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arthurbresnu/
splade-co-condenser-marco-msmarco-margin-mse-1-bs_128-lr_2e-05-lq_0.1-ld_0.08

Name: splade-co-condenser-marco-msmarco-margin-mse-1-bs_128-lr_2e-05-lq_0.1-ld_0.08
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splade-co-condenser-marco trained on MS MARCO hard negatives with distillation

This is a SPLADE Sparse Encoder model finetuned from Luyu/co-condenser-marco on the msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: Luyu/co-condenser-marco
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

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 SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("arthurbresnu/splade-co-condenser-marco-msmarco-margin-mse-1-bs_128-lr_2e-05-lq_0.1-ld_0.08")
# Run inference
queries = [
    "skype how do i get video  voice",
]
documents = [
    'Speaker volume to adjust the sound. Slide the pointer up and down for volume, or select the speaker icon at the top of the volume control to mute your speaker. Select the Video button to add video to a Skype for Business call. Select the IM button to add instant messaging to a Skype for Business call.',
    'To make a free voice or video call on Skype for Web, you need to download a plugin. You can do this when you first sign in, or wait for when you want to make or receive your first call. Installing the plugin should only take a few moments, as the plugin is just 13.6MB. Making a voice or video call is simple.',
    'Century Dictionary and Cyclopedia. 1  n vogue The mode or fashion prevalent at any particular time; popular reception, repute, or estimation; common currency: now generally used in the phrase in vogue: as, a particular style of dress was then in. 2  n vogue General drift of ideas; rumor; report. ***.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[14.1007, 17.3336,  0.0000]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
dot_accuracy@1	0.52	0.48	0.6	0.32	0.76	0.84	0.44	0.88	0.82	0.4	0.12	0.58	0.6939
dot_accuracy@3	0.74	0.6	0.7	0.4	0.88	0.96	0.56	0.92	0.88	0.64	0.44	0.7	0.9796
dot_accuracy@5	0.78	0.62	0.76	0.5	0.9	1.0	0.6	0.96	0.96	0.72	0.72	0.76	0.9796
dot_accuracy@10	0.84	0.66	0.88	0.64	0.92	1.0	0.7	1.0	1.0	0.84	0.82	0.82	1.0
dot_precision@1	0.52	0.48	0.6	0.32	0.76	0.84	0.44	0.88	0.82	0.4	0.12	0.58	0.6939
dot_precision@3	0.2467	0.4067	0.2333	0.1467	0.6467	0.3467	0.2533	0.48	0.3533	0.3267	0.1467	0.2467	0.6803
dot_precision@5	0.156	0.352	0.156	0.116	0.612	0.216	0.188	0.304	0.24	0.236	0.144	0.172	0.649
dot_precision@10	0.084	0.272	0.092	0.08	0.54	0.108	0.118	0.166	0.13	0.17	0.082	0.092	0.5327
dot_recall@1	0.52	0.0641	0.58	0.1567	0.0952	0.7967	0.2367	0.44	0.7207	0.0837	0.12	0.555	0.0482
dot_recall@3	0.74	0.0987	0.66	0.198	0.1886	0.9333	0.3458	0.72	0.8387	0.2017	0.44	0.67	0.1352
dot_recall@5	0.78	0.1169	0.73	0.258	0.2541	0.9733	0.4129	0.76	0.92	0.2427	0.72	0.75	0.2113
dot_recall@10	0.84	0.1408	0.84	0.3197	0.371	0.9733	0.5074	0.83	0.98	0.3467	0.82	0.81	0.3371
dot_ndcg@10	0.6831	0.3577	0.7016	0.2825	0.6615	0.9134	0.4345	0.7989	0.8858	0.339	0.4614	0.6885	0.5945
dot_mrr@10	0.6327	0.5392	0.6728	0.3947	0.8275	0.9057	0.5141	0.9144	0.8713	0.5398	0.3466	0.6556	0.8328
dot_map@100	0.6416	0.1733	0.6572	0.2299	0.5179	0.8881	0.3738	0.7399	0.8503	0.2567	0.3539	0.6523	0.4093
query_active_dims	8.02	7.8	11.68	78.88	9.4	35.18	14.32	22.88	14.18	21.5	192.48	71.74	10.7143
query_sparsity_ratio	0.9997	0.9997	0.9996	0.9974	0.9997	0.9988	0.9995	0.9993	0.9995	0.9993	0.9937	0.9976	0.9996
corpus_active_dims	105.1868	199.2269	128.6282	155.6984	105.1973	146.2776	143.9738	103.4279	15.7222	186.5579	193.5227	211.6221	157.7224
corpus_sparsity_ratio	0.9966	0.9935	0.9958	0.9949	0.9966	0.9952	0.9953	0.9966	0.9995	0.9939	0.9937	0.9931	0.9948

Sparse Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with SparseNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nfcorpus", 5 "nq" 6 ] 7}

Metric	Value
dot_accuracy@1	0.4867
dot_accuracy@3	0.66
dot_accuracy@5	0.7467
dot_accuracy@10	0.78
dot_precision@1	0.4867
dot_precision@3	0.2933
dot_precision@5	0.2333
dot_precision@10	0.15
dot_recall@1	0.3545
dot_recall@3	0.4931
dot_recall@5	0.5569
dot_recall@10	0.5941
dot_ndcg@10	0.565
dot_mrr@10	0.5891
dot_map@100	0.4718
query_active_dims	7.9133
query_sparsity_ratio	0.9997
corpus_active_dims	77.9468
corpus_sparsity_ratio	0.9974

Sparse Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with SparseNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "climatefever", 4 "dbpedia", 5 "fever", 6 "fiqa2018", 7 "hotpotqa", 8 "msmarco", 9 "nfcorpus", 10 "nq", 11 "quoraretrieval", 12 "scidocs", 13 "arguana", 14 "scifact", 15 "touche2020" 16 ] 17}

Metric	Value
dot_accuracy@1	0.5734
dot_accuracy@3	0.723
dot_accuracy@5	0.7892
dot_accuracy@10	0.8554
dot_precision@1	0.5734
dot_precision@3	0.3472
dot_precision@5	0.2724
dot_precision@10	0.1897
dot_recall@1	0.3398
dot_recall@3	0.4746
dot_recall@5	0.5484
dot_recall@10	0.6243
dot_ndcg@10	0.6002
dot_mrr@10	0.6652
dot_map@100	0.5188
query_active_dims	38.4099
query_sparsity_ratio	0.9987
corpus_active_dims	133.1462
corpus_sparsity_ratio	0.9956

Training Details

Training Dataset

msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores

Dataset: msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores
Size: 522,751 training samples
Columns: query, positive, negative, and label

Approximate statistics based on the first 1000 samples:

	query	positive	negative	label
type	string	string	string	float
details	min: 4 tokens mean: 9.16 tokens max: 48 tokens	min: 17 tokens mean: 80.22 tokens max: 256 tokens	min: 20 tokens mean: 77.64 tokens max: 234 tokens	min: -17.67 mean: -1.3 max: 7.3

Samples:

query	positive	negative	label
`can pasta be cooked and put in fridge`	`Cooked, refrigerated pasta is easily reheated by dropping it in boiling water for several seconds. Cooked pasta can also be frozen for up to 2 weeks. The pasta should be slightly cooled first and tossed with a bit of cooking or olive oil and placed into airtight freezer bags or containers.`	`CLOSE. 1 Cooked pasta can be stored in airtight containers in the refrigerator for 3 to 5 days. 2 Or, freeze cooked pasta for up to 2 weeks: Cool the pasta slightly, then drizzle with a little olive oil or cooking oil and toss gently. 3 Defrost a bag of frozen pasta in a colander in the sink by running tepid water over it.`	`-0.22228431701660156`
`what is a oscillator circuit`	`An electronic oscillator is an electronic circuit that produces a periodic, oscillating electronic signal, often a sine wave or a square wave. Oscillators convert direct current (DC) from a power supply to an alternating current signal.`	`An electronic oscillator is an electronic circuit that produces a periodic, oscillating electronic signal, often a sine wave or a square wave. Oscillators convert direct current (DC) from a power supply to an alternating current (AC) signal. They are widely used in many electronic devices.`	`-0.22161579132080078`
`what company makes the mclaren`	`Mclaren are a British company, the cars are manufactured in Surrey, England.`	`McLaren Automotive. McLaren Automotive (often simply McLaren) is a British automaker founded in 1963 by New Zealander Bruce McLaren and is based at the McLaren Technology Campus in Woking, Surrey. It produces and manufactures sports and luxury cars, usually produced in-house at designated production facilities.`	`-0.6905484199523926`

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMarginMSELoss", 3 "document_regularizer_weight": 0.08, 4 "query_regularizer_weight": 0.1 5}

Evaluation Dataset

msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores

Dataset: msmarco-hard-negatives-cross-encoder-ms-marco-mini_lm-l-6-v2-scores
Size: 10,000 evaluation samples
Columns: query, positive, negative, and label

Approximate statistics based on the first 1000 samples:

	query	positive	negative	label
type	string	string	string	float
details	min: 4 tokens mean: 8.99 tokens max: 28 tokens	min: 23 tokens mean: 80.74 tokens max: 229 tokens	min: 21 tokens mean: 78.11 tokens max: 214 tokens	min: -17.67 mean: -1.46 max: 5.21

Samples:

query	positive	negative	label
`what is the legal meaning of a life estate`	`DEFINITION of 'Life Estate'. A type of estate that only lasts for the lifetime of the beneficiary. A life estate is a very restrictive type of estate that prevents the beneficiary from selling the property that produces the income before the beneficiary's death. But the estate cannot continue beyond the life of the beneficiary.`	`In common law and statutory law, a life estate is the ownership of land for the duration of a person's life. In legal terms it is an estate in real property that ends at death when ownership of the property may revert to the original owner, or it may pass to another person. The owner of a life estate is called a life tenant.`	`-1.0078916549682617`
`what is the latest nvidia graphics card`	`GeForce GTX TITAN X is the ultimate graphics card. It combines the latest technologies and performance of the new NVIDIA Maxwellâ¢ architecture to be the fastest, most advanced graphics card on the planet.`	`NVIDIA Titan X: The Ultimate Graphics Card Unleashed â Features Pascal GP102 GPU, 12 GB G5X Memory and a $1200 US Price. Today, NVIDIA launches their $1200 US, Titan X graphics card aiming the enthusiast and professional market. Do not mistake this card with last yearâs GeForce GTX Titan X which featured the Maxwell GPU as the latest Titan X adopts the Pascal GPU architecture.`	`-0.10915374755859375`
`what elements are used in a jewelry store`	the most common metallic elements used in jewelry are gold, silver, platinum and copper. technically speaking, a diamond is an elemental form of carbon, if you want to includ â¦ e that as well.most other jewelry components are compounds or mixtures of different elements. 1 person found this useful. Kimberly O'Brien. There are so many ways to sell your jewelry, but the problem is you prâ¦. 2 Shopping on Jeweler's Row Offers Maximum Selection and Minimum Prices Jeweler's Row is a stretch of several downtown Chicago blocks with an abundance of fine jewelry stores. 3 If there is a heaven for jewelry shoppers, this is it. 4 In one buildingâ¦.	`Silver and gold are both malleable and lustrous, excellent properties for using these elements in jewelry. Metalloids share some characteristics of both metals and nonmetals. For example, silicon has luster and looks like a metal but does not conduct heat or electricity like a metal.`	`1.2301173210144043`

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMarginMSELoss", 3 "document_regularizer_weight": 0.08, 4 "query_regularizer_weight": 0.1 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 35
warmup_ratio: 0.05
bf16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 35
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
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: 2
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
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: True
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
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	Validation Loss	NanoMSMARCO_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoNQ_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10	NanoClimateFEVER_dot_ndcg@10	NanoDBPedia_dot_ndcg@10	NanoFEVER_dot_ndcg@10	NanoFiQA2018_dot_ndcg@10	NanoHotpotQA_dot_ndcg@10	NanoQuoraRetrieval_dot_ndcg@10	NanoSCIDOCS_dot_ndcg@10	NanoArguAna_dot_ndcg@10	NanoSciFact_dot_ndcg@10	NanoTouche2020_dot_ndcg@10
0.0980	400	147401.16	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1959	800	26.7579	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2939	1200	7.3508	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3919	1600	6.0019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4898	2000	5.0412	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5878	2400	4.2727	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6858	2800	3.6961	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7837	3200	3.4853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8817	3600	3.2762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9797	4000	3.1646	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0	4083	-	2.7194	0.6049	0.3374	0.6824	0.5416	-	-	-	-	-	-	-	-	-	-
1.0776	4400	2.8406	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1756	4800	2.7254	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2736	5200	2.6307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3715	5600	2.5854	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4695	6000	2.6132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5675	6400	2.5701	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6654	6800	2.4706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7634	7200	2.4933	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8614	7600	2.47	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9593	8000	2.3888	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0	8166	-	2.3257	0.6389	0.3311	0.6923	0.5541	-	-	-	-	-	-	-	-	-	-
2.0573	8400	2.0776	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1553	8800	1.8881	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2532	9200	1.8701	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3512	9600	1.8678	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4492	10000	1.8307	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5471	10400	1.8535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6451	10800	1.8208	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7431	11200	1.8596	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8410	11600	1.8242	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9390	12000	1.8065	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0	12249	-	2.0072	0.6582	0.3463	0.6827	0.5624	-	-	-	-	-	-	-	-	-	-
3.0370	12400	1.6428	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.1349	12800	1.3588	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.2329	13200	1.3502	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.3309	13600	1.4121	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.4289	14000	1.4051	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.5268	14400	1.3704	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.6248	14800	1.3929	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.7228	15200	1.4014	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.8207	15600	1.41	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.9187	16000	1.4211	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.0	16332	-	1.9579	0.6758	0.3445	0.6929	0.5711	-	-	-	-	-	-	-	-	-	-
4.0167	16400	1.3491	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.1146	16800	1.0892	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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4.3106	17600	1.1157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.4085	18000	1.1267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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4.6045	18800	1.1348	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
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4.8004	19600	1.1204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.8984	20000	1.1791	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
4.9963	20400	1.1559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.0	20415	-	1.9729	0.6652	0.3585	0.6790	0.5676	-	-	-	-	-	-	-	-	-	-
5.0943	20800	0.909	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.1923	21200	0.9201	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.2902	21600	0.9246	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.3882	22000	0.9491	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.4862	22400	0.9559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.5841	22800	0.9526	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.6821	23200	0.9688	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.7801	23600	0.9702	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.8780	24000	0.967	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
5.9760	24400	0.9934	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.0	24498	-	1.9280	0.6620	0.3520	0.6743	0.5628	-	-	-	-	-	-	-	-	-	-
6.0740	24800	0.837	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.1719	25200	0.8065	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.2699	25600	0.8057	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.3679	26000	0.8283	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.4658	26400	0.8235	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.5638	26800	0.857	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.6618	27200	0.8607	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.7597	27600	0.8537	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.8577	28000	0.8634	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
6.9557	28400	0.8693	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.0	28581	-	1.9013	0.6494	0.3543	0.6988	0.5675	-	-	-	-	-	-	-	-	-	-
7.0536	28800	0.7885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.1516	29200	0.7167	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.2496	29600	0.7331	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.3475	30000	0.7376	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.4455	30400	0.7632	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.5435	30800	0.7466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.6414	31200	0.7709	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.7394	31600	0.777	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.8374	32000	0.7816	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
7.9353	32400	0.7925	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.0	32664	-	1.9439	0.6650	0.3594	0.6768	0.5670	-	-	-	-	-	-	-	-	-	-
8.0333	32800	0.7509	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.1313	33200	0.6741	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.2292	33600	0.6762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.3272	34000	0.6896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.4252	34400	0.688	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.5231	34800	0.7047	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.6211	35200	0.706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.7191	35600	0.7033	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.8170	36000	0.729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
8.9150	36400	0.7322	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.0	36747	-	1.9782	0.6333	0.3635	0.6881	0.5617	-	-	-	-	-	-	-	-	-	-
9.0130	36800	0.7245	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.1109	37200	0.6358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.2089	37600	0.6512	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.3069	38000	0.6561	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.4048	38400	0.6514	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.5028	38800	0.6675	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.6008	39200	0.6743	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.6988	39600	0.6693	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.7967	40000	0.6795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.8947	40400	0.6856	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
9.9927	40800	0.6938	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.0	40830	-	2.029	0.6831	0.3577	0.7016	0.5808	-	-	-	-	-	-	-	-	-	-
10.0906	41200	0.6134	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.1886	41600	0.6158	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.2866	42000	0.6222	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.3845	42400	0.6288	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.4825	42800	0.6341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.5805	43200	0.643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.6784	43600	0.6443	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.7764	44000	0.6563	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.8744	44400	0.666	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
10.9723	44800	0.6624	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.0	44913	-	2.0551	0.6483	0.3588	0.6733	0.5601	-	-	-	-	-	-	-	-	-	-
11.0703	45200	0.6143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.1683	45600	0.5974	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.2662	46000	0.6035	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.3642	46400	0.6159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.4622	46800	0.6141	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.5601	47200	0.6237	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.6581	47600	0.6235	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.7561	48000	0.6335	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.8540	48400	0.6361	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
11.9520	48800	0.6384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.0	48996	-	2.0546	0.6704	0.3582	0.6972	0.5753	-	-	-	-	-	-	-	-	-	-
12.0500	49200	0.5967	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.1479	49600	0.5669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.2459	50000	0.5741	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.3439	50400	0.5756	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.4418	50800	0.5823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.5398	51200	0.5855	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.6378	51600	0.5823	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.7357	52000	0.5872	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.8337	52400	0.5864	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
12.9317	52800	0.5885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.0	53079	-	2.0731	0.6569	0.3629	0.6821	0.5673	-	-	-	-	-	-	-	-	-	-
13.0296	53200	0.5772	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.1276	53600	0.5367	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.2256	54000	0.5354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.3235	54400	0.5426	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.4215	54800	0.5413	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.5195	55200	0.5372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.6174	55600	0.5479	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.7154	56000	0.5527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.8134	56400	0.5499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
13.9113	56800	0.5456	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.0	57162	-	2.0736	0.6528	0.3625	0.7021	0.5725	-	-	-	-	-	-	-	-	-	-
14.0093	57200	0.5438	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.1073	57600	0.5051	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.2052	58000	0.5028	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.3032	58400	0.5059	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.4012	58800	0.5055	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.4991	59200	0.5101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.5971	59600	0.5157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.6951	60000	0.5085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.7930	60400	0.518	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.8910	60800	0.5101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
14.9890	61200	0.5141	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.0	61245	-	2.0690	0.6528	0.3545	0.7131	0.5735	-	-	-	-	-	-	-	-	-	-
15.0869	61600	0.4812	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.1849	62000	0.4729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.2829	62400	0.4787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.3808	62800	0.4809	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.4788	63200	0.4762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.5768	63600	0.4835	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.6747	64000	0.4877	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.7727	64400	0.4818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.8707	64800	0.4893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
15.9687	65200	0.4853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.0	65328	-	2.0776	0.6552	0.3683	0.6883	0.5706	-	-	-	-	-	-	-	-	-	-
16.0666	65600	0.4638	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.1646	66000	0.4465	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.2626	66400	0.4546	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.3605	66800	0.4572	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.4585	67200	0.4569	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.5565	67600	0.4608	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.6544	68000	0.4584	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.7524	68400	0.4587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.8504	68800	0.4612	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
16.9483	69200	0.4625	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.0	69411	-	2.0769	0.6490	0.3571	0.7061	0.5707	-	-	-	-	-	-	-	-	-	-
17.0463	69600	0.4519	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.1443	70000	0.4297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.2422	70400	0.4355	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.3402	70800	0.4387	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.4382	71200	0.437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.5361	71600	0.439	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.6341	72000	0.433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.7321	72400	0.4411	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.8300	72800	0.435	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
17.9280	73200	0.4452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.0	73494	-	2.0449	0.6553	0.3542	0.7062	0.5719	-	-	-	-	-	-	-	-	-	-
18.0260	73600	0.4342	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.1239	74000	0.4147	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.2219	74400	0.4137	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.3199	74800	0.4158	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.4178	75200	0.4161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.5158	75600	0.4152	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.6138	76000	0.4198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.7117	76400	0.4204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.8097	76800	0.4179	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
18.9077	77200	0.4182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.0	77577	-	2.0543	0.6484	0.3547	0.6994	0.5675	-	-	-	-	-	-	-	-	-	-
19.0056	77600	0.4168	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.1036	78000	0.3969	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.2016	78400	0.4014	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.2995	78800	0.3979	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.3975	79200	0.3989	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.4955	79600	0.401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.5934	80000	0.3991	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.6914	80400	0.4	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.7894	80800	0.4021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.8873	81200	0.4024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
19.9853	81600	0.4052	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.0	81660	-	2.0791	0.6495	0.3545	0.6999	0.5680	-	-	-	-	-	-	-	-	-	-
20.0833	82000	0.3812	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.1812	82400	0.3809	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.2792	82800	0.3831	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.3772	83200	0.3845	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.4751	83600	0.3828	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.5731	84000	0.3829	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.6711	84400	0.3839	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.7690	84800	0.3869	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.8670	85200	0.3883	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
20.9650	85600	0.386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.0	85743	-	2.0739	0.6378	0.3526	0.6996	0.5633	-	-	-	-	-	-	-	-	-	-
21.0629	86000	0.3706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.1609	86400	0.3686	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.2589	86800	0.3654	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.3568	87200	0.372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.4548	87600	0.3696	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.5528	88000	0.3692	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.6507	88400	0.37	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.7487	88800	0.3719	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.8467	89200	0.3754	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
21.9446	89600	0.3746	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.0	89826	-	2.0893	0.6433	0.3524	0.7000	0.5653	-	-	-	-	-	-	-	-	-	-
22.0426	90000	0.3642	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.1406	90400	0.3536	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.2386	90800	0.3547	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.3365	91200	0.3539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.4345	91600	0.3538	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.5325	92000	0.3596	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.6304	92400	0.3572	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.7284	92800	0.3604	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.8264	93200	0.3575	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
22.9243	93600	0.3591	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.0	93909	-	2.0807	0.6458	0.3558	0.6872	0.5629	-	-	-	-	-	-	-	-	-	-
23.0223	94000	0.3515	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.1203	94400	0.3432	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.2182	94800	0.3448	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.3162	95200	0.3453	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.4142	95600	0.3468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.5121	96000	0.3443	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.6101	96400	0.3457	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.7081	96800	0.3459	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.8060	97200	0.346	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
23.9040	97600	0.3462	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.0	97992	-	2.0896	0.6445	0.3547	0.6967	0.5653	-	-	-	-	-	-	-	-	-	-
24.0020	98000	0.3452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.0999	98400	0.3366	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.1979	98800	0.3341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.2959	99200	0.3317	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.3938	99600	0.3345	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.4918	100000	0.3347	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.5898	100400	0.3371	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.6877	100800	0.3364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.7857	101200	0.3354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.8837	101600	0.336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
24.9816	102000	0.3346	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.0	102075	-	2.1040	0.6366	0.3614	0.6919	0.5633	-	-	-	-	-	-	-	-	-	-
25.0796	102400	0.3247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.1776	102800	0.3268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.2755	103200	0.3218	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.3735	103600	0.3264	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.4715	104000	0.3256	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.5694	104400	0.3249	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.6674	104800	0.3263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.7654	105200	0.3273	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.8633	105600	0.3263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
25.9613	106000	0.3299	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.0	106158	-	2.1057	0.6505	0.3534	0.6897	0.5645	-	-	-	-	-	-	-	-	-	-
26.0593	106400	0.3175	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.1572	106800	0.316	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.2552	107200	0.3155	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.3532	107600	0.3153	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.4511	108000	0.3165	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.5491	108400	0.3163	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.6471	108800	0.3164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.7450	109200	0.3216	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.8430	109600	0.3186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
26.9410	110000	0.3173	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.0	110241	-	2.1127	0.6377	0.3499	0.6968	0.5615	-	-	-	-	-	-	-	-	-	-
27.0389	110400	0.315	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.1369	110800	0.3052	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.2349	111200	0.3063	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.3328	111600	0.3052	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.4308	112000	0.3091	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.5288	112400	0.3096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.6267	112800	0.3084	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.7247	113200	0.3097	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.8227	113600	0.3108	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
27.9206	114000	0.3098	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.0	114324	-	2.1209	0.6504	0.3525	0.6904	0.5644	-	-	-	-	-	-	-	-	-	-
28.0186	114400	0.3059	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.1166	114800	0.3007	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.2145	115200	0.302	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.3125	115600	0.3025	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.4105	116000	0.2988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.5084	116400	0.3019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.6064	116800	0.3027	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.7044	117200	0.3002	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.8024	117600	0.3027	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.9003	118000	0.302	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
28.9983	118400	0.3025	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.0	118407	-	2.1126	0.6565	0.3536	0.6922	0.5674	-	-	-	-	-	-	-	-	-	-
29.0963	118800	0.294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.1942	119200	0.2934	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.2922	119600	0.2971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.3902	120000	0.2962	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.4881	120400	0.2936	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.5861	120800	0.2935	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.6841	121200	0.2943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.7820	121600	0.2919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.8800	122000	0.2942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
29.9780	122400	0.2957	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.0	122490	-	2.1210	0.6491	0.3531	0.6910	0.5644	-	-	-	-	-	-	-	-	-	-
30.0759	122800	0.2922	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.1739	123200	0.2889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.2719	123600	0.2891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.3698	124000	0.2907	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.4678	124400	0.2892	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.5658	124800	0.289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.6637	125200	0.2884	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.7617	125600	0.2888	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.8597	126000	0.2893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
30.9576	126400	0.2883	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.0	126573	-	2.1149	0.6447	0.3526	0.6919	0.5630	-	-	-	-	-	-	-	-	-	-
31.0556	126800	0.2865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.1536	127200	0.284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.2515	127600	0.2839	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.3495	128000	0.284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.4475	128400	0.2839	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.5454	128800	0.2831	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.6434	129200	0.284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.7414	129600	0.2836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.8393	130000	0.2844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
31.9373	130400	0.2862	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.0	130656	-	2.1219	0.6496	0.3516	0.6945	0.5652	-	-	-	-	-	-	-	-	-	-
32.0353	130800	0.282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.1332	131200	0.2795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.2312	131600	0.2792	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.3292	132000	0.2813	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.4271	132400	0.2798	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.5251	132800	0.2801	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.6231	133200	0.2773	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.7210	133600	0.28	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.8190	134000	0.2789	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
32.9170	134400	0.2812	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.0	134739	-	2.1282	0.6433	0.3519	0.6907	0.5620	-	-	-	-	-	-	-	-	-	-
33.0149	134800	0.278	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.1129	135200	0.2763	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.2109	135600	0.2771	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.3088	136000	0.2755	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.4068	136400	0.2763	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.5048	136800	0.2748	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.6027	137200	0.2761	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.7007	137600	0.276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.7987	138000	0.2779	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.8966	138400	0.2744	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
33.9946	138800	0.2766	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.0	138822	-	2.1325	0.6510	0.3507	0.6907	0.5641	-	-	-	-	-	-	-	-	-	-
34.0926	139200	0.275	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.1905	139600	0.2724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.2885	140000	0.2726	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.3865	140400	0.2735	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.4844	140800	0.2717	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.5824	141200	0.2736	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.6804	141600	0.272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.7783	142000	0.2713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.8763	142400	0.2738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
34.9743	142800	0.2715	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
35.0	142905	-	2.1323	0.6510	0.3530	0.6910	0.5650	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.6831	0.3577	0.7016	0.6002	0.2825	0.6615	0.9134	0.4345	0.7989	0.8858	0.3390	0.4614	0.6885	0.5945

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.13.3
Sentence Transformers: 5.1.0.dev0
Transformers: 4.53.2
PyTorch: 2.7.1+cu126
Accelerate: 1.9.0
Datasets: 3.6.0
Tokenizers: 0.21.2

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",
}

SpladeLoss

bibtex

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMarginMSELoss

bibtex

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}

FlopsLoss

bibtex

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}