tomaarsen/ms-marco-ettin-32m-reranker

CrossEncoder based on jhu-clsp/ettin-encoder-32m

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-32m on the msmarco dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: jhu-clsp/ettin-encoder-32m
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label
• Training Dataset:
  • msmarco
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/ms-marco-ettin-32m-reranker")
# Get scores for pairs of texts
pairs = [
    ['what is honor society', 'In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America.'],
    ['what happens to blood pressure when you raise your arm', 'Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago.'],
    ['what country is the name astrid from', 'Comment by silkfire. Astrid, Estrid, Ã\x86striðr æstriðr Ã\x81strÃ\xadðr astriðr ástrÃ\xadðr is a given Name Of north. Germanic origin it Comes From Ã\x81sfrÃ\xadðr (Norse Asfriðr), ásfrÃ\xadðr divine (beauty) + from (ass). áss god friðr FrÃ\xadðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ã\x81sfrÃ\xadðr (ásfrÃ\xadðr Divine), beauty from (ass) + áss (god). Friðr frÃ\xadðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia'],
    ['where the latin people came from', 'Latina is a city in Italy and the Latin people come from southern Italy around Rome.'],
    ['how long do cocker spaniels', "the cocker spaniel's life span is 12-15 years. a dog that is kept fit and on nutritious dog food has a better chance of living longer than those who aren't. I would assume roughly 10-15 years. My cocker spaniel is 13.5 years old and just starting to lose her health."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is honor society',
    [
        'In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America.',
        'Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago.',
        'Comment by silkfire. Astrid, Estrid, Ã\x86striðr æstriðr Ã\x81strÃ\xadðr astriðr ástrÃ\xadðr is a given Name Of north. Germanic origin it Comes From Ã\x81sfrÃ\xadðr (Norse Asfriðr), ásfrÃ\xadðr divine (beauty) + from (ass). áss god friðr FrÃ\xadðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ã\x81sfrÃ\xadðr (ásfrÃ\xadðr Divine), beauty from (ass) + áss (god). Friðr frÃ\xadðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia',
        'Latina is a city in Italy and the Latin people come from southern Italy around Rome.',
        "the cocker spaniel's life span is 12-15 years. a dog that is kept fit and on nutritious dog food has a better chance of living longer than those who aren't. I would assume roughly 10-15 years. My cocker spaniel is 13.5 years old and just starting to lose her health.",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

• Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }
  

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.6380 (+0.1484)	0.3547 (+0.0937)	0.6748 (+0.2552)
mrr@10	0.6319 (+0.1544)	0.6102 (+0.1104)	0.6919 (+0.2652)
ndcg@10	0.6951 (+0.1547)	0.4138 (+0.0888)	0.7294 (+0.2287)

Cross Encoder Nano BEIR

• Dataset: NanoBEIR_R100_mean
• Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus",
          "nq"
      ],
      "rerank_k": 100,
      "at_k": 10,
      "always_rerank_positives": true
  }
  

Metric	Value
map	0.5558 (+0.1658)
mrr@10	0.6446 (+0.1766)
ndcg@10	0.6128 (+0.1574)

Training Details

Training Dataset

msmarco

• Dataset: msmarco at 9e329ed
• Size: 39,770,704 training samples
• Columns: query_id, positive_id, negative_id, and score
• Approximate statistics based on the first 1000 samples:

  	query_id	positive_id	negative_id	score
  type	string	string	string	float
  details	min: 11 characters mean: 33.98 characters max: 124 characters	min: 55 characters mean: 348.53 characters max: 926 characters	min: 80 characters mean: 340.54 characters max: 818 characters	min: -3.12 mean: 13.18 max: 22.49

• Samples:

  query_id	positive_id	negative_id	score
  which artist started fauves movement	Les Fauves (Wild Beasts) • Fauvism was the first twentieth-century movement in modern art. Inspired by. the examples of van Gogh, Gauguin, Seurat, and Cézanne. • It grew out of a loosely allied group of French painters with shared interests. Henri Matisse was eventually recognized as the leader of Les Fauves, or The. Wild Beasts”.	Let’s get a reflection onto your glass, for added realism. Architectural 2D to 3D visualizer and graphic artist Jonathan Pagaduan Ignas shows you how in this great little tutorial he has contributed to us here at SketchUpArtists.Here are a few easy steps to a glass reflection effect using Google SketchUp and V-Ray for SketchUp..let’s get started!Step 1 - Import Image File. Step 2 - Place Image in Glass. Step 3 - Position Texture. Step 4 - Sample Texture with Dropper.rchitectural 2D to 3D visualizer and graphic artist Jonathan Pagaduan Ignas shows you how in this great little tutorial he has contributed to us here at SketchUpArtists. Here are a few easy steps to a glass reflection effect using Google SketchUp and V-Ray for SketchUp..let’s get started!	18.45222806930542
  what foods are high in uric acid list	Avoid high-purine foods. See attached lists. Avoid or limit alcohol. Alcohol increases purine production, leading to higher uric acid levels in your blood and urine. Limit meat to 3 ounces per meal. Limit high-fat foods such as salad dressings, ice cream, fried foods, gravies, and dressings.Fat holds onto uric acid in your kidneys.lcohol increases purine production, leading to higher uric acid levels in your blood and urine. Limit meat to 3 ounces per meal. Limit high-fat foods such as salad dressings, ice cream, fried foods, gravies, and dressings. Fat holds onto uric acid in your kidneys.	You can develop gout if you have too much uric acid in your body. Uric acid is a chemical that everyone has in their blood. It's a waste product that forms from substances called purines. These are found in every cell in your body, and in certain foods, such as seafood and liver.	7.823118130366007
  what battle was the turning point in the pacific theatre	The campaign lasted for nearly six months and cost thousands of American and Japanese lives. This campaign, from the naval perspective, and indeed from a strategic perspective, was the turning point of World War Two in the Pacific Theater. The experienced and well trained Japanese fleet was still superior to the United States fleet after the Battle of Midway, and was still a threat to the US fleet until it was checked in the actions off Guadalcanal.	It is famous as the setting of the February–March 1945 Battle of Iwo Jima involving the United States and a small number of elements of the British Pacific Fleet versus the Empire of Japan during World War II.	7.8578096230824785

• Loss: MarginMSELoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity"
  }
  

Evaluation Dataset

msmarco

• Dataset: msmarco at 9e329ed
• Size: 10,000 evaluation samples
• Columns: query_id, positive_id, negative_id, and score
• Approximate statistics based on the first 1000 samples:

  	query_id	positive_id	negative_id	score
  type	string	string	string	float
  details	min: 11 characters mean: 34.75 characters max: 124 characters	min: 69 characters mean: 346.11 characters max: 920 characters	min: 79 characters mean: 350.68 characters max: 1014 characters	min: -2.41 mean: 13.41 max: 22.48

• Samples:

  query_id	positive_id	negative_id	score
  what is honor society	In the United States, an honor society is a rank organization that recognizes excellence among peers. Numerous societies recognize various fields and circumstances. The Order of the Arrow, for example, is the national honor society of the Boy Scouts of America.	Pi Gamma Mu Headlines. Pi Gamma Mu is the oldest and preeminent honor society in the social sciences. Our mission is to encourage and recognize superior scholarship in social science disciplines and to foster cooperation and social service among its members.	7.578772008419037
  what happens to blood pressure when you raise your arm	Well, you measured blood pressure in that arm would drop because while the pressure that your heart puts out will stay pretty constant, your arm is now higher and requires more energy to reach the end, so the pressure seen there will be lower. The Doc · 7 years ago.	Constriction of blood vessels and increase in heart rate does raise blood pressure, but only temporarily; when the stress reaction goes away, blood pressure returns to its pre-stress level.This is called situational stress, and its effects are generally short-lived and disappear when the stressful event is over.hronic (constant) stress causes our bodies to go into high gear on and off for days or weeks at a time. The links between chronic stress and blood pressure are not clear. Although stress does not clearly cause heart disease, it can play a role in general wellness.	8.200530961155891
  what country is the name astrid from	Comment by silkfire. Astrid, Estrid, Æstriðr æstriðr Ástríðr astriðr ástríðr is a given Name Of north. Germanic origin it Comes From Ásfríðr (Norse Asfriðr), ásfríðr divine (beauty) + from (ass). áss god friðr Fríðr beautiful names derived From, astrid include the name astrida which is a somewhat common name for Girls. in the country of latviat comes from Old Norse Ásfríðr (ásfríðr Divine), beauty from (ass) + áss (god). Friðr fríðr beautiful Names derived from astrid Include, the name astrida which is a somewhat common name for girls in The. country of latvia	» Follow author. » Share. Astrid Ayrianto was on omeegle one day on the video chat, little did she know, she had almost everything in common with the lead singer of her favorite band Get Scared, Nicholas Matthews.	10.449922064940136

• Loss: MarginMSELoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• seed: 12
• bf16: True
• load_best_model_at_end: 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: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 12
• data_seed: None
• jit_mode_eval: 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: 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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: no
• 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: True
• 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_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	-	0.0694 (-0.4711)	0.2964 (-0.0286)	0.0281 (-0.4725)	0.1313 (-0.3241)
0.0001	1	209.9035	-	-	-	-	-
0.0020	39	207.8315	-	-	-	-	-
0.0040	78	204.3864	-	-	-	-	-
0.0060	117	197.3435	-	-	-	-	-
0.0080	156	188.2296	-	-	-	-	-
0.0100	195	176.2927	169.8326	0.1222 (-0.4182)	0.2790 (-0.0460)	0.0840 (-0.4166)	0.1618 (-0.2936)
0.0121	234	161.1845	-	-	-	-	-
0.0141	273	145.5738	-	-	-	-	-
0.0161	312	122.6948	-	-	-	-	-
0.0181	351	81.3822	-	-	-	-	-
0.0201	390	43.1117	35.8988	0.5569 (+0.0164)	0.3702 (+0.0452)	0.4567 (-0.0439)	0.4613 (+0.0059)
0.0221	429	32.5851	-	-	-	-	-
0.0241	468	27.7726	-	-	-	-	-
0.0261	507	24.6193	-	-	-	-	-
0.0281	546	22.2331	-	-	-	-	-
0.0301	585	20.8158	19.3642	0.6007 (+0.0603)	0.3694 (+0.0444)	0.5467 (+0.0460)	0.5056 (+0.0502)
0.0321	624	19.5039	-	-	-	-	-
0.0341	663	18.151	-	-	-	-	-
0.0362	702	17.1317	-	-	-	-	-
0.0382	741	16.6977	-	-	-	-	-
0.0402	780	15.6749	15.1013	0.6167 (+0.0763)	0.3794 (+0.0544)	0.5682 (+0.0675)	0.5214 (+0.0661)
0.0422	819	15.2219	-	-	-	-	-
0.0442	858	14.7046	-	-	-	-	-
0.0462	897	14.1086	-	-	-	-	-
0.0482	936	13.5811	-	-	-	-	-
0.0502	975	13.2202	12.5601	0.6616 (+0.1212)	0.3763 (+0.0513)	0.5839 (+0.0833)	0.5406 (+0.0852)
0.0522	1014	12.7417	-	-	-	-	-
0.0542	1053	12.4341	-	-	-	-	-
0.0562	1092	12.1748	-	-	-	-	-
0.0582	1131	11.8355	-	-	-	-	-
0.0603	1170	11.3288	11.0595	0.6582 (+0.1178)	0.3847 (+0.0596)	0.6125 (+0.1118)	0.5518 (+0.0964)
0.0623	1209	11.2907	-	-	-	-	-
0.0643	1248	10.9028	-	-	-	-	-
0.0663	1287	10.627	-	-	-	-	-
0.0683	1326	10.4322	-	-	-	-	-
0.0703	1365	10.0831	9.7591	0.6565 (+0.1161)	0.3910 (+0.0659)	0.6118 (+0.1111)	0.5531 (+0.0977)
0.0723	1404	10.0484	-	-	-	-	-
0.0743	1443	9.7691	-	-	-	-	-
0.0763	1482	9.5597	-	-	-	-	-
0.0783	1521	9.47	-	-	-	-	-
0.0803	1560	9.2528	8.9910	0.6667 (+0.1263)	0.3815 (+0.0564)	0.6247 (+0.1240)	0.5576 (+0.1023)
0.0823	1599	9.1445	-	-	-	-	-
0.0844	1638	9.0069	-	-	-	-	-
0.0864	1677	8.6523	-	-	-	-	-
0.0884	1716	8.5901	-	-	-	-	-
0.0904	1755	8.715	8.3166	0.6402 (+0.0997)	0.3853 (+0.0603)	0.6448 (+0.1442)	0.5568 (+0.1014)
0.0924	1794	8.457	-	-	-	-	-
0.0944	1833	8.3209	-	-	-	-	-
0.0964	1872	8.1242	-	-	-	-	-
0.0984	1911	8.1453	-	-	-	-	-
0.1004	1950	8.0561	7.6634	0.6806 (+0.1401)	0.3843 (+0.0592)	0.6495 (+0.1489)	0.5715 (+0.1161)
0.1024	1989	7.855	-	-	-	-	-
0.1044	2028	7.7573	-	-	-	-	-
0.1064	2067	7.5976	-	-	-	-	-
0.1085	2106	7.4529	-	-	-	-	-
0.1105	2145	7.372	7.0798	0.6667 (+0.1263)	0.3952 (+0.0702)	0.6386 (+0.1379)	0.5668 (+0.1115)
0.1125	2184	7.1673	-	-	-	-	-
0.1145	2223	7.2075	-	-	-	-	-
0.1165	2262	7.1261	-	-	-	-	-
0.1185	2301	7.0716	-	-	-	-	-
0.1205	2340	7.0182	6.7560	0.6557 (+0.1153)	0.3876 (+0.0626)	0.6544 (+0.1537)	0.5659 (+0.1105)
0.1225	2379	6.861	-	-	-	-	-
0.1245	2418	6.8366	-	-	-	-	-
0.1265	2457	6.6857	-	-	-	-	-
0.1285	2496	6.665	-	-	-	-	-
0.1305	2535	6.6564	6.2824	0.6796 (+0.1392)	0.3834 (+0.0584)	0.6637 (+0.1630)	0.5756 (+0.1202)
0.1326	2574	6.5436	-	-	-	-	-
0.1346	2613	6.4091	-	-	-	-	-
0.1366	2652	6.2974	-	-	-	-	-
0.1386	2691	6.3251	-	-	-	-	-
0.1406	2730	6.2799	5.9852	0.6719 (+0.1314)	0.3829 (+0.0579)	0.6711 (+0.1704)	0.5753 (+0.1199)
0.1426	2769	6.2637	-	-	-	-	-
0.1446	2808	6.1648	-	-	-	-	-
0.1466	2847	6.1382	-	-	-	-	-
0.1486	2886	6.1291	-	-	-	-	-
0.1506	2925	5.9413	5.8402	0.6665 (+0.1261)	0.3950 (+0.0700)	0.6448 (+0.1441)	0.5688 (+0.1134)
0.1526	2964	5.9748	-	-	-	-	-
0.1546	3003	5.8973	-	-	-	-	-
0.1567	3042	5.8654	-	-	-	-	-
0.1587	3081	5.6654	-	-	-	-	-
0.1607	3120	5.8549	5.6918	0.6765 (+0.1361)	0.3948 (+0.0697)	0.6639 (+0.1632)	0.5784 (+0.1230)
0.1627	3159	5.6934	-	-	-	-	-
0.1647	3198	5.7285	-	-	-	-	-
0.1667	3237	5.5589	-	-	-	-	-
0.1687	3276	5.6317	-	-	-	-	-
0.1707	3315	5.5741	5.4521	0.6545 (+0.1141)	0.3988 (+0.0738)	0.6863 (+0.1856)	0.5799 (+0.1245)
0.1727	3354	5.4948	-	-	-	-	-
0.1747	3393	5.4782	-	-	-	-	-
0.1767	3432	5.5595	-	-	-	-	-
0.1787	3471	5.417	-	-	-	-	-
0.1808	3510	5.4339	5.2730	0.6224 (+0.0820)	0.3970 (+0.0720)	0.6666 (+0.1660)	0.5620 (+0.1066)
0.1828	3549	5.3723	-	-	-	-	-
0.1848	3588	5.2479	-	-	-	-	-
0.1868	3627	5.2665	-	-	-	-	-
0.1888	3666	5.2302	-	-	-	-	-
0.1908	3705	5.1863	5.1419	0.6791 (+0.1387)	0.4037 (+0.0786)	0.6941 (+0.1934)	0.5923 (+0.1369)
0.1928	3744	5.1855	-	-	-	-	-
0.1948	3783	5.1529	-	-	-	-	-
0.1968	3822	5.2058	-	-	-	-	-
0.1988	3861	5.098	-	-	-	-	-
0.2008	3900	5.0176	4.9869	0.6590 (+0.1186)	0.3849 (+0.0599)	0.6844 (+0.1837)	0.5761 (+0.1207)
0.2028	3939	5.0708	-	-	-	-	-
0.2049	3978	5.0215	-	-	-	-	-
0.2069	4017	4.974	-	-	-	-	-
0.2089	4056	4.9687	-	-	-	-	-
0.2109	4095	4.9689	4.8506	0.6734 (+0.1330)	0.3995 (+0.0745)	0.7023 (+0.2016)	0.5917 (+0.1364)
0.2129	4134	4.8809	-	-	-	-	-
0.2149	4173	4.9176	-	-	-	-	-
0.2169	4212	4.7451	-	-	-	-	-
0.2189	4251	4.7807	-	-	-	-	-
0.2209	4290	4.8157	4.7150	0.6269 (+0.0865)	0.3948 (+0.0698)	0.6938 (+0.1932)	0.5719 (+0.1165)
0.2229	4329	4.7986	-	-	-	-	-
0.2249	4368	4.7942	-	-	-	-	-
0.2269	4407	4.7008	-	-	-	-	-
0.2290	4446	4.7572	-	-	-	-	-
0.2310	4485	4.7616	4.6657	0.6577 (+0.1172)	0.4022 (+0.0772)	0.7019 (+0.2013)	0.5873 (+0.1319)
0.2330	4524	4.7014	-	-	-	-	-
0.2350	4563	4.6512	-	-	-	-	-
0.2370	4602	4.6997	-	-	-	-	-
0.2390	4641	4.5655	-	-	-	-	-
0.2410	4680	4.5727	4.5367	0.6826 (+0.1422)	0.3937 (+0.0687)	0.6958 (+0.1951)	0.5907 (+0.1353)
0.2430	4719	4.5258	-	-	-	-	-
0.2450	4758	4.6012	-	-	-	-	-
0.2470	4797	4.5785	-	-	-	-	-
0.2490	4836	4.5415	-	-	-	-	-
0.2510	4875	4.4921	4.4462	0.6689 (+0.1285)	0.3977 (+0.0727)	0.7103 (+0.2096)	0.5923 (+0.1369)
0.2531	4914	4.4911	-	-	-	-	-
0.2551	4953	4.4795	-	-	-	-	-
0.2571	4992	4.4027	-	-	-	-	-
0.2591	5031	4.3652	-	-	-	-	-
0.2611	5070	4.3868	4.3909	0.6535 (+0.1130)	0.3868 (+0.0617)	0.6815 (+0.1809)	0.5739 (+0.1185)
0.2631	5109	4.4055	-	-	-	-	-
0.2651	5148	4.3968	-	-	-	-	-
0.2671	5187	4.3333	-	-	-	-	-
0.2691	5226	4.3369	-	-	-	-	-
0.2711	5265	4.3079	4.3355	0.6550 (+0.1145)	0.3895 (+0.0644)	0.6957 (+0.1950)	0.5800 (+0.1247)
0.2731	5304	4.3211	-	-	-	-	-
0.2751	5343	4.2841	-	-	-	-	-
0.2772	5382	4.2753	-	-	-	-	-
0.2792	5421	4.221	-	-	-	-	-
0.2812	5460	4.2146	4.2056	0.6590 (+0.1186)	0.3796 (+0.0545)	0.7028 (+0.2021)	0.5804 (+0.1251)
0.2832	5499	4.2692	-	-	-	-	-
0.2852	5538	4.2236	-	-	-	-	-
0.2872	5577	4.1555	-	-	-	-	-
0.2892	5616	4.1684	-	-	-	-	-
0.2912	5655	4.1731	4.1304	0.6734 (+0.1329)	0.3865 (+0.0615)	0.6950 (+0.1944)	0.5850 (+0.1296)
0.2932	5694	4.1562	-	-	-	-	-
0.2952	5733	4.1689	-	-	-	-	-
0.2972	5772	4.1617	-	-	-	-	-
0.2992	5811	4.1256	-	-	-	-	-
0.3013	5850	4.0592	4.0723	0.6694 (+0.1290)	0.3870 (+0.0619)	0.7079 (+0.2073)	0.5881 (+0.1327)
0.3033	5889	4.0894	-	-	-	-	-
0.3053	5928	4.103	-	-	-	-	-
0.3073	5967	4.0083	-	-	-	-	-
0.3093	6006	4.03	-	-	-	-	-
0.3113	6045	3.9931	4.0058	0.6695 (+0.1290)	0.3914 (+0.0664)	0.7024 (+0.2018)	0.5878 (+0.1324)
0.3133	6084	4.0186	-	-	-	-	-
0.3153	6123	3.9312	-	-	-	-	-
0.3173	6162	4.0398	-	-	-	-	-
0.3193	6201	3.9672	-	-	-	-	-
0.3213	6240	3.9879	3.9322	0.6696 (+0.1292)	0.3932 (+0.0681)	0.7075 (+0.2068)	0.5901 (+0.1347)
0.3233	6279	3.879	-	-	-	-	-
0.3254	6318	3.9123	-	-	-	-	-
0.3274	6357	3.9144	-	-	-	-	-
0.3294	6396	3.903	-	-	-	-	-
0.3314	6435	3.9447	3.9070	0.6440 (+0.1036)	0.3849 (+0.0599)	0.7099 (+0.2092)	0.5796 (+0.1242)
0.3334	6474	3.9082	-	-	-	-	-
0.3354	6513	3.8405	-	-	-	-	-
0.3374	6552	3.8633	-	-	-	-	-
0.3394	6591	3.8301	-	-	-	-	-
0.3414	6630	3.8188	3.8524	0.6611 (+0.1206)	0.3804 (+0.0554)	0.6935 (+0.1929)	0.5783 (+0.1230)
0.3434	6669	3.8292	-	-	-	-	-
0.3454	6708	3.8502	-	-	-	-	-
0.3474	6747	3.8649	-	-	-	-	-
0.3495	6786	3.7942	-	-	-	-	-
0.3515	6825	3.7324	3.7958	0.6732 (+0.1328)	0.3816 (+0.0565)	0.7011 (+0.2005)	0.5853 (+0.1299)
0.3535	6864	3.7916	-	-	-	-	-
0.3555	6903	3.7299	-	-	-	-	-
0.3575	6942	3.8087	-	-	-	-	-
0.3595	6981	3.7821	-	-	-	-	-
0.3615	7020	3.7395	3.7438	0.6585 (+0.1181)	0.3875 (+0.0625)	0.7004 (+0.1998)	0.5821 (+0.1268)
0.3635	7059	3.7345	-	-	-	-	-
0.3655	7098	3.7472	-	-	-	-	-
0.3675	7137	3.7277	-	-	-	-	-
0.3695	7176	3.6535	-	-	-	-	-
0.3715	7215	3.6586	3.6982	0.6616 (+0.1212)	0.3823 (+0.0573)	0.7002 (+0.1996)	0.5814 (+0.1260)
0.3736	7254	3.6653	-	-	-	-	-
0.3756	7293	3.7074	-	-	-	-	-
0.3776	7332	3.6542	-	-	-	-	-
0.3796	7371	3.5972	-	-	-	-	-
0.3816	7410	3.6499	3.6283	0.6569 (+0.1165)	0.3796 (+0.0545)	0.7079 (+0.2073)	0.5815 (+0.1261)
0.3836	7449	3.6373	-	-	-	-	-
0.3856	7488	3.6253	-	-	-	-	-
0.3876	7527	3.6441	-	-	-	-	-
0.3896	7566	3.6278	-	-	-	-	-
0.3916	7605	3.6291	3.6008	0.6695 (+0.1291)	0.3928 (+0.0677)	0.7187 (+0.2180)	0.5936 (+0.1383)
0.3936	7644	3.5957	-	-	-	-	-
0.3956	7683	3.6031	-	-	-	-	-
0.3977	7722	3.5544	-	-	-	-	-
0.3997	7761	3.5823	-	-	-	-	-
0.4017	7800	3.5426	3.6026	0.6732 (+0.1328)	0.3851 (+0.0600)	0.7170 (+0.2163)	0.5918 (+0.1364)
0.4037	7839	3.5943	-	-	-	-	-
0.4057	7878	3.4955	-	-	-	-	-
0.4077	7917	3.5072	-	-	-	-	-
0.4097	7956	3.5529	-	-	-	-	-
0.4117	7995	3.5523	3.4759	0.6666 (+0.1262)	0.3826 (+0.0576)	0.7048 (+0.2041)	0.5847 (+0.1293)
0.4137	8034	3.4947	-	-	-	-	-
0.4157	8073	3.4753	-	-	-	-	-
0.4177	8112	3.431	-	-	-	-	-
0.4197	8151	3.4871	-	-	-	-	-
0.4218	8190	3.5072	3.4841	0.6703 (+0.1299)	0.3880 (+0.0630)	0.7216 (+0.2210)	0.5933 (+0.1380)
0.4238	8229	3.4812	-	-	-	-	-
0.4258	8268	3.4408	-	-	-	-	-
0.4278	8307	3.4781	-	-	-	-	-
0.4298	8346	3.4667	-	-	-	-	-
0.4318	8385	3.4618	3.4575	0.6709 (+0.1305)	0.3785 (+0.0535)	0.7062 (+0.2055)	0.5852 (+0.1298)
0.4338	8424	3.4711	-	-	-	-	-
0.4358	8463	3.4586	-	-	-	-	-
0.4378	8502	3.4246	-	-	-	-	-
0.4398	8541	3.4322	-	-	-	-	-
0.4418	8580	3.3901	3.4289	0.6657 (+0.1253)	0.3780 (+0.0530)	0.7118 (+0.2111)	0.5852 (+0.1298)
0.4438	8619	3.3776	-	-	-	-	-
0.4459	8658	3.3819	-	-	-	-	-
0.4479	8697	3.393	-	-	-	-	-
0.4499	8736	3.3559	-	-	-	-	-
0.4519	8775	3.4004	3.2981	0.6748 (+0.1344)	0.3870 (+0.0620)	0.7121 (+0.2114)	0.5913 (+0.1359)
0.4539	8814	3.426	-	-	-	-	-
0.4559	8853	3.3938	-	-	-	-	-
0.4579	8892	3.3304	-	-	-	-	-
0.4599	8931	3.3455	-	-	-	-	-
0.4619	8970	3.3314	3.3531	0.6700 (+0.1296)	0.3775 (+0.0524)	0.7173 (+0.2166)	0.5882 (+0.1329)
0.4639	9009	3.3455	-	-	-	-	-
0.4659	9048	3.3363	-	-	-	-	-
0.4679	9087	3.3492	-	-	-	-	-
0.4700	9126	3.3272	-	-	-	-	-
0.4720	9165	3.3253	3.2839	0.6963 (+0.1558)	0.3831 (+0.0581)	0.7113 (+0.2107)	0.5969 (+0.1415)
0.4740	9204	3.3214	-	-	-	-	-
0.4760	9243	3.2383	-	-	-	-	-
0.4780	9282	3.3198	-	-	-	-	-
0.4800	9321	3.2898	-	-	-	-	-
0.4820	9360	3.2641	3.2287	0.6832 (+0.1428)	0.3880 (+0.0630)	0.7206 (+0.2200)	0.5973 (+0.1419)
0.4840	9399	3.2974	-	-	-	-	-
0.4860	9438	3.3147	-	-	-	-	-
0.4880	9477	3.2911	-	-	-	-	-
0.4900	9516	3.2745	-	-	-	-	-
0.4920	9555	3.2168	3.2729	0.6730 (+0.1326)	0.3920 (+0.0669)	0.7173 (+0.2167)	0.5941 (+0.1387)
0.4941	9594	3.2036	-	-	-	-	-
0.4961	9633	3.2618	-	-	-	-	-
0.4981	9672	3.2786	-	-	-	-	-
0.5001	9711	3.2214	-	-	-	-	-
0.5021	9750	3.1759	3.2106	0.6764 (+0.1360)	0.3852 (+0.0602)	0.7226 (+0.2220)	0.5948 (+0.1394)
0.5041	9789	3.2159	-	-	-	-	-
0.5061	9828	3.1856	-	-	-	-	-
0.5081	9867	3.209	-	-	-	-	-
0.5101	9906	3.2239	-	-	-	-	-
0.5121	9945	3.2142	3.2159	0.6697 (+0.1292)	0.3913 (+0.0662)	0.7009 (+0.2003)	0.5873 (+0.1319)
0.5141	9984	3.2156	-	-	-	-	-
0.5161	10023	3.1568	-	-	-	-	-
0.5182	10062	3.1344	-	-	-	-	-
0.5202	10101	3.1754	-	-	-	-	-
0.5222	10140	3.1414	3.2009	0.6913 (+0.1509)	0.3913 (+0.0663)	0.7180 (+0.2173)	0.6002 (+0.1448)
0.5242	10179	3.2359	-	-	-	-	-
0.5262	10218	3.149	-	-	-	-	-
0.5282	10257	3.1741	-	-	-	-	-
0.5302	10296	3.081	-	-	-	-	-
0.5322	10335	3.1457	3.1939	0.6685 (+0.1281)	0.3858 (+0.0608)	0.7304 (+0.2297)	0.5949 (+0.1395)
0.5342	10374	3.1322	-	-	-	-	-
0.5362	10413	3.1307	-	-	-	-	-
0.5382	10452	3.1212	-	-	-	-	-
0.5402	10491	3.105	-	-	-	-	-
0.5423	10530	3.1339	3.1324	0.6693 (+0.1289)	0.3942 (+0.0692)	0.7101 (+0.2094)	0.5912 (+0.1358)
0.5443	10569	3.111	-	-	-	-	-
0.5463	10608	3.1045	-	-	-	-	-
0.5483	10647	3.0923	-	-	-	-	-
0.5503	10686	3.075	-	-	-	-	-
0.5523	10725	3.062	3.1363	0.6931 (+0.1527)	0.4079 (+0.0828)	0.7233 (+0.2227)	0.6081 (+0.1527)
0.5543	10764	3.1167	-	-	-	-	-
0.5563	10803	3.0903	-	-	-	-	-
0.5583	10842	3.0604	-	-	-	-	-
0.5603	10881	3.0724	-	-	-	-	-
0.5623	10920	3.1052	3.1181	0.6856 (+0.1452)	0.4003 (+0.0753)	0.7249 (+0.2242)	0.6036 (+0.1482)
0.5643	10959	3.0984	-	-	-	-	-
0.5664	10998	3.0779	-	-	-	-	-
0.5684	11037	2.9911	-	-	-	-	-
0.5704	11076	3.0431	-	-	-	-	-
0.5724	11115	3.0793	3.1106	0.6709 (+0.1305)	0.3998 (+0.0748)	0.7186 (+0.2180)	0.5965 (+0.1411)
0.5744	11154	3.0504	-	-	-	-	-
0.5764	11193	3.0118	-	-	-	-	-
0.5784	11232	3.0704	-	-	-	-	-
0.5804	11271	3.0498	-	-	-	-	-
0.5824	11310	3.0393	3.0475	0.6806 (+0.1401)	0.4091 (+0.0841)	0.7122 (+0.2116)	0.6006 (+0.1453)
0.5844	11349	2.9919	-	-	-	-	-
0.5864	11388	3.0291	-	-	-	-	-
0.5884	11427	3.0119	-	-	-	-	-
0.5905	11466	3.0201	-	-	-	-	-
0.5925	11505	3.0319	3.0064	0.6852 (+0.1448)	0.4061 (+0.0811)	0.7163 (+0.2156)	0.6025 (+0.1472)
0.5945	11544	3.0309	-	-	-	-	-
0.5965	11583	3.0097	-	-	-	-	-
0.5985	11622	2.9759	-	-	-	-	-
0.6005	11661	2.9937	-	-	-	-	-
0.6025	11700	2.9885	3.0262	0.6729 (+0.1325)	0.3985 (+0.0735)	0.7227 (+0.2221)	0.5980 (+0.1427)
0.6045	11739	3.0092	-	-	-	-	-
0.6065	11778	2.9569	-	-	-	-	-
0.6085	11817	2.9665	-	-	-	-	-
0.6105	11856	2.9774	-	-	-	-	-
0.6125	11895	2.9915	2.9842	0.6857 (+0.1452)	0.4055 (+0.0804)	0.7379 (+0.2372)	0.6097 (+0.1543)
0.6146	11934	2.9555	-	-	-	-	-
0.6166	11973	2.9833	-	-	-	-	-
0.6186	12012	2.9858	-	-	-	-	-
0.6206	12051	2.9743	-	-	-	-	-
0.6226	12090	2.9686	2.9523	0.6821 (+0.1417)	0.4044 (+0.0794)	0.7238 (+0.2232)	0.6035 (+0.1481)
0.6246	12129	2.9867	-	-	-	-	-
0.6266	12168	2.9548	-	-	-	-	-
0.6286	12207	2.9557	-	-	-	-	-
0.6306	12246	2.9506	-	-	-	-	-
0.6326	12285	2.9692	2.9323	0.6862 (+0.1458)	0.4063 (+0.0813)	0.7257 (+0.2251)	0.6061 (+0.1507)
0.6346	12324	2.9458	-	-	-	-	-
0.6366	12363	2.9131	-	-	-	-	-
0.6387	12402	2.9118	-	-	-	-	-
0.6407	12441	2.8914	-	-	-	-	-
0.6427	12480	2.8845	2.9832	0.6796 (+0.1392)	0.4009 (+0.0759)	0.7250 (+0.2244)	0.6019 (+0.1465)
0.6447	12519	2.9312	-	-	-	-	-
0.6467	12558	2.9344	-	-	-	-	-
0.6487	12597	2.9033	-	-	-	-	-
0.6507	12636	2.892	-	-	-	-	-
0.6527	12675	2.8944	2.9286	0.6874 (+0.1469)	0.4015 (+0.0764)	0.7247 (+0.2241)	0.6045 (+0.1491)
0.6547	12714	2.9269	-	-	-	-	-
0.6567	12753	2.8988	-	-	-	-	-
0.6587	12792	2.9167	-	-	-	-	-
0.6607	12831	2.8703	-	-	-	-	-
0.6628	12870	2.8619	2.8724	0.6829 (+0.1425)	0.4005 (+0.0754)	0.7176 (+0.2170)	0.6003 (+0.1450)
0.6648	12909	2.868	-	-	-	-	-
0.6668	12948	2.8775	-	-	-	-	-
0.6688	12987	2.866	-	-	-	-	-
0.6708	13026	2.8877	-	-	-	-	-
0.6728	13065	2.896	2.9117	0.6846 (+0.1442)	0.4023 (+0.0773)	0.7198 (+0.2192)	0.6022 (+0.1469)
0.6748	13104	2.8351	-	-	-	-	-
0.6768	13143	2.8679	-	-	-	-	-
0.6788	13182	2.9197	-	-	-	-	-
0.6808	13221	2.822	-	-	-	-	-
0.6828	13260	2.8443	2.9010	0.6775 (+0.1371)	0.4039 (+0.0789)	0.7116 (+0.2109)	0.5977 (+0.1423)
0.6848	13299	2.8646	-	-	-	-	-
0.6869	13338	2.8645	-	-	-	-	-
0.6889	13377	2.8659	-	-	-	-	-
0.6909	13416	2.8264	-	-	-	-	-
0.6929	13455	2.8222	2.8632	0.6947 (+0.1543)	0.4057 (+0.0807)	0.7314 (+0.2307)	0.6106 (+0.1552)
0.6949	13494	2.8565	-	-	-	-	-
0.6969	13533	2.8385	-	-	-	-	-
0.6989	13572	2.8305	-	-	-	-	-
0.7009	13611	2.8368	-	-	-	-	-
0.7029	13650	2.8416	2.8309	0.6784 (+0.1380)	0.4113 (+0.0863)	0.7270 (+0.2264)	0.6056 (+0.1502)
0.7049	13689	2.8007	-	-	-	-	-
0.7069	13728	2.8565	-	-	-	-	-
0.7089	13767	2.8893	-	-	-	-	-
0.7110	13806	2.844	-	-	-	-	-
0.7130	13845	2.8293	2.8423	0.6693 (+0.1289)	0.4053 (+0.0803)	0.7244 (+0.2238)	0.5997 (+0.1443)
0.7150	13884	2.8424	-	-	-	-	-
0.7170	13923	2.7951	-	-	-	-	-
0.7190	13962	2.8004	-	-	-	-	-
0.7210	14001	2.7833	-	-	-	-	-
0.7230	14040	2.8133	2.8021	0.6788 (+0.1383)	0.4013 (+0.0763)	0.7285 (+0.2278)	0.6028 (+0.1475)
0.7250	14079	2.8245	-	-	-	-	-
0.7270	14118	2.7995	-	-	-	-	-
0.7290	14157	2.7859	-	-	-	-	-
0.7310	14196	2.8067	-	-	-	-	-
0.7330	14235	2.7606	2.8098	0.6750 (+0.1346)	0.4012 (+0.0762)	0.7381 (+0.2375)	0.6048 (+0.1494)
0.7351	14274	2.7662	-	-	-	-	-
0.7371	14313	2.8081	-	-	-	-	-
0.7391	14352	2.8159	-	-	-	-	-
0.7411	14391	2.7604	-	-	-	-	-
0.7431	14430	2.7721	2.7912	0.6759 (+0.1355)	0.4040 (+0.0790)	0.7283 (+0.2276)	0.6027 (+0.1474)
0.7451	14469	2.7772	-	-	-	-	-
0.7471	14508	2.8183	-	-	-	-	-
0.7491	14547	2.7821	-	-	-	-	-
0.7511	14586	2.7434	-	-	-	-	-
0.7531	14625	2.812	2.7940	0.6673 (+0.1268)	0.4034 (+0.0783)	0.7277 (+0.2271)	0.5995 (+0.1441)
0.7551	14664	2.7847	-	-	-	-	-
0.7571	14703	2.7604	-	-	-	-	-
0.7592	14742	2.7271	-	-	-	-	-
0.7612	14781	2.7663	-	-	-	-	-
0.7632	14820	2.7731	2.7489	0.6694 (+0.1290)	0.3963 (+0.0713)	0.7380 (+0.2373)	0.6012 (+0.1459)
0.7652	14859	2.8013	-	-	-	-	-
0.7672	14898	2.762	-	-	-	-	-
0.7692	14937	2.7646	-	-	-	-	-
0.7712	14976	2.762	-	-	-	-	-
0.7732	15015	2.77	2.7367	0.6815 (+0.1411)	0.4066 (+0.0816)	0.7262 (+0.2256)	0.6048 (+0.1494)
0.7752	15054	2.7827	-	-	-	-	-
0.7772	15093	2.7027	-	-	-	-	-
0.7792	15132	2.7395	-	-	-	-	-
0.7812	15171	2.7425	-	-	-	-	-
0.7833	15210	2.7757	2.7380	0.6720 (+0.1315)	0.4057 (+0.0807)	0.7374 (+0.2367)	0.6050 (+0.1497)
0.7853	15249	2.7185	-	-	-	-	-
0.7873	15288	2.7287	-	-	-	-	-
0.7893	15327	2.7311	-	-	-	-	-
0.7913	15366	2.7281	-	-	-	-	-
0.7933	15405	2.7039	2.7142	0.6854 (+0.1450)	0.4034 (+0.0784)	0.7364 (+0.2358)	0.6084 (+0.1531)
0.7953	15444	2.7028	-	-	-	-	-
0.7973	15483	2.7182	-	-	-	-	-
0.7993	15522	2.688	-	-	-	-	-
0.8013	15561	2.7562	-	-	-	-	-
0.8033	15600	2.6968	2.7413	0.6669 (+0.1264)	0.4118 (+0.0867)	0.7269 (+0.2263)	0.6018 (+0.1465)
0.8053	15639	2.6802	-	-	-	-	-
0.8074	15678	2.6936	-	-	-	-	-
0.8094	15717	2.7059	-	-	-	-	-
0.8114	15756	2.7052	-	-	-	-	-
0.8134	15795	2.7178	2.6968	0.6738 (+0.1334)	0.4064 (+0.0813)	0.7283 (+0.2277)	0.6028 (+0.1475)
0.8154	15834	2.669	-	-	-	-	-
0.8174	15873	2.6881	-	-	-	-	-
0.8194	15912	2.6973	-	-	-	-	-
0.8214	15951	2.6861	-	-	-	-	-
0.8234	15990	2.6695	2.7110	0.6821 (+0.1417)	0.4104 (+0.0853)	0.7304 (+0.2298)	0.6076 (+0.1523)
0.8254	16029	2.6974	-	-	-	-	-
0.8274	16068	2.7043	-	-	-	-	-
0.8294	16107	2.6929	-	-	-	-	-
0.8315	16146	2.667	-	-	-	-	-
0.8335	16185	2.7035	2.6746	0.6891 (+0.1487)	0.4084 (+0.0834)	0.7262 (+0.2256)	0.6079 (+0.1526)
0.8355	16224	2.6472	-	-	-	-	-
0.8375	16263	2.6956	-	-	-	-	-
0.8395	16302	2.6823	-	-	-	-	-
0.8415	16341	2.695	-	-	-	-	-
0.8435	16380	2.6439	2.6841	0.6835 (+0.1430)	0.4015 (+0.0764)	0.7288 (+0.2282)	0.6046 (+0.1492)
0.8455	16419	2.6767	-	-	-	-	-
0.8475	16458	2.6556	-	-	-	-	-
0.8495	16497	2.6431	-	-	-	-	-
0.8515	16536	2.6594	-	-	-	-	-
0.8535	16575	2.6403	2.6657	0.6709 (+0.1304)	0.4044 (+0.0794)	0.7323 (+0.2316)	0.6025 (+0.1471)
0.8556	16614	2.6687	-	-	-	-	-
0.8576	16653	2.6887	-	-	-	-	-
0.8596	16692	2.6761	-	-	-	-	-
0.8616	16731	2.6371	-	-	-	-	-
0.8636	16770	2.6368	2.6791	0.6768 (+0.1364)	0.4068 (+0.0818)	0.7314 (+0.2308)	0.6050 (+0.1497)
0.8656	16809	2.6325	-	-	-	-	-
0.8676	16848	2.641	-	-	-	-	-
0.8696	16887	2.6614	-	-	-	-	-
0.8716	16926	2.7	-	-	-	-	-
0.8736	16965	2.678	2.6354	0.6938 (+0.1533)	0.4108 (+0.0857)	0.7274 (+0.2268)	0.6107 (+0.1553)
0.8756	17004	2.6619	-	-	-	-	-
0.8776	17043	2.681	-	-	-	-	-
0.8797	17082	2.6465	-	-	-	-	-
0.8817	17121	2.6977	-	-	-	-	-
0.8837	17160	2.6476	2.6324	0.6969 (+0.1565)	0.4118 (+0.0868)	0.7293 (+0.2287)	0.6127 (+0.1573)
0.8857	17199	2.6405	-	-	-	-	-
0.8877	17238	2.6645	-	-	-	-	-
0.8897	17277	2.6635	-	-	-	-	-
0.8917	17316	2.6038	-	-	-	-	-
0.8937	17355	2.6807	2.642	0.6951 (+0.1547)	0.4138 (+0.0888)	0.7294 (+0.2287)	0.6128 (+0.1574)
0.8957	17394	2.6679	-	-	-	-	-
0.8977	17433	2.6063	-	-	-	-	-
0.8997	17472	2.6215	-	-	-	-	-
0.9017	17511	2.6141	-	-	-	-	-
0.9038	17550	2.6324	2.6330	0.6963 (+0.1559)	0.4117 (+0.0866)	0.7287 (+0.2280)	0.6122 (+0.1568)
0.9058	17589	2.6405	-	-	-	-	-
0.9078	17628	2.619	-	-	-	-	-
0.9098	17667	2.6153	-	-	-	-	-
0.9118	17706	2.6323	-	-	-	-	-
0.9138	17745	2.5891	2.6280	0.6886 (+0.1482)	0.4110 (+0.0860)	0.7275 (+0.2268)	0.6090 (+0.1537)
0.9158	17784	2.6385	-	-	-	-	-
0.9178	17823	2.6138	-	-	-	-	-
0.9198	17862	2.6181	-	-	-	-	-
0.9218	17901	2.6414	-	-	-	-	-
0.9238	17940	2.6362	2.6224	0.6907 (+0.1502)	0.4085 (+0.0835)	0.7360 (+0.2354)	0.6117 (+0.1564)
0.9258	17979	2.6156	-	-	-	-	-
0.9279	18018	2.597	-	-	-	-	-
0.9299	18057	2.6254	-	-	-	-	-
0.9319	18096	2.6434	-	-	-	-	-
0.9339	18135	2.6474	2.6014	0.6884 (+0.1479)	0.4133 (+0.0882)	0.7248 (+0.2242)	0.6088 (+0.1534)
0.9359	18174	2.6214	-	-	-	-	-
0.9379	18213	2.6145	-	-	-	-	-
0.9399	18252	2.617	-	-	-	-	-
0.9419	18291	2.6209	-	-	-	-	-
0.9439	18330	2.5976	2.6057	0.6871 (+0.1467)	0.4072 (+0.0821)	0.7290 (+0.2284)	0.6078 (+0.1524)
0.9459	18369	2.61	-	-	-	-	-
0.9479	18408	2.614	-	-	-	-	-
0.9499	18447	2.6187	-	-	-	-	-
0.9520	18486	2.6004	-	-	-	-	-
0.9540	18525	2.6515	2.6052	0.6871 (+0.1467)	0.4091 (+0.0841)	0.7270 (+0.2263)	0.6077 (+0.1524)
0.9560	18564	2.6141	-	-	-	-	-
0.9580	18603	2.6016	-	-	-	-	-
0.9600	18642	2.5918	-	-	-	-	-
0.9620	18681	2.5684	-	-	-	-	-
0.9640	18720	2.6087	2.5941	0.6797 (+0.1393)	0.4105 (+0.0854)	0.7284 (+0.2277)	0.6062 (+0.1508)
0.9660	18759	2.5961	-	-	-	-	-
0.9680	18798	2.6121	-	-	-	-	-
0.9700	18837	2.5896	-	-	-	-	-
0.9720	18876	2.6101	-	-	-	-	-
0.9740	18915	2.6106	2.5921	0.6856 (+0.1452)	0.4093 (+0.0843)	0.7284 (+0.2277)	0.6078 (+0.1524)
0.9761	18954	2.6046	-	-	-	-	-
0.9781	18993	2.6155	-	-	-	-	-
0.9801	19032	2.6166	-	-	-	-	-
0.9821	19071	2.5866	-	-	-	-	-
0.9841	19110	2.6369	2.5943	0.6838 (+0.1433)	0.4117 (+0.0867)	0.7284 (+0.2277)	0.6079 (+0.1526)
0.9861	19149	2.6319	-	-	-	-	-
0.9881	19188	2.6035	-	-	-	-	-
0.9901	19227	2.565	-	-	-	-	-
0.9921	19266	2.6071	-	-	-	-	-
0.9941	19305	2.5908	2.5866	0.6838 (+0.1433)	0.4116 (+0.0866)	0.7284 (+0.2277)	0.6079 (+0.1525)
0.9961	19344	2.5983	-	-	-	-	-
0.9981	19383	2.5995	-	-	-	-	-
-1	-1	-	-	0.6951 (+0.1547)	0.4138 (+0.0888)	0.7294 (+0.2287)	0.6128 (+0.1574)

• The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 6.535 kWh
• Carbon Emitted: 2.412 kg of CO2
• Hours Used: 1.932 hours

Training Hardware

• On Cloud: No
• GPU Model: 8 x NVIDIA H100 80GB HBM3
• CPU Model: AMD EPYC 7R13 Processor
• RAM Size: 1999.99 GB

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.9.1+cu126
• Accelerate: 1.12.0
• Datasets: 4.4.1
• Tokenizers: 0.22.1

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",
}

MarginMSELoss

@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}
}