SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the en-ja dataset. 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: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- en-ja

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': 128, 'do_lower_case': False}) with Transformer model: 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    "We'll apply that throughout our global supply chain regardless of ownership or control.",
    '私たちはこれを グローバル・サプライチェーンにおいて 所有権や支配権に関係なく適用します。',
    '浸透していません',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Knowledge Distillation

Dataset: en-ja
Evaluated with MSEEvaluator

Metric	Value
negative_mse	-0.1723

Translation

Dataset: en-ja
Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.6883
trg2src_accuracy	0.6617
mean_accuracy	0.675

Training Details

Training Dataset

en-ja

Dataset: en-ja
Size: 8,692,806 training samples
Columns: english, non_english, and label
Approximate statistics based on the first 1000 samples:
english non_english label
type string string list
details
min: 8 tokens
mean: 72.37 tokens
max: 128 tokens

min: 4 tokens
mean: 37.76 tokens
max: 128 tokens

size: 384 elements

	english	non_english	label
type	string	string	list
details	min: 8 tokens mean: 72.37 tokens max: 128 tokens	min: 4 tokens mean: 37.76 tokens max: 128 tokens	size: 384 elements

Samples:

english	non_english	label
`Are the basics of driving a car learning how to service it, or design it for that matter?`	`車の運転の基礎は点検の仕方？それともデザインの仕方？`	`[0.04097634181380272, 0.015725482255220413, 0.04093917831778526, 0.005089071579277515, -0.008469141088426113, ...]`
`Are the basics of writing learning how to sharpen a quill?`	`執筆の基礎は羽ペンの削り方？`	`[-0.0036177693400532007, 0.010684962384402752, -0.014135013334453106, -0.05535861477255821, -0.08116177469491959, ...]`
`I don't think so.`	`違うと思います`	`[-0.07840073108673096, -0.0229326281696558, -0.012929541990160942, -0.007635382004082203, -0.04817994683980942, ...]`

Loss: MSELoss

Evaluation Dataset

en-ja

Dataset: en-ja
Size: 7,244 evaluation samples
Columns: english, non_english, and label
Approximate statistics based on the first 1000 samples:
english non_english label
type string string list
details
min: 5 tokens
mean: 77.14 tokens
max: 128 tokens

min: 4 tokens
mean: 42.63 tokens
max: 128 tokens

size: 384 elements

	english	non_english	label
type	string	string	list
details	min: 5 tokens mean: 77.14 tokens max: 128 tokens	min: 4 tokens mean: 42.63 tokens max: 128 tokens	size: 384 elements

Samples:

english	non_english	label
`Thank you so much, Chris.`	`どうもありがとうクリスこのステージに立てる機会を`	`[0.02692059800028801, 0.05314800143241882, 0.14048902690410614, -0.10380179435014725, -0.04118778929114342, ...]`
`And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.`	`2度もいただけるというのは実に光栄なことでとてもうれしく思っています`	`[0.024387270212173462, 0.09500126540660858, 0.12180333584547043, -0.07149269431829453, -0.018444567918777466, ...]`
`I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.`	`このカンファレンスには圧倒されっぱなしです皆さんから― 前回の講演に対していただいた温かいコメントにお礼を申し上げたい`	`[0.015005433931946754, 0.014678305946290493, 0.13112004101276398, 0.03133269399404526, 0.06942533701658249, ...]`

Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
gradient_accumulation_steps: 4
learning_rate: 0.0003
num_train_epochs: 10
warmup_ratio: 0.15
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0003
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.15
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: 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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	en-ja loss	en-ja_negative_mse	en-ja_mean_accuracy
0.0118	100	0.0049	-	-	-
0.0236	200	0.004	-	-	-
0.0353	300	0.0038	-	-	-
0.0471	400	0.0037	-	-	-
0.0589	500	0.0037	-	-	-
0.0707	600	0.0037	-	-	-
0.0825	700	0.0036	-	-	-
0.0942	800	0.0036	-	-	-
0.1060	900	0.0035	-	-	-
0.1178	1000	0.0035	0.0034	-0.34472314	0.0121
0.1296	1100	0.0035	-	-	-
0.1414	1200	0.0034	-	-	-
0.1531	1300	0.0034	-	-	-
0.1649	1400	0.0034	-	-	-
0.1767	1500	0.0033	-	-	-
0.1885	1600	0.0033	-	-	-
0.2003	1700	0.0032	-	-	-
0.2120	1800	0.0032	-	-	-
0.2238	1900	0.0032	-	-	-
0.2356	2000	0.0031	0.0031	-0.3021978	0.0279
0.2474	2100	0.0031	-	-	-
0.2592	2200	0.0031	-	-	-
0.2709	2300	0.0031	-	-	-
0.2827	2400	0.003	-	-	-
0.2945	2500	0.003	-	-	-
0.3063	2600	0.003	-	-	-
0.3180	2700	0.0029	-	-	-
0.3298	2800	0.0029	-	-	-
0.3416	2900	0.0029	-	-	-
0.3534	3000	0.0028	0.0027	-0.27366027	0.0888
0.3652	3100	0.0028	-	-	-
0.3769	3200	0.0028	-	-	-
0.3887	3300	0.0027	-	-	-
0.4005	3400	0.0027	-	-	-
0.4123	3500	0.0027	-	-	-
0.4241	3600	0.0026	-	-	-
0.4358	3700	0.0026	-	-	-
0.4476	3800	0.0026	-	-	-
0.4594	3900	0.0025	-	-	-
0.4712	4000	0.0025	0.0024	-0.25388333	0.2314
0.4830	4100	0.0025	-	-	-
0.4947	4200	0.0024	-	-	-
0.5065	4300	0.0024	-	-	-
0.5183	4400	0.0024	-	-	-
0.5301	4500	0.0023	-	-	-
0.5419	4600	0.0023	-	-	-
0.5536	4700	0.0023	-	-	-
0.5654	4800	0.0023	-	-	-
0.5772	4900	0.0022	-	-	-
0.5890	5000	0.0022	0.0021	-0.23925437	0.3782
0.6008	5100	0.0022	-	-	-
0.6125	5200	0.0022	-	-	-
0.6243	5300	0.0021	-	-	-
0.6361	5400	0.0021	-	-	-
0.6479	5500	0.0021	-	-	-
0.6597	5600	0.0021	-	-	-
0.6714	5700	0.0021	-	-	-
0.6832	5800	0.002	-	-	-
0.6950	5900	0.002	-	-	-
0.7068	6000	0.002	0.0020	-0.22806446	0.4660
0.7186	6100	0.002	-	-	-
0.7303	6200	0.002	-	-	-
0.7421	6300	0.002	-	-	-
0.7539	6400	0.0019	-	-	-
0.7657	6500	0.0019	-	-	-
0.7775	6600	0.0019	-	-	-
0.7892	6700	0.0019	-	-	-
0.8010	6800	0.0019	-	-	-
0.8128	6900	0.0019	-	-	-
0.8246	7000	0.0018	0.0018	-0.2197086	0.5271
0.8364	7100	0.0018	-	-	-
0.8481	7200	0.0018	-	-	-
0.8599	7300	0.0018	-	-	-
0.8717	7400	0.0018	-	-	-
0.8835	7500	0.0018	-	-	-
0.8952	7600	0.0018	-	-	-
0.9070	7700	0.0018	-	-	-
0.9188	7800	0.0018	-	-	-
0.9306	7900	0.0018	-	-	-
0.9424	8000	0.0017	0.0017	-0.21387897	0.5574
0.9541	8100	0.0017	-	-	-
0.9659	8200	0.0017	-	-	-
0.9777	8300	0.0017	-	-	-
0.9895	8400	0.0017	-	-	-
1.0012	8500	0.0017	-	-	-
1.0130	8600	0.0017	-	-	-
1.0247	8700	0.0017	-	-	-
1.0365	8800	0.0017	-	-	-
1.0483	8900	0.0017	-	-	-
1.0601	9000	0.0017	0.0016	-0.2085446	0.5752
1.0719	9100	0.0016	-	-	-
1.0836	9200	0.0016	-	-	-
1.0954	9300	0.0016	-	-	-
1.1072	9400	0.0016	-	-	-
1.1190	9500	0.0016	-	-	-
1.1308	9600	0.0016	-	-	-
1.1425	9700	0.0016	-	-	-
1.1543	9800	0.0016	-	-	-
1.1661	9900	0.0016	-	-	-
1.1779	10000	0.0016	0.0016	-0.20458691	0.5946
1.1897	10100	0.0016	-	-	-
1.2014	10200	0.0016	-	-	-
1.2132	10300	0.0016	-	-	-
1.2250	10400	0.0016	-	-	-
1.2368	10500	0.0016	-	-	-
1.2485	10600	0.0016	-	-	-
1.2603	10700	0.0015	-	-	-
1.2721	10800	0.0015	-	-	-
1.2839	10900	0.0015	-	-	-
1.2957	11000	0.0015	0.0015	-0.20193738	0.6010
1.3074	11100	0.0015	-	-	-
1.3192	11200	0.0015	-	-	-
1.3310	11300	0.0015	-	-	-
1.3428	11400	0.0015	-	-	-
1.3546	11500	0.0015	-	-	-
1.3663	11600	0.0015	-	-	-
1.3781	11700	0.0015	-	-	-
1.3899	11800	0.0015	-	-	-
1.4017	11900	0.0015	-	-	-
1.4135	12000	0.0015	0.0015	-0.19939835	0.6132
1.4252	12100	0.0015	-	-	-
1.4370	12200	0.0015	-	-	-
1.4488	12300	0.0015	-	-	-
1.4606	12400	0.0015	-	-	-
1.4724	12500	0.0015	-	-	-
1.4841	12600	0.0015	-	-	-
1.4959	12700	0.0015	-	-	-
1.5077	12800	0.0015	-	-	-
1.5195	12900	0.0015	-	-	-
1.5313	13000	0.0015	0.0014	-0.19621891	0.6252
1.5430	13100	0.0014	-	-	-
1.5548	13200	0.0014	-	-	-
1.5666	13300	0.0014	-	-	-
1.5784	13400	0.0014	-	-	-
1.5902	13500	0.0014	-	-	-
1.6019	13600	0.0014	-	-	-
1.6137	13700	0.0014	-	-	-
1.6255	13800	0.0014	-	-	-
1.6373	13900	0.0014	-	-	-
1.6491	14000	0.0014	0.0014	-0.19430138	0.6285
1.6608	14100	0.0014	-	-	-
1.6726	14200	0.0014	-	-	-
1.6844	14300	0.0014	-	-	-
1.6962	14400	0.0014	-	-	-
1.7080	14500	0.0014	-	-	-
1.7197	14600	0.0014	-	-	-
1.7315	14700	0.0014	-	-	-
1.7433	14800	0.0014	-	-	-
1.7551	14900	0.0014	-	-	-
1.7669	15000	0.0014	0.0014	-0.19249634	0.6341
1.7786	15100	0.0014	-	-	-
1.7904	15200	0.0014	-	-	-
1.8022	15300	0.0014	-	-	-
1.8140	15400	0.0014	-	-	-
1.8258	15500	0.0014	-	-	-
1.8375	15600	0.0014	-	-	-
1.8493	15700	0.0014	-	-	-
1.8611	15800	0.0014	-	-	-
1.8729	15900	0.0014	-	-	-
1.8846	16000	0.0014	0.0014	-0.19071046	0.6386
1.8964	16100	0.0014	-	-	-
1.9082	16200	0.0014	-	-	-
1.9200	16300	0.0014	-	-	-
1.9318	16400	0.0014	-	-	-
1.9435	16500	0.0014	-	-	-
1.9553	16600	0.0014	-	-	-
1.9671	16700	0.0014	-	-	-
1.9789	16800	0.0014	-	-	-
1.9907	16900	0.0014	-	-	-
2.0024	17000	0.0013	0.0014	-0.1893078	0.6416
2.0141	17100	0.0013	-	-	-
2.0259	17200	0.0013	-	-	-
2.0377	17300	0.0013	-	-	-
2.0495	17400	0.0013	-	-	-
2.0613	17500	0.0013	-	-	-
2.0730	17600	0.0013	-	-	-
2.0848	17700	0.0013	-	-	-
2.0966	17800	0.0013	-	-	-
2.1084	17900	0.0013	-	-	-
2.1202	18000	0.0013	0.0013	-0.188142	0.6453
2.1319	18100	0.0013	-	-	-
2.1437	18200	0.0013	-	-	-
2.1555	18300	0.0013	-	-	-
2.1673	18400	0.0013	-	-	-
2.1790	18500	0.0013	-	-	-
2.1908	18600	0.0013	-	-	-
2.2026	18700	0.0013	-	-	-
2.2144	18800	0.0013	-	-	-
2.2262	18900	0.0013	-	-	-
2.2379	19000	0.0013	0.0013	-0.18757328	0.6469
2.2497	19100	0.0013	-	-	-
2.2615	19200	0.0013	-	-	-
2.2733	19300	0.0013	-	-	-
2.2851	19400	0.0013	-	-	-
2.2968	19500	0.0013	-	-	-
2.3086	19600	0.0013	-	-	-
2.3204	19700	0.0013	-	-	-
2.3322	19800	0.0013	-	-	-
2.3440	19900	0.0013	-	-	-
2.3557	20000	0.0013	0.0013	-0.18630134	0.6434
2.3675	20100	0.0013	-	-	-
2.3793	20200	0.0013	-	-	-
2.3911	20300	0.0013	-	-	-
2.4029	20400	0.0013	-	-	-
2.4146	20500	0.0013	-	-	-
2.4264	20600	0.0013	-	-	-
2.4382	20700	0.0013	-	-	-
2.4500	20800	0.0013	-	-	-
2.4618	20900	0.0013	-	-	-
2.4735	21000	0.0013	0.0013	-0.18556643	0.6488
2.4853	21100	0.0013	-	-	-
2.4971	21200	0.0013	-	-	-
2.5089	21300	0.0013	-	-	-
2.5207	21400	0.0013	-	-	-
2.5324	21500	0.0013	-	-	-
2.5442	21600	0.0013	-	-	-
2.5560	21700	0.0013	-	-	-
2.5678	21800	0.0013	-	-	-
2.5796	21900	0.0013	-	-	-
2.5913	22000	0.0013	0.0013	-0.18505765	0.6485
2.6031	22100	0.0013	-	-	-
2.6149	22200	0.0013	-	-	-
2.6267	22300	0.0013	-	-	-
2.6385	22400	0.0013	-	-	-
2.6502	22500	0.0013	-	-	-
2.6620	22600	0.0013	-	-	-
2.6738	22700	0.0013	-	-	-
2.6856	22800	0.0013	-	-	-
2.6974	22900	0.0013	-	-	-
2.7091	23000	0.0013	0.0013	-0.18397436	0.6518
2.7209	23100	0.0013	-	-	-
2.7327	23200	0.0013	-	-	-
2.7445	23300	0.0013	-	-	-
2.7563	23400	0.0013	-	-	-
2.7680	23500	0.0013	-	-	-
2.7798	23600	0.0013	-	-	-
2.7916	23700	0.0013	-	-	-
2.8034	23800	0.0013	-	-	-
2.8151	23900	0.0013	-	-	-
2.8269	24000	0.0013	0.0013	-0.18347077	0.6547
2.8387	24100	0.0013	-	-	-
2.8505	24200	0.0013	-	-	-
2.8623	24300	0.0013	-	-	-
2.8740	24400	0.0013	-	-	-
2.8858	24500	0.0013	-	-	-
2.8976	24600	0.0013	-	-	-
2.9094	24700	0.0013	-	-	-
2.9212	24800	0.0013	-	-	-
2.9329	24900	0.0013	-	-	-
2.9447	25000	0.0013	0.0013	-0.18285994	0.6518
2.9565	25100	0.0013	-	-	-
2.9683	25200	0.0013	-	-	-
2.9801	25300	0.0013	-	-	-
2.9918	25400	0.0013	-	-	-
3.0035	25500	0.0012	-	-	-
3.0153	25600	0.0013	-	-	-
3.0271	25700	0.0012	-	-	-
3.0389	25800	0.0013	-	-	-
3.0507	25900	0.0012	-	-	-
3.0624	26000	0.0012	0.0013	-0.18248549	0.6536
3.0742	26100	0.0012	-	-	-
3.0860	26200	0.0012	-	-	-
3.0978	26300	0.0012	-	-	-
3.1096	26400	0.0012	-	-	-
3.1213	26500	0.0012	-	-	-
3.1331	26600	0.0012	-	-	-
3.1449	26700	0.0012	-	-	-
3.1567	26800	0.0012	-	-	-
3.1684	26900	0.0012	-	-	-
3.1802	27000	0.0012	0.0013	-0.1820667	0.6569
3.1920	27100	0.0012	-	-	-
3.2038	27200	0.0012	-	-	-
3.2156	27300	0.0012	-	-	-
3.2273	27400	0.0012	-	-	-
3.2391	27500	0.0012	-	-	-
3.2509	27600	0.0012	-	-	-
3.2627	27700	0.0012	-	-	-
3.2745	27800	0.0012	-	-	-
3.2862	27900	0.0012	-	-	-
3.2980	28000	0.0012	0.0013	-0.18136427	0.6596
3.3098	28100	0.0012	-	-	-
3.3216	28200	0.0012	-	-	-
3.3334	28300	0.0012	-	-	-
3.3451	28400	0.0012	-	-	-
3.3569	28500	0.0012	-	-	-
3.3687	28600	0.0012	-	-	-
3.3805	28700	0.0012	-	-	-
3.3923	28800	0.0012	-	-	-
3.4040	28900	0.0012	-	-	-
3.4158	29000	0.0012	0.0012	-0.18090644	0.6586
3.4276	29100	0.0012	-	-	-
3.4394	29200	0.0012	-	-	-
3.4512	29300	0.0012	-	-	-
3.4629	29400	0.0012	-	-	-
3.4747	29500	0.0012	-	-	-
3.4865	29600	0.0012	-	-	-
3.4983	29700	0.0012	-	-	-
3.5101	29800	0.0012	-	-	-
3.5218	29900	0.0012	-	-	-
3.5336	30000	0.0012	0.0012	-0.18087307	0.6602
3.5454	30100	0.0012	-	-	-
3.5572	30200	0.0012	-	-	-
3.5690	30300	0.0012	-	-	-
3.5807	30400	0.0012	-	-	-
3.5925	30500	0.0012	-	-	-
3.6043	30600	0.0012	-	-	-
3.6161	30700	0.0012	-	-	-
3.6279	30800	0.0012	-	-	-
3.6396	30900	0.0012	-	-	-
3.6514	31000	0.0012	0.0012	-0.17998679	0.6612
3.6632	31100	0.0012	-	-	-
3.6750	31200	0.0012	-	-	-
3.6868	31300	0.0012	-	-	-
3.6985	31400	0.0012	-	-	-
3.7103	31500	0.0012	-	-	-
3.7221	31600	0.0012	-	-	-
3.7339	31700	0.0012	-	-	-
3.7456	31800	0.0012	-	-	-
3.7574	31900	0.0012	-	-	-
3.7692	32000	0.0012	0.0012	-0.17991492	0.6630
3.7810	32100	0.0012	-	-	-
3.7928	32200	0.0012	-	-	-
3.8045	32300	0.0012	-	-	-
3.8163	32400	0.0012	-	-	-
3.8281	32500	0.0012	-	-	-
3.8399	32600	0.0012	-	-	-
3.8517	32700	0.0012	-	-	-
3.8634	32800	0.0012	-	-	-
3.8752	32900	0.0012	-	-	-
3.8870	33000	0.0012	0.0012	-0.1794728	0.6632
3.8988	33100	0.0012	-	-	-
3.9106	33200	0.0012	-	-	-
3.9223	33300	0.0012	-	-	-
3.9341	33400	0.0012	-	-	-
3.9459	33500	0.0012	-	-	-
3.9577	33600	0.0012	-	-	-
3.9695	33700	0.0012	-	-	-
3.9812	33800	0.0012	-	-	-
3.9930	33900	0.0012	-	-	-
4.0047	34000	0.0012	0.0012	-0.17887509	0.6643
4.0165	34100	0.0012	-	-	-
4.0283	34200	0.0012	-	-	-
4.0401	34300	0.0012	-	-	-
4.0518	34400	0.0012	-	-	-
4.0636	34500	0.0012	-	-	-
4.0754	34600	0.0012	-	-	-
4.0872	34700	0.0012	-	-	-
4.0989	34800	0.0012	-	-	-
4.1107	34900	0.0012	-	-	-
4.1225	35000	0.0012	0.0012	-0.17876983	0.6648
4.1343	35100	0.0012	-	-	-
4.1461	35200	0.0012	-	-	-
4.1578	35300	0.0012	-	-	-
4.1696	35400	0.0012	-	-	-
4.1814	35500	0.0012	-	-	-
4.1932	35600	0.0012	-	-	-
4.2050	35700	0.0012	-	-	-
4.2167	35800	0.0012	-	-	-
4.2285	35900	0.0012	-	-	-
4.2403	36000	0.0012	0.0012	-0.17866188	0.6629
4.2521	36100	0.0012	-	-	-
4.2639	36200	0.0012	-	-	-
4.2756	36300	0.0012	-	-	-
4.2874	36400	0.0012	-	-	-
4.2992	36500	0.0012	-	-	-
4.3110	36600	0.0012	-	-	-
4.3228	36700	0.0012	-	-	-
4.3345	36800	0.0012	-	-	-
4.3463	36900	0.0012	-	-	-
4.3581	37000	0.0012	0.0012	-0.17826374	0.6642
4.3699	37100	0.0012	-	-	-
4.3817	37200	0.0012	-	-	-
4.3934	37300	0.0012	-	-	-
4.4052	37400	0.0012	-	-	-
4.4170	37500	0.0012	-	-	-
4.4288	37600	0.0012	-	-	-
4.4406	37700	0.0012	-	-	-
4.4523	37800	0.0012	-	-	-
4.4641	37900	0.0012	-	-	-
4.4759	38000	0.0012	0.0012	-0.17813009	0.6654
4.4877	38100	0.0012	-	-	-
4.4995	38200	0.0012	-	-	-
4.5112	38300	0.0012	-	-	-
4.5230	38400	0.0012	-	-	-
4.5348	38500	0.0012	-	-	-
4.5466	38600	0.0012	-	-	-
4.5584	38700	0.0012	-	-	-
4.5701	38800	0.0012	-	-	-
4.5819	38900	0.0012	-	-	-
4.5937	39000	0.0012	0.0012	-0.17787422	0.6677
4.6055	39100	0.0012	-	-	-
4.6173	39200	0.0012	-	-	-
4.6290	39300	0.0012	-	-	-
4.6408	39400	0.0012	-	-	-
4.6526	39500	0.0012	-	-	-
4.6644	39600	0.0012	-	-	-
4.6761	39700	0.0012	-	-	-
4.6879	39800	0.0012	-	-	-
4.6997	39900	0.0012	-	-	-
4.7115	40000	0.0012	0.0012	-0.17763866	0.6631
4.7233	40100	0.0012	-	-	-
4.7350	40200	0.0012	-	-	-
4.7468	40300	0.0012	-	-	-
4.7586	40400	0.0012	-	-	-
4.7704	40500	0.0012	-	-	-
4.7822	40600	0.0012	-	-	-
4.7939	40700	0.0012	-	-	-
4.8057	40800	0.0012	-	-	-
4.8175	40900	0.0012	-	-	-
4.8293	41000	0.0012	0.0012	-0.17747028	0.6683
4.8411	41100	0.0012	-	-	-
4.8528	41200	0.0012	-	-	-
4.8646	41300	0.0012	-	-	-
4.8764	41400	0.0012	-	-	-
4.8882	41500	0.0012	-	-	-
4.9000	41600	0.0012	-	-	-
4.9117	41700	0.0012	-	-	-
4.9235	41800	0.0012	-	-	-
4.9353	41900	0.0012	-	-	-
4.9471	42000	0.0012	0.0012	-0.1769735	0.6677
4.9589	42100	0.0012	-	-	-
4.9706	42200	0.0012	-	-	-
4.9824	42300	0.0012	-	-	-
4.9942	42400	0.0012	-	-	-
5.0059	42500	0.0012	-	-	-
5.0177	42600	0.0012	-	-	-
5.0294	42700	0.0012	-	-	-
5.0412	42800	0.0012	-	-	-
5.0530	42900	0.0012	-	-	-
5.0648	43000	0.0012	0.0012	-0.17719762	0.6653
5.0766	43100	0.0012	-	-	-
5.0883	43200	0.0012	-	-	-
5.1001	43300	0.0012	-	-	-
5.1119	43400	0.0012	-	-	-
5.1237	43500	0.0012	-	-	-
5.1355	43600	0.0012	-	-	-
5.1472	43700	0.0012	-	-	-
5.1590	43800	0.0012	-	-	-
5.1708	43900	0.0012	-	-	-
5.1826	44000	0.0012	0.0012	-0.17653656	0.6693
5.1944	44100	0.0012	-	-	-
5.2061	44200	0.0012	-	-	-
5.2179	44300	0.0012	-	-	-
5.2297	44400	0.0012	-	-	-
5.2415	44500	0.0012	-	-	-
5.2533	44600	0.0012	-	-	-
5.2650	44700	0.0012	-	-	-
5.2768	44800	0.0012	-	-	-
5.2886	44900	0.0012	-	-	-
5.3004	45000	0.0012	0.0012	-0.17657608	0.6683
5.3122	45100	0.0012	-	-	-
5.3239	45200	0.0012	-	-	-
5.3357	45300	0.0012	-	-	-
5.3475	45400	0.0012	-	-	-
5.3593	45500	0.0012	-	-	-
5.3711	45600	0.0012	-	-	-
5.3828	45700	0.0012	-	-	-
5.3946	45800	0.0012	-	-	-
5.4064	45900	0.0012	-	-	-
5.4182	46000	0.0012	0.0012	-0.17643414	0.6694
5.4300	46100	0.0012	-	-	-
5.4417	46200	0.0012	-	-	-
5.4535	46300	0.0012	-	-	-
5.4653	46400	0.0012	-	-	-
5.4771	46500	0.0012	-	-	-
5.4889	46600	0.0012	-	-	-
5.5006	46700	0.0012	-	-	-
5.5124	46800	0.0012	-	-	-
5.5242	46900	0.0012	-	-	-
5.5360	47000	0.0012	0.0012	-0.17619923	0.6679
5.5478	47100	0.0012	-	-	-
5.5595	47200	0.0012	-	-	-
5.5713	47300	0.0012	-	-	-
5.5831	47400	0.0012	-	-	-
5.5949	47500	0.0012	-	-	-
5.6066	47600	0.0012	-	-	-
5.6184	47700	0.0012	-	-	-
5.6302	47800	0.0012	-	-	-
5.6420	47900	0.0012	-	-	-
5.6538	48000	0.0012	0.0012	-0.17586452	0.6694
5.6655	48100	0.0012	-	-	-
5.6773	48200	0.0012	-	-	-
5.6891	48300	0.0012	-	-	-
5.7009	48400	0.0012	-	-	-
5.7127	48500	0.0012	-	-	-
5.7244	48600	0.0012	-	-	-
5.7362	48700	0.0012	-	-	-
5.7480	48800	0.0012	-	-	-
5.7598	48900	0.0012	-	-	-
5.7716	49000	0.0012	0.0012	-0.1760546	0.6687
5.7833	49100	0.0012	-	-	-
5.7951	49200	0.0012	-	-	-
5.8069	49300	0.0012	-	-	-
5.8187	49400	0.0012	-	-	-
5.8305	49500	0.0012	-	-	-
5.8422	49600	0.0012	-	-	-
5.8540	49700	0.0012	-	-	-
5.8658	49800	0.0012	-	-	-
5.8776	49900	0.0012	-	-	-
5.8894	50000	0.0012	0.0012	-0.17587146	0.6677
5.9011	50100	0.0012	-	-	-
5.9129	50200	0.0012	-	-	-
5.9247	50300	0.0012	-	-	-
5.9365	50400	0.0012	-	-	-
5.9483	50500	0.0012	-	-	-
5.9600	50600	0.0012	-	-	-
5.9718	50700	0.0012	-	-	-
5.9836	50800	0.0012	-	-	-
5.9954	50900	0.0012	-	-	-
6.0071	51000	0.0011	0.0012	-0.17524663	0.6717
6.0188	51100	0.0012	-	-	-
6.0306	51200	0.0012	-	-	-
6.0424	51300	0.0012	-	-	-
6.0542	51400	0.0012	-	-	-
6.0660	51500	0.0012	-	-	-
6.0777	51600	0.0012	-	-	-
6.0895	51700	0.0012	-	-	-
6.1013	51800	0.0012	-	-	-
6.1131	51900	0.0012	-	-	-
6.1249	52000	0.0012	0.0012	-0.17546739	0.6696
6.1366	52100	0.0012	-	-	-
6.1484	52200	0.0012	-	-	-
6.1602	52300	0.0012	-	-	-
6.1720	52400	0.0012	-	-	-
6.1838	52500	0.0012	-	-	-
6.1955	52600	0.0012	-	-	-
6.2073	52700	0.0012	-	-	-
6.2191	52800	0.0012	-	-	-
6.2309	52900	0.0012	-	-	-
6.2427	53000	0.0012	0.0012	-0.17500012	0.6687
6.2544	53100	0.0012	-	-	-
6.2662	53200	0.0012	-	-	-
6.2780	53300	0.0012	-	-	-
6.2898	53400	0.0012	-	-	-
6.3016	53500	0.0011	-	-	-
6.3133	53600	0.0012	-	-	-
6.3251	53700	0.0012	-	-	-
6.3369	53800	0.0012	-	-	-
6.3487	53900	0.0012	-	-	-
6.3605	54000	0.0012	0.0012	-0.1750529	0.6728
6.3722	54100	0.0012	-	-	-
6.3840	54200	0.0012	-	-	-
6.3958	54300	0.0012	-	-	-
6.4076	54400	0.0012	-	-	-
6.4194	54500	0.0012	-	-	-
6.4311	54600	0.0011	-	-	-
6.4429	54700	0.0012	-	-	-
6.4547	54800	0.0012	-	-	-
6.4665	54900	0.0011	-	-	-
6.4783	55000	0.0011	0.0012	-0.17491975	0.6687
6.4900	55100	0.0012	-	-	-
6.5018	55200	0.0012	-	-	-
6.5136	55300	0.0011	-	-	-
6.5254	55400	0.0011	-	-	-
6.5371	55500	0.0011	-	-	-
6.5489	55600	0.0011	-	-	-
6.5607	55700	0.0011	-	-	-
6.5725	55800	0.0011	-	-	-
6.5843	55900	0.0012	-	-	-
6.5960	56000	0.0012	0.0012	-0.17456226	0.6726
6.6078	56100	0.0012	-	-	-
6.6196	56200	0.0011	-	-	-
6.6314	56300	0.0012	-	-	-
6.6432	56400	0.0011	-	-	-
6.6549	56500	0.0011	-	-	-
6.6667	56600	0.0011	-	-	-
6.6785	56700	0.0011	-	-	-
6.6903	56800	0.0011	-	-	-
6.7021	56900	0.0012	-	-	-
6.7138	57000	0.0011	0.0012	-0.17479357	0.6721
6.7256	57100	0.0011	-	-	-
6.7374	57200	0.0012	-	-	-
6.7492	57300	0.0011	-	-	-
6.7610	57400	0.0011	-	-	-
6.7727	57500	0.0011	-	-	-
6.7845	57600	0.0011	-	-	-
6.7963	57700	0.0011	-	-	-
6.8081	57800	0.0011	-	-	-
6.8199	57900	0.0011	-	-	-
6.8316	58000	0.0011	0.0012	-0.17439803	0.6710
6.8434	58100	0.0011	-	-	-
6.8552	58200	0.0011	-	-	-
6.8670	58300	0.0011	-	-	-
6.8788	58400	0.0011	-	-	-
6.8905	58500	0.0011	-	-	-
6.9023	58600	0.0011	-	-	-
6.9141	58700	0.0011	-	-	-
6.9259	58800	0.0011	-	-	-
6.9377	58900	0.0011	-	-	-
6.9494	59000	0.0011	0.0012	-0.17475043	0.6718
6.9612	59100	0.0011	-	-	-
6.9730	59200	0.0011	-	-	-
6.9848	59300	0.0011	-	-	-
6.9966	59400	0.0011	-	-	-
7.0082	59500	0.0011	-	-	-
7.0200	59600	0.0011	-	-	-
7.0318	59700	0.0011	-	-	-
7.0436	59800	0.0011	-	-	-
7.0554	59900	0.0011	-	-	-
7.0671	60000	0.0011	0.0012	-0.17430946	0.6722
7.0789	60100	0.0011	-	-	-
7.0907	60200	0.0011	-	-	-
7.1025	60300	0.0011	-	-	-
7.1143	60400	0.0011	-	-	-
7.1260	60500	0.0011	-	-	-
7.1378	60600	0.0011	-	-	-
7.1496	60700	0.0011	-	-	-
7.1614	60800	0.0011	-	-	-
7.1732	60900	0.0011	-	-	-
7.1849	61000	0.0011	0.0012	-0.17427135	0.6732
7.1967	61100	0.0011	-	-	-
7.2085	61200	0.0011	-	-	-
7.2203	61300	0.0011	-	-	-
7.2321	61400	0.0011	-	-	-
7.2438	61500	0.0011	-	-	-
7.2556	61600	0.0011	-	-	-
7.2674	61700	0.0011	-	-	-
7.2792	61800	0.0011	-	-	-
7.2910	61900	0.0011	-	-	-
7.3027	62000	0.0011	0.0012	-0.17400834	0.6719
7.3145	62100	0.0011	-	-	-
7.3263	62200	0.0011	-	-	-
7.3381	62300	0.0011	-	-	-
7.3499	62400	0.0011	-	-	-
7.3616	62500	0.0011	-	-	-
7.3734	62600	0.0011	-	-	-
7.3852	62700	0.0011	-	-	-
7.3970	62800	0.0011	-	-	-
7.4088	62900	0.0011	-	-	-
7.4205	63000	0.0011	0.0012	-0.1740251	0.6726
7.4323	63100	0.0011	-	-	-
7.4441	63200	0.0011	-	-	-
7.4559	63300	0.0011	-	-	-
7.4677	63400	0.0011	-	-	-
7.4794	63500	0.0011	-	-	-
7.4912	63600	0.0011	-	-	-
7.5030	63700	0.0011	-	-	-
7.5148	63800	0.0011	-	-	-
7.5265	63900	0.0011	-	-	-
7.5383	64000	0.0011	0.0012	-0.17380536	0.6712
7.5501	64100	0.0011	-	-	-
7.5619	64200	0.0011	-	-	-
7.5737	64300	0.0011	-	-	-
7.5854	64400	0.0011	-	-	-
7.5972	64500	0.0011	-	-	-
7.6090	64600	0.0011	-	-	-
7.6208	64700	0.0011	-	-	-
7.6326	64800	0.0011	-	-	-
7.6443	64900	0.0011	-	-	-
7.6561	65000	0.0011	0.0012	-0.17366579	0.6723
7.6679	65100	0.0011	-	-	-
7.6797	65200	0.0011	-	-	-
7.6915	65300	0.0011	-	-	-
7.7032	65400	0.0011	-	-	-
7.7150	65500	0.0011	-	-	-
7.7268	65600	0.0011	-	-	-
7.7386	65700	0.0011	-	-	-
7.7504	65800	0.0011	-	-	-
7.7621	65900	0.0011	-	-	-
7.7739	66000	0.0011	0.0012	-0.17369126	0.6746
7.7857	66100	0.0011	-	-	-
7.7975	66200	0.0011	-	-	-
7.8093	66300	0.0011	-	-	-
7.8210	66400	0.0011	-	-	-
7.8328	66500	0.0011	-	-	-
7.8446	66600	0.0011	-	-	-
7.8564	66700	0.0011	-	-	-
7.8682	66800	0.0011	-	-	-
7.8799	66900	0.0011	-	-	-
7.8917	67000	0.0011	0.0012	-0.17330332	0.6740
7.9035	67100	0.0011	-	-	-
7.9153	67200	0.0011	-	-	-
7.9271	67300	0.0011	-	-	-
7.9388	67400	0.0011	-	-	-
7.9506	67500	0.0011	-	-	-
7.9624	67600	0.0011	-	-	-
7.9742	67700	0.0011	-	-	-
7.9860	67800	0.0011	-	-	-
7.9977	67900	0.0011	-	-	-
8.0094	68000	0.0011	0.0012	-0.17326406	0.6732
8.0212	68100	0.0011	-	-	-
8.0330	68200	0.0011	-	-	-
8.0448	68300	0.0011	-	-	-
8.0565	68400	0.0011	-	-	-
8.0683	68500	0.0011	-	-	-
8.0801	68600	0.0011	-	-	-
8.0919	68700	0.0011	-	-	-
8.1037	68800	0.0011	-	-	-
8.1154	68900	0.0011	-	-	-
8.1272	69000	0.0011	0.0012	-0.17333928	0.6730
8.1390	69100	0.0011	-	-	-
8.1508	69200	0.0011	-	-	-
8.1626	69300	0.0011	-	-	-
8.1743	69400	0.0011	-	-	-
8.1861	69500	0.0011	-	-	-
8.1979	69600	0.0011	-	-	-
8.2097	69700	0.0011	-	-	-
8.2215	69800	0.0011	-	-	-
8.2332	69900	0.0011	-	-	-
8.2450	70000	0.0011	0.0012	-0.17320158	0.6726
8.2568	70100	0.0011	-	-	-
8.2686	70200	0.0011	-	-	-
8.2804	70300	0.0011	-	-	-
8.2921	70400	0.0011	-	-	-
8.3039	70500	0.0011	-	-	-
8.3157	70600	0.0011	-	-	-
8.3275	70700	0.0011	-	-	-
8.3393	70800	0.0011	-	-	-
8.3510	70900	0.0011	-	-	-
8.3628	71000	0.0011	0.0012	-0.17346236	0.6726
8.3746	71100	0.0011	-	-	-
8.3864	71200	0.0011	-	-	-
8.3982	71300	0.0011	-	-	-
8.4099	71400	0.0011	-	-	-
8.4217	71500	0.0011	-	-	-
8.4335	71600	0.0011	-	-	-
8.4453	71700	0.0011	-	-	-
8.4570	71800	0.0011	-	-	-
8.4688	71900	0.0011	-	-	-
8.4806	72000	0.0011	0.0012	-0.17322065	0.6749
8.4924	72100	0.0011	-	-	-
8.5042	72200	0.0011	-	-	-
8.5159	72300	0.0011	-	-	-
8.5277	72400	0.0011	-	-	-
8.5395	72500	0.0011	-	-	-
8.5513	72600	0.0011	-	-	-
8.5631	72700	0.0011	-	-	-
8.5748	72800	0.0011	-	-	-
8.5866	72900	0.0011	-	-	-
8.5984	73000	0.0011	0.0011	-0.1729941	0.6725
8.6102	73100	0.0011	-	-	-
8.6220	73200	0.0011	-	-	-
8.6337	73300	0.0011	-	-	-
8.6455	73400	0.0011	-	-	-
8.6573	73500	0.0011	-	-	-
8.6691	73600	0.0011	-	-	-
8.6809	73700	0.0011	-	-	-
8.6926	73800	0.0011	-	-	-
8.7044	73900	0.0011	-	-	-
8.7162	74000	0.0011	0.0011	-0.17297848	0.6719
8.7280	74100	0.0011	-	-	-
8.7398	74200	0.0011	-	-	-
8.7515	74300	0.0011	-	-	-
8.7633	74400	0.0011	-	-	-
8.7751	74500	0.0011	-	-	-
8.7869	74600	0.0011	-	-	-
8.7987	74700	0.0011	-	-	-
8.8104	74800	0.0011	-	-	-
8.8222	74900	0.0011	-	-	-
8.8340	75000	0.0011	0.0011	-0.17291391	0.6728
8.8458	75100	0.0011	-	-	-
8.8576	75200	0.0011	-	-	-
8.8693	75300	0.0011	-	-	-
8.8811	75400	0.0011	-	-	-
8.8929	75500	0.0011	-	-	-
8.9047	75600	0.0011	-	-	-
8.9165	75700	0.0011	-	-	-
8.9282	75800	0.0011	-	-	-
8.9400	75900	0.0011	-	-	-
8.9518	76000	0.0011	0.0011	-0.1728144	0.6741
8.9636	76100	0.0011	-	-	-
8.9754	76200	0.0011	-	-	-
8.9871	76300	0.0011	-	-	-
8.9989	76400	0.0011	-	-	-
9.0106	76500	0.0011	-	-	-
9.0224	76600	0.0011	-	-	-
9.0342	76700	0.0011	-	-	-
9.0459	76800	0.0011	-	-	-
9.0577	76900	0.0011	-	-	-
9.0695	77000	0.0011	0.0011	-0.1725926	0.6728
9.0813	77100	0.0011	-	-	-
9.0931	77200	0.0011	-	-	-
9.1048	77300	0.0011	-	-	-
9.1166	77400	0.0011	-	-	-
9.1284	77500	0.0011	-	-	-
9.1402	77600	0.0011	-	-	-
9.1520	77700	0.0011	-	-	-
9.1637	77800	0.0011	-	-	-
9.1755	77900	0.0011	-	-	-
9.1873	78000	0.0011	0.0011	-0.17272119	0.6735
9.1991	78100	0.0011	-	-	-
9.2109	78200	0.0011	-	-	-
9.2226	78300	0.0011	-	-	-
9.2344	78400	0.0011	-	-	-
9.2462	78500	0.0011	-	-	-
9.2580	78600	0.0011	-	-	-
9.2698	78700	0.0011	-	-	-
9.2815	78800	0.0011	-	-	-
9.2933	78900	0.0011	-	-	-
9.3051	79000	0.0011	0.0011	-0.1726715	0.6730
9.3169	79100	0.0011	-	-	-
9.3287	79200	0.0011	-	-	-
9.3404	79300	0.0011	-	-	-
9.3522	79400	0.0011	-	-	-
9.3640	79500	0.0011	-	-	-
9.3758	79600	0.0011	-	-	-
9.3875	79700	0.0011	-	-	-
9.3993	79800	0.0011	-	-	-
9.4111	79900	0.0011	-	-	-
9.4229	80000	0.0011	0.0011	-0.172526	0.6737
9.4347	80100	0.0011	-	-	-
9.4464	80200	0.0011	-	-	-
9.4582	80300	0.0011	-	-	-
9.4700	80400	0.0011	-	-	-
9.4818	80500	0.0011	-	-	-
9.4936	80600	0.0011	-	-	-
9.5053	80700	0.0011	-	-	-
9.5171	80800	0.0011	-	-	-
9.5289	80900	0.0011	-	-	-
9.5407	81000	0.0011	0.0011	-0.1724438	0.6749
9.5525	81100	0.0011	-	-	-
9.5642	81200	0.0011	-	-	-
9.5760	81300	0.0011	-	-	-
9.5878	81400	0.0011	-	-	-
9.5996	81500	0.0011	-	-	-
9.6114	81600	0.0011	-	-	-
9.6231	81700	0.0011	-	-	-
9.6349	81800	0.0011	-	-	-
9.6467	81900	0.0011	-	-	-
9.6585	82000	0.0011	0.0011	-0.17250738	0.6729
9.6703	82100	0.0011	-	-	-
9.6820	82200	0.0011	-	-	-
9.6938	82300	0.0011	-	-	-
9.7056	82400	0.0011	-	-	-
9.7174	82500	0.0011	-	-	-
9.7292	82600	0.0011	-	-	-
9.7409	82700	0.0011	-	-	-
9.7527	82800	0.0011	-	-	-
9.7645	82900	0.0011	-	-	-
9.7763	83000	0.0011	0.0011	-0.1723447	0.6728
9.7881	83100	0.0011	-	-	-
9.7998	83200	0.0011	-	-	-
9.8116	83300	0.0011	-	-	-
9.8234	83400	0.0011	-	-	-
9.8352	83500	0.0011	-	-	-
9.8470	83600	0.0011	-	-	-
9.8587	83700	0.0011	-	-	-
9.8705	83800	0.0011	-	-	-
9.8823	83900	0.0011	-	-	-
9.8941	84000	0.0011	0.0011	-0.17230988	0.6750
9.9059	84100	0.0011	-	-	-
9.9176	84200	0.0011	-	-	-
9.9294	84300	0.0011	-	-	-
9.9412	84400	0.0011	-	-	-
9.9530	84500	0.0011	-	-	-
9.9647	84600	0.0011	-	-	-
9.9765	84700	0.0011	-	-	-
9.9883	84800	0.0011	-	-	-

Framework Versions

Python: 3.10.16
Sentence Transformers: 3.3.1
Transformers: 4.48.0
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

jvanhoof
/

all-miniLM-L6-en-ja

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Knowledge Distillation

Translation

Training Details

Training Dataset

en-ja

Evaluation Dataset

en-ja

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MSELoss

Model tree for jvanhoof/all-miniLM-L6-en-ja

Evaluation results