7364	96100	0.7748	-	-	-
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0.7387	96400	0.7627	-	-	-
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0.7410	96700	0.7898	-	-	-
0.7418	96800	0.7804	-	-	-
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0.7440	97100	0.7542	-	-	-
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0.7494	97800	0.8171	-	-	-
0.7502	97900	0.7946	-	-	-
0.7509	98000	0.7825	0.1815	0.9586	-
0.7517	98100	0.7709	-	-	-
0.7525	98200	0.7646	-	-	-
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0.7540	98400	0.7812	-	-	-
0.7548	98500	0.7277	-	-	-
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0.7563	98700	0.8027	-	-	-
0.7571	98800	0.7509	-	-	-
0.7578	98900	0.7898	-	-	-
0.7586	99000	0.8319	-	-	-
0.7594	99100	0.7737	-	-	-
0.7601	99200	0.7546	-	-	-
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0.7640	99700	0.7827	-	-	-
0.7647	99800	0.7933	-	-	-
0.7655	99900	0.7767	-	-	-
0.7663	100000	0.7515	0.1655	0.9632	-
0.7670	100100	0.7787	-	-	-
0.7678	100200	0.7528	-	-	-
0.7686	100300	0.7858	-	-	-
0.7693	100400	0.7492	-	-	-
0.7701	100500	0.7622	-	-	-
0.7709	100600	0.7647	-	-	-
0.7716	100700	0.7822	-	-	-
0.7724	100800	0.7673	-	-	-
0.7732	100900	0.774	-	-	-
0.7739	101000	0.7627	-	-	-
0.7747	101100	0.7456	-	-	-
0.7755	101200	0.8082	-	-	-
0.7762	101300	0.773	-	-	-
0.7770	101400	0.779	-	-	-
0.7778	101500	0.7946	-	-	-
0.7785	101600	0.7823	-	-	-
0.7793	101700	0.7499	-	-	-
0.7801	101800	0.8175	-	-	-
0.7808	101900	0.8097	-	-	-
0.7816	102000	0.7561	0.1779	0.963	-
0.7824	102100	0.7691	-	-	-
0.7831	102200	0.784	-	-	-
0.7839	102300	0.7468	-	-	-
0.7847	102400	0.8237	-	-	-
0.7854	102500	0.7578	-	-	-
0.7862	102600	0.7622	-	-	-
0.7870	102700	0.844	-	-	-
0.7877	102800	0.8233	-	-	-
0.7885	102900	0.7852	-	-	-
0.7893	103000	0.8253	-	-	-
0.7900	103100	0.7684	-	-	-
0.7908	103200	0.7489	-	-	-
0.7916	103300	0.7767	-	-	-
0.7923	103400	0.7859	-	-	-
0.7931	103500	0.7739	-	-	-
0.7939	103600	0.7303	-	-	-
0.7946	103700	0.7546	-	-	-
0.7954	103800	0.7719	-	-	-
0.7962	103900	0.7511	-	-	-
0.7969	104000	0.7531	0.1643	0.9646	-
0.7977	104100	0.7297	-	-	-
0.7985	104200	0.7698	-	-	-
0.7992	104300	0.774	-	-	-
0.8000	104400	0.8124	-	-	-
0.8008	104500	0.8012	-	-	-
0.8015	104600	0.8163	-	-	-
0.8023	104700	0.7677	-	-	-
0.8031	104800	0.8017	-	-	-
0.8038	104900	0.7194	-	-	-
0.8046	105000	0.7623	-	-	-
0.8054	105100	0.7559	-	-	-
0.8061	105200	0.7735	-	-	-
0.8069	105300	0.7338	-	-	-
0.8077	105400	0.7104	-	-	-
0.8084	105500	0.7607	-	-	-
0.8092	105600	0.823	-	-	-
0.8099	105700	0.8029	-	-	-
0.8107	105800	0.7811	-	-	-
0.8115	105900	0.7794	-	-	-
0.8122	106000	0.7782	0.1784	0.961	-
0.8130	106100	0.7459	-	-	-
0.8138	106200	0.744	-	-	-
0.8145	106300	0.7681	-	-	-
0.8153	106400	0.7698	-	-	-
0.8161	106500	0.7359	-	-	-
0.8168	106600	0.781	-	-	-
0.8176	106700	0.7251	-	-	-
0.8184	106800	0.7478	-	-	-
0.8191	106900	0.7782	-	-	-
0.8199	107000	0.7464	-	-	-
0.8207	107100	0.6965	-	-	-
0.8214	107200	0.7368	-	-	-
0.8222	107300	0.7081	-	-	-
0.8230	107400	0.7037	-	-	-
0.8237	107500	0.6953	-	-	-
0.8245	107600	0.7169	-	-	-
0.8253	107700	0.7177	-	-	-
0.8260	107800	0.6925	-	-	-
0.8268	107900	0.6474	-	-	-
0.8276	108000	0.6675	0.1514	0.9684	-
0.8283	108100	0.6432	-	-	-
0.8291	108200	0.7523	-	-	-
0.8299	108300	0.6864	-	-	-
0.8306	108400	0.7036	-	-	-
0.8314	108500	0.6708	-	-	-
0.8322	108600	0.6739	-	-	-
0.8329	108700	0.6925	-	-	-
0.8337	108800	0.6101	-	-	-
0.8345	108900	0.653	-	-	-
0.8352	109000	0.664	-	-	-
0.8360	109100	0.6638	-	-	-
0.8368	109200	0.6587	-	-	-
0.8375	109300	0.6837	-	-	-
0.8383	109400	0.6813	-	-	-
0.8391	109500	0.6678	-	-	-
0.8398	109600	0.6601	-	-	-
0.8406	109700	0.61	-	-	-
0.8414	109800	0.6185	-	-	-
0.8421	109900	0.5919	-	-	-
0.8429	110000	0.6647	0.1559	0.9656	-
0.8437	110100	0.6891	-	-	-
0.8444	110200	0.652	-	-	-
0.8452	110300	0.6482	-	-	-
0.8460	110400	0.6493	-	-	-
0.8467	110500	0.5998	-	-	-
0.8475	110600	0.665	-	-	-
0.8483	110700	0.6228	-	-	-
0.8490	110800	0.6149	-	-	-
0.8498	110900	0.6488	-	-	-
0.8506	111000	0.61	-	-	-
0.8513	111100	0.657	-	-	-
0.8521	111200	0.638	-	-	-
0.8529	111300	0.6588	-	-	-
0.8536	111400	0.6086	-	-	-
0.8544	111500	0.6365	-	-	-
0.8552	111600	0.6066	-	-	-
0.8559	111700	0.663	-	-	-
0.8567	111800	0.5876	-	-	-
0.8575	111900	0.6153	-	-	-
0.8582	112000	0.6502	0.1445	0.9698	-
0.8590	112100	0.6061	-	-	-
0.8598	112200	0.6064	-	-	-
0.8605	112300	0.5988	-	-	-
0.8613	112400	0.5941	-	-	-
0.8621	112500	0.6372	-	-	-
0.8628	112600	0.652	-	-	-
0.8636	112700	0.5988	-	-	-
0.8644	112800	0.5789	-	-	-
0.8651	112900	0.5987	-	-	-
0.8659	113000	0.6204	-	-	-
0.8667	113100	0.6054	-	-	-
0.8674	113200	0.5752	-	-	-
0.8682	113300	0.6005	-	-	-
0.8690	113400	0.6158	-	-	-
0.8697	113500	0.6136	-	-	-
0.8705	113600	0.6227	-	-	-
0.8713	113700	0.6229	-	-	-
0.8720	113800	0.539	-	-	-
0.8728	113900	0.606	-	-	-
0.8735	114000	0.6278	0.1484	0.9696	-
0.8743	114100	0.595	-	-	-
0.8751	114200	0.5903	-	-	-
0.8758	114300	0.6173	-	-	-
0.8766	114400	0.6345	-	-	-
0.8774	114500	0.6205	-	-	-
0.8781	114600	0.5783	-	-	-
0.8789	114700	0.5859	-	-	-
0.8797	114800	0.588	-	-	-
0.8804	114900	0.601	-	-	-
0.8812	115000	0.5924	-	-	-
0.8820	115100	0.6528	-	-	-
0.8827	115200	0.6359	-	-	-
0.8835	115300	0.5895	-	-	-
0.8843	115400	0.5417	-	-	-
0.8850	115500	0.5728	-	-	-
0.8858	115600	0.5493	-	-	-
0.8866	115700	0.5687	-	-	-
0.8873	115800	0.5954	-	-	-
0.8881	115900	0.5786	-	-	-
0.8889	116000	0.6036	0.1424	0.9698	-
0.8896	116100	0.5575	-	-	-
0.8904	116200	0.5787	-	-	-
0.8912	116300	0.6071	-	-	-
0.8919	116400	0.5871	-	-	-
0.8927	116500	0.5929	-	-	-
0.8935	116600	0.5926	-	-	-
0.8942	116700	0.6003	-	-	-
0.8950	116800	0.5767	-	-	-
0.8958	116900	0.59	-	-	-
0.8965	117000	0.5877	-	-	-
0.8973	117100	0.5613	-	-	-
0.8981	117200	0.5706	-	-	-
0.8988	117300	0.5777	-	-	-
0.8996	117400	0.5986	-	-	-
0.9004	117500	0.611	-	-	-
0.9011	117600	0.5516	-	-	-
0.9019	117700	0.6038	-	-	-
0.9027	117800	0.5589	-	-	-
0.9034	117900	0.5935	-	-	-
0.9042	118000	0.5896	0.1477	0.9696	-
0.9050	118100	0.5737	-	-	-
0.9057	118200	0.5562	-	-	-
0.9065	118300	0.5624	-	-	-
0.9073	118400	0.6038	-	-	-
0.9080	118500	0.5552	-	-	-
0.9088	118600	0.5646	-	-	-
0.9096	118700	0.5629	-	-	-
0.9103	118800	0.5674	-	-	-
0.9111	118900	0.5716	-	-	-
0.9119	119000	0.5767	-	-	-
0.9126	119100	0.5352	-	-	-
0.9134	119200	0.59	-	-	-
0.9142	119300	0.5584	-	-	-
0.9149	119400	0.5769	-	-	-
0.9157	119500	0.5906	-	-	-
0.9165	119600	0.5807	-	-	-
0.9172	119700	0.5469	-	-	-
0.9180	119800	0.9169	-	-	-
0.9188	119900	0.9665	-	-	-
0.9195	120000	1.0132	0.1389	0.9704	-
0.9203	120100	0.9572	-	-	-
0.9211	120200	0.9023	-	-	-
0.9218	120300	0.6828	-	-	-
0.9226	120400	0.7277	-	-	-
0.9234	120500	0.7439	-	-	-
0.9241	120600	0.7554	-	-	-
0.9249	120700	0.7376	-	-	-
0.9257	120800	0.6783	-	-	-
0.9264	120900	0.7071	-	-	-
0.9272	121000	0.7251	-	-	-
0.9280	121100	0.7385	-	-	-
0.9287	121200	0.7207	-	-	-
0.9295	121300	0.7903	-	-	-
0.9303	121400	0.7863	-	-	-
0.9310	121500	0.7672	-	-	-
0.9318	121600	0.6873	-	-	-
0.9326	121700	0.7526	-	-	-
0.9333	121800	0.764	-	-	-
0.9341	121900	0.7827	-	-	-
0.9349	122000	0.8713	0.1329	0.9706	-
0.9356	122100	0.7872	-	-	-
0.9364	122200	0.6837	-	-	-
0.9372	122300	0.6017	-	-	-
0.9379	122400	0.6283	-	-	-
0.9387	122500	0.758	-	-	-
0.9394	122600	0.694	-	-	-
0.9402	122700	0.7112	-	-	-
0.9410	122800	0.7566	-	-	-
0.9417	122900	0.7118	-	-	-
0.9425	123000	0.6594	-	-	-
0.9433	123100	0.678	-	-	-
0.9440	123200	0.6626	-	-	-
0.9448	123300	0.6724	-	-	-
0.9456	123400	0.7042	-	-	-
0.9463	123500	0.6526	-	-	-
0.9471	123600	0.7039	-	-	-
0.9479	123700	0.6459	-	-	-
0.9486	123800	0.5759	-	-	-
0.9494	123900	0.6211	-	-	-
0.9502	124000	0.6905	0.1363	0.9698	-
0.9509	124100	0.6422	-	-	-
0.9517	124200	0.668	-	-	-
0.9525	124300	0.5819	-	-	-
0.9532	124400	0.661	-	-	-
0.9540	124500	0.6243	-	-	-
0.9548	124600	0.5936	-	-	-
0.9555	124700	0.5736	-	-	-
0.9563	124800	0.5955	-	-	-
0.9571	124900	0.5115	-	-	-
0.9578	125000	0.5495	-	-	-
0.9586	125100	0.5858	-	-	-
0.9594	125200	0.5644	-	-	-
0.9601	125300	0.5262	-	-	-
0.9609	125400	0.5588	-	-	-
0.9617	125500	0.7303	-	-	-
0.9624	125600	0.6894	-	-	-
0.9632	125700	0.5998	-	-	-
0.9640	125800	0.6216	-	-	-
0.9647	125900	0.5922	-	-	-
0.9655	126000	0.6121	0.1362	0.9694	-
0.9663	126100	0.5996	-	-	-
0.9670	126200	0.5894	-	-	-
0.9678	126300	0.5347	-	-	-
0.9686	126400	0.5803	-	-	-
0.9693	126500	0.6104	-	-	-
0.9701	126600	0.7723	-	-	-
0.9709	126700	0.7577	-	-	-
0.9716	126800	0.7315	-	-	-
0.9724	126900	0.7341	-	-	-
0.9732	127000	0.7652	-	-	-
0.9739	127100	0.6364	-	-	-
0.9747	127200	0.7246	-	-	-
0.9755	127300	1.2764	-	-	-
0.9762	127400	1.0487	-	-	-
0.9770	127500	0.9892	-	-	-
0.9778	127600	0.767	-	-	-
0.9785	127700	0.7633	-	-	-
0.9793	127800	0.8695	-	-	-
0.9801	127900	0.648	-	-	-
0.9808	128000	0.6227	0.1418	0.97	-
0.9816	128100	0.5542	-	-	-
0.9824	128200	0.5535	-	-	-
0.9831	128300	0.7327	-	-	-
0.9839	128400	0.6541	-	-	-
0.9847	128500	0.6697	-	-	-
0.9854	128600	0.5708	-	-	-
0.9862	128700	0.6833	-	-	-
0.9870	128800	0.6328	-	-	-
0.9877	128900	0.7026	-	-	-
0.9885	129000	0.5497	-	-	-
0.9893	129100	0.5846	-	-	-
0.9900	129200	0.5708	-	-	-
0.9908	129300	0.7514	-	-	-
0.9916	129400	0.5386	-	-	-
0.9923	129500	0.7419	-	-	-
0.9931	129600	0.8613	-	-	-
0.9939	129700	0.8322	-	-	-
0.9946	129800	0.7606	-	-	-
0.9954	129900	0.7086	-	-	-
0.9962	130000	0.6828	0.1488	0.9688	-
0.9969	130100	0.8267	-	-	-
0.9977	130200	0.8491	-	-	-
0.9985	130300	1.0619	-	-	-
0.9992	130400	0.8276	-	-	-
1.0000	130500	0.8043	-	-	-
1.0	130502	-	-	-	0.9726

Framework Versions

Python: 3.11.8
Sentence Transformers: 3.1.1
Transformers: 4.44.0
PyTorch: 2.3.0.post101
Accelerate: 0.33.0
Datasets: 3.0.2
Tokenizers: 0.19.0

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}
}