chenbowen184/instacart-two-tower-sbert

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

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '[+30d w5h19] Banana, Ezekiel 4:9 Bread Organic Sprouted Whole Grain, Grade A Cage Free Omega-3 Enriched Medium Eggs, Sweet Potatoes; [+10d w1h18] Cookie Dough Ice Cream Chocolate Chip, Peanut Butter Ice Cream Cup, Blackberry Cucumber Sparkling Water, Kiwi Sandia Sparkling Water, Protein Power Cold Pressed Fruit Smoothie, Defense Up Cold Pressed Fruit Smoothie; [+30d w4h12] Blackberry Cucumber Sparkling Water, Peanut Butter Ice Cream Cup, Cookie Dough Ice Cream Chocolate Chip, Cran Raspberry Sparkling Water, Lemon Italian Sparkling Mineral Water, Natural Chicken & Sage Breakfast Sausage, Organic Jasmine Rice, Organic Riced Cauliflower, Country Cheddar Bowl; [+5d w2h10] Original Rotisserie Chicken. Next: +16d w0h14',
    'Product: Large Lemon. Aisle: fresh fruits. Department: produce.',
    'Product: Blood Orange Italian Soda. Aisle: soft drinks. Department: beverages.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3506, 0.3826],
#         [0.3506, 1.0000, 0.6395],
#         [0.3826, 0.6395, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: order-recommendation
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.232
cosine_accuracy@3	0.3826
cosine_accuracy@5	0.4508
cosine_accuracy@10	0.5377
cosine_precision@1	0.232
cosine_precision@3	0.1581
cosine_precision@5	0.1246
cosine_precision@10	0.0852
cosine_recall@1	0.0398
cosine_recall@3	0.0764
cosine_recall@5	0.0975
cosine_recall@10	0.1278
cosine_ndcg@10	0.1511
cosine_mrr@10	0.3253
cosine_map@100	0.0849

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,246,220 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 76 tokens mean: 151.76 tokens max: 208 tokens	min: 15 tokens mean: 20.12 tokens max: 38 tokens

• Samples:

  anchor	positive
  [w1h14] Tuna Ventresca, in Olive Oil, Lightly Smoked Sardines in Olive Oil, Naturally Smoked Oysters in Pure Olive Oil, Bulgarian Yogurt, Organic Whole String Cheese, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery; [+7d w1h10] Bulgarian Yogurt, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery, Organic Whole String Cheese, Banana, Plus Cranberry Almond + Antioxidants with Macadamia Nuts Bar, Dark Chocolate Cinnamon Pecan Bar; [+15d w2h21] Pure Sparkling Water, Dark Chocolate Cinnamon Pecan Bar, Sparkling Water Grapefruit, Naturally Smoked Oysters in Pure Olive Oil. Next: +9d w4h10	Product: Bulgarian Yogurt. Aisle: yogurt. Department: dairy eggs.
  [w1h14] Tuna Ventresca, in Olive Oil, Lightly Smoked Sardines in Olive Oil, Naturally Smoked Oysters in Pure Olive Oil, Bulgarian Yogurt, Organic Whole String Cheese, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery; [+7d w1h10] Bulgarian Yogurt, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery, Organic Whole String Cheese, Banana, Plus Cranberry Almond + Antioxidants with Macadamia Nuts Bar, Dark Chocolate Cinnamon Pecan Bar; [+15d w2h21] Pure Sparkling Water, Dark Chocolate Cinnamon Pecan Bar, Sparkling Water Grapefruit, Naturally Smoked Oysters in Pure Olive Oil. Next: +9d w4h10	Product: Organic 4% Milk Fat Whole Milk Cottage Cheese. Aisle: other creams cheeses. Department: dairy eggs.
  [w1h14] Tuna Ventresca, in Olive Oil, Lightly Smoked Sardines in Olive Oil, Naturally Smoked Oysters in Pure Olive Oil, Bulgarian Yogurt, Organic Whole String Cheese, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery; [+7d w1h10] Bulgarian Yogurt, Organic 4% Milk Fat Whole Milk Cottage Cheese, Pure Sparkling Water, Organic Small Bunch Celery, Organic Whole String Cheese, Banana, Plus Cranberry Almond + Antioxidants with Macadamia Nuts Bar, Dark Chocolate Cinnamon Pecan Bar; [+15d w2h21] Pure Sparkling Water, Dark Chocolate Cinnamon Pecan Bar, Sparkling Water Grapefruit, Naturally Smoked Oysters in Pure Olive Oil. Next: +9d w4h10	Product: Organic Celery Hearts. Aisle: fresh vegetables. Department: produce.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 30.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 138,397 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 66 tokens mean: 150.77 tokens max: 203 tokens	min: 14 tokens mean: 20.47 tokens max: 44 tokens

• Samples:

  anchor	positive
  [w1h21] Large Alfresco Eggs, Organic Whole Milk, Organic Cream Cheese Bar, Organic Grape Tomatoes, Organic Raspberries, Organic Strawberries, Organic D'Anjou Pears, Organic Green Seedless Grapes, Small Hass Avocado, Organic Unsalted Diced Tomatoes, Pasta Sauce, Aged White Cheddar Gluten-Free Baked Rice And Corn Puffs, Crescent Rolls, Plain Greek Yogurt, Natural Free & Clear Dish Liquid, Organic Honey Sweet Whole Wheat Bread, Flaky Biscuits; [+13d w0h15] Strawberry Preserves, Organic Raspberries, Organic Frozen Peas. Next: +24d w0h21	Product: Organic Grape Tomatoes. Aisle: packaged vegetables fruits. Department: produce.
  [w1h21] Large Alfresco Eggs, Organic Whole Milk, Organic Cream Cheese Bar, Organic Grape Tomatoes, Organic Raspberries, Organic Strawberries, Organic D'Anjou Pears, Organic Green Seedless Grapes, Small Hass Avocado, Organic Unsalted Diced Tomatoes, Pasta Sauce, Aged White Cheddar Gluten-Free Baked Rice And Corn Puffs, Crescent Rolls, Plain Greek Yogurt, Natural Free & Clear Dish Liquid, Organic Honey Sweet Whole Wheat Bread, Flaky Biscuits; [+13d w0h15] Strawberry Preserves, Organic Raspberries, Organic Frozen Peas. Next: +24d w0h21	Product: Organic Strawberries. Aisle: fresh fruits. Department: produce.
  [w1h21] Large Alfresco Eggs, Organic Whole Milk, Organic Cream Cheese Bar, Organic Grape Tomatoes, Organic Raspberries, Organic Strawberries, Organic D'Anjou Pears, Organic Green Seedless Grapes, Small Hass Avocado, Organic Unsalted Diced Tomatoes, Pasta Sauce, Aged White Cheddar Gluten-Free Baked Rice And Corn Puffs, Crescent Rolls, Plain Greek Yogurt, Natural Free & Clear Dish Liquid, Organic Honey Sweet Whole Wheat Bread, Flaky Biscuits; [+13d w0h15] Strawberry Preserves, Organic Raspberries, Organic Frozen Peas. Next: +24d w0h21	Product: Organic Honey Sweet Whole Wheat Bread. Aisle: bread. Department: bakery.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 30.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 0.0001
• num_train_epochs: 5
• lr_scheduler_type: cosine
• warmup_steps: 9736
• dataloader_drop_last: True
• load_best_model_at_end: True
• dataloader_pin_memory: False
• gradient_checkpointing: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 0.0001
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 9736
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• 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
• 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: False
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• 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: True
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• 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
• use_cache: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	order-recommendation_cosine_ndcg@10
0.0051	100	5.0148	-	-
0.0103	200	4.2076	-	-
0.0154	300	3.9676	-	-
0.0205	400	3.8869	-	-
0.0257	500	3.8553	-	-
0.0308	600	3.8129	-	-
0.0359	700	3.8327	-	-
0.0411	800	3.7999	-	-
0.0462	900	3.7678	-	-
0.0514	1000	3.7722	-	-
0.0565	1100	3.7560	-	-
0.0616	1200	3.7471	-	-
0.0668	1300	3.7073	-	-
0.0719	1400	3.7077	-	-
0.0770	1500	3.7134	-	-
0.0822	1600	3.6892	-	-
0.0873	1700	3.7045	-	-
0.0924	1800	3.6850	-	-
0.0976	1900	3.6800	-	-
0.1027	2000	3.6838	-	-
0.1078	2100	3.6676	-	-
0.1130	2200	3.6377	-	-
0.1181	2300	3.6649	-	-
0.1233	2400	3.6375	-	-
0.1284	2500	3.6710	-	-
0.1335	2600	3.6716	-	-
0.1387	2700	3.6523	-	-
0.1438	2800	3.6643	-	-
0.1489	2900	3.6395	-	-
0.1541	3000	3.6263	-	-
0.1592	3100	3.6311	-	-
0.1643	3200	3.6382	-	-
0.1695	3300	3.6250	-	-
0.1746	3400	3.6121	-	-
0.1797	3500	3.6154	-	-
0.1849	3600	3.6034	-	-
0.1900	3700	3.5938	-	-
0.1952	3800	3.5910	-	-
0.2003	3900	3.5868	-	-
0.2054	4000	3.5717	-	-
0.2106	4100	3.5964	-	-
0.2157	4200	3.5636	-	-
0.2208	4300	3.5896	-	-
0.2260	4400	3.5937	-	-
0.2311	4500	3.5780	-	-
0.2362	4600	3.5769	-	-
0.2414	4700	3.5661	-	-
0.2465	4800	3.5671	-	-
0.2516	4900	3.5595	-	-
0.2568	5000	3.5742	-	-
0.2619	5100	3.5944	-	-
0.2671	5200	3.5448	-	-
0.2722	5300	3.5585	-	-
0.2773	5400	3.5530	-	-
0.2825	5500	3.5716	-	-
0.2876	5600	3.5650	-	-
0.2927	5700	3.5348	-	-
0.2979	5800	3.5276	-	-
0.3030	5900	3.5526	-	-
0.3081	6000	3.5418	-	-
0.3133	6100	3.5535	-	-
0.3184	6200	3.5499	-	-
0.3235	6300	3.5132	-	-
0.3287	6400	3.5250	-	-
0.3338	6500	3.5458	-	-
0.3389	6600	3.5398	-	-
0.3441	6700	3.5275	-	-
0.3492	6800	3.5267	-	-
0.3544	6900	3.5242	-	-
0.3595	7000	3.5357	-	-
0.3646	7100	3.5328	-	-
0.3698	7200	3.5254	-	-
0.3749	7300	3.5085	-	-
0.3800	7400	3.5071	-	-
0.3852	7500	3.5446	-	-
0.3903	7600	3.4990	-	-
0.3954	7700	3.5005	-	-
0.4006	7800	3.5334	-	-
0.4057	7900	3.4959	-	-
0.4108	8000	3.4916	-	-
0.4160	8100	3.5176	-	-
0.4211	8200	3.4992	-	-
0.4263	8300	3.4858	-	-
0.4314	8400	3.4768	-	-
0.4365	8500	3.5073	-	-
0.4417	8600	3.5054	-	-
0.4468	8700	3.5122	-	-
0.4519	8800	3.4864	-	-
0.4571	8900	3.4893	-	-
0.4622	9000	3.5090	-	-
0.4673	9100	3.4894	-	-
0.4725	9200	3.4848	-	-
0.4776	9300	3.4880	-	-
0.4827	9400	3.4988	-	-
0.4879	9500	3.4678	-	-
0.4930	9600	3.4685	-	-
0.4982	9700	3.4857	-	-
0.5033	9800	3.4722	-	-
0.5084	9900	3.4946	-	-
0.5136	10000	3.4759	-	-
0.5187	10100	3.4672	-	-
0.5238	10200	3.4608	-	-
0.5290	10300	3.4605	-	-
0.5341	10400	3.5044	-	-
0.5392	10500	3.4811	-	-
0.5444	10600	3.4761	-	-
0.5495	10700	3.4695	-	-
0.5546	10800	3.4497	-	-
0.5598	10900	3.4686	-	-
0.5649	11000	3.4562	-	-
0.5700	11100	3.4670	-	-
0.5752	11200	3.4465	-	-
0.5803	11300	3.4628	-	-
0.5855	11400	3.4663	-	-
0.5906	11500	3.4309	-	-
0.5957	11600	3.4253	-	-
0.6009	11700	3.4590	-	-
0.6060	11800	3.4404	-	-
0.6111	11900	3.4365	-	-
0.6163	12000	3.4287	-	-
0.6214	12100	3.4546	-	-
0.6265	12200	3.4464	-	-
0.6317	12300	3.4408	-	-
0.6368	12400	3.4256	-	-
0.6419	12500	3.4694	-	-
0.6471	12600	3.4362	-	-
0.6522	12700	3.4509	-	-
0.6574	12800	3.4353	-	-
0.6625	12900	3.3987	-	-
0.6676	13000	3.4355	-	-
0.6728	13100	3.4135	-	-
0.6779	13200	3.4300	-	-
0.6830	13300	3.4278	-	-
0.6882	13400	3.4348	-	-
0.6933	13500	3.3989	-	-
0.6984	13600	3.3901	-	-
0.7036	13700	3.3859	-	-
0.7087	13800	3.3795	-	-
0.7138	13900	3.4163	-	-
0.7190	14000	3.3952	-	-
0.7241	14100	3.4414	-	-
0.7293	14200	3.3959	-	-
0.7344	14300	3.4156	-	-
0.7395	14400	3.4057	-	-
0.7447	14500	3.4122	-	-
0.7498	14600	3.4097	-	-
0.7549	14700	3.3727	-	-
0.7601	14800	3.4042	-	-
0.7652	14900	3.4193	-	-
0.7703	15000	3.3870	-	-
0.7755	15100	3.3930	-	-
0.7806	15200	3.3864	-	-
0.7857	15300	3.3595	-	-
0.7909	15400	3.3897	-	-
0.7960	15500	3.3731	-	-
0.8012	15600	3.4054	-	-
0.8063	15700	3.3887	-	-
0.8114	15800	3.3804	-	-
0.8166	15900	3.4027	-	-
0.8217	16000	3.3777	-	-
0.8268	16100	3.3700	-	-
0.8320	16200	3.3772	-	-
0.8371	16300	3.4055	-	-
0.8422	16400	3.3798	-	-
0.8474	16500	3.3694	-	-
0.8525	16600	3.3790	-	-
0.8576	16700	3.3723	-	-
0.8628	16800	3.3572	-	-
0.8679	16900	3.3853	-	-
0.8730	17000	3.3825	-	-
0.8782	17100	3.3728	-	-
0.8833	17200	3.3861	-	-
0.8885	17300	3.3751	-	-
0.8936	17400	3.3733	-	-
0.8987	17500	3.3948	-	-
0.9039	17600	3.3633	-	-
0.9090	17700	3.3779	-	-
0.9141	17800	3.3379	-	-
0.9193	17900	3.3815	-	-
0.9244	18000	3.3530	-	-
0.9295	18100	3.3704	-	-
0.9347	18200	3.3629	-	-
0.9398	18300	3.3534	-	-
0.9449	18400	3.3752	-	-
0.9501	18500	3.3786	-	-
0.9552	18600	3.3924	-	-
0.9604	18700	3.3497	-	-
0.9655	18800	3.3699	-	-
0.9706	18900	3.3479	-	-
0.9758	19000	3.3650	-	-
0.9809	19100	3.3654	-	-
0.9860	19200	3.3505	-	-
0.9912	19300	3.3551	-	-
0.9963	19400	3.3448	-	-
1.0	19472	-	3.3238	0.1253
1.0014	19500	3.3456	-	-
1.0066	19600	3.2904	-	-
1.0117	19700	3.2984	-	-
1.0168	19800	3.2993	-	-
1.0220	19900	3.3182	-	-
1.0271	20000	3.2858	-	-
1.0323	20100	3.3119	-	-
1.0374	20200	3.2513	-	-
1.0425	20300	3.3119	-	-
1.0477	20400	3.3224	-	-
1.0528	20500	3.3038	-	-
1.0579	20600	3.3189	-	-
1.0631	20700	3.2923	-	-
1.0682	20800	3.2664	-	-
1.0733	20900	3.2768	-	-
1.0785	21000	3.3033	-	-
1.0836	21100	3.2736	-	-
1.0887	21200	3.2813	-	-
1.0939	21300	3.2621	-	-
1.0990	21400	3.3036	-	-
1.1041	21500	3.3002	-	-
1.1093	21600	3.3098	-	-
1.1144	21700	3.2922	-	-
1.1196	21800	3.3120	-	-
1.1247	21900	3.2995	-	-
1.1298	22000	3.2466	-	-
1.1350	22100	3.2837	-	-
1.1401	22200	3.2854	-	-
1.1452	22300	3.2977	-	-
1.1504	22400	3.3157	-	-
1.1555	22500	3.2911	-	-
1.1606	22600	3.3013	-	-
1.1658	22700	3.2650	-	-
1.1709	22800	3.3018	-	-
1.1760	22900	3.2954	-	-
1.1812	23000	3.2984	-	-
1.1863	23100	3.2657	-	-
1.1915	23200	3.2746	-	-
1.1966	23300	3.2955	-	-
1.2017	23400	3.2984	-	-
1.2069	23500	3.2963	-	-
1.2120	23600	3.2621	-	-
1.2171	23700	3.2896	-	-
1.2223	23800	3.2823	-	-
1.2274	23900	3.2628	-	-
1.2325	24000	3.2522	-	-
1.2377	24100	3.2873	-	-
1.2428	24200	3.2714	-	-
1.2479	24300	3.2671	-	-
1.2531	24400	3.3075	-	-
1.2582	24500	3.2701	-	-
1.2634	24600	3.2795	-	-
1.2685	24700	3.2776	-	-
1.2736	24800	3.3064	-	-
1.2788	24900	3.2660	-	-
1.2839	25000	3.2868	-	-
1.2890	25100	3.2537	-	-
1.2942	25200	3.2865	-	-
1.2993	25300	3.2926	-	-
1.3044	25400	3.2690	-	-
1.3096	25500	3.2797	-	-
1.3147	25600	3.2441	-	-
1.3198	25700	3.2865	-	-
1.3250	25800	3.2739	-	-
1.3301	25900	3.2890	-	-
1.3353	26000	3.2845	-	-
1.3404	26100	3.2483	-	-
1.3455	26200	3.2714	-	-
1.3507	26300	3.2964	-	-
1.3558	26400	3.2575	-	-
1.3609	26500	3.2860	-	-
1.3661	26600	3.2752	-	-
1.3712	26700	3.3032	-	-
1.3763	26800	3.3210	-	-
1.3815	26900	3.2540	-	-
1.3866	27000	3.2566	-	-
1.3917	27100	3.2279	-	-
1.3969	27200	3.2609	-	-
1.4020	27300	3.2498	-	-
1.4071	27400	3.2767	-	-
1.4123	27500	3.2352	-	-
1.4174	27600	3.2872	-	-
1.4226	27700	3.2349	-	-
1.4277	27800	3.2441	-	-
1.4328	27900	3.2458	-	-
1.4380	28000	3.2426	-	-
1.4431	28100	3.2654	-	-
1.4482	28200	3.2561	-	-
1.4534	28300	3.2515	-	-
1.4585	28400	3.2677	-	-
1.4636	28500	3.2615	-	-
1.4688	28600	3.2560	-	-
1.4739	28700	3.2492	-	-
1.4790	28800	3.2832	-	-
1.4842	28900	3.2639	-	-
1.4893	29000	3.2420	-	-
1.4945	29100	3.2438	-	-
1.4996	29200	3.2750	-	-
1.5047	29300	3.2428	-	-
1.5099	29400	3.2326	-	-
1.5150	29500	3.2852	-	-
1.5201	29600	3.2729	-	-
1.5253	29700	3.2521	-	-
1.5304	29800	3.2332	-	-
1.5355	29900	3.2784	-	-
1.5407	30000	3.2720	-	-
1.5458	30100	3.2340	-	-
1.5509	30200	3.3085	-	-
1.5561	30300	3.2340	-	-
1.5612	30400	3.2547	-	-
1.5664	30500	3.2922	-	-
1.5715	30600	3.2738	-	-
1.5766	30700	3.2546	-	-
1.5818	30800	3.2857	-	-
1.5869	30900	3.2614	-	-
1.5920	31000	3.2752	-	-
1.5972	31100	3.2455	-	-
1.6023	31200	3.2548	-	-
1.6074	31300	3.2418	-	-
1.6126	31400	3.2583	-	-
1.6177	31500	3.2870	-	-
1.6228	31600	3.2599	-	-
1.6280	31700	3.2516	-	-
1.6331	31800	3.2764	-	-
1.6382	31900	3.2377	-	-
1.6434	32000	3.2785	-	-
1.6485	32100	3.2706	-	-
1.6537	32200	3.2749	-	-
1.6588	32300	3.2830	-	-
1.6639	32400	3.2827	-	-
1.6691	32500	3.2464	-	-
1.6742	32600	3.2478	-	-
1.6793	32700	3.2653	-	-
1.6845	32800	3.2755	-	-
1.6896	32900	3.2443	-	-
1.6947	33000	3.2820	-	-
1.6999	33100	3.2538	-	-
1.7050	33200	3.2383	-	-
1.7101	33300	3.2530	-	-
1.7153	33400	3.2718	-	-
1.7204	33500	3.2586	-	-
1.7256	33600	3.2415	-	-
1.7307	33700	3.2396	-	-
1.7358	33800	3.2291	-	-
1.7410	33900	3.2298	-	-
1.7461	34000	3.2710	-	-
1.7512	34100	3.2587	-	-
1.7564	34200	3.2365	-	-
1.7615	34300	3.2471	-	-
1.7666	34400	3.2501	-	-
1.7718	34500	3.2258	-	-
1.7769	34600	3.2526	-	-
1.7820	34700	3.2337	-	-
1.7872	34800	3.2358	-	-
1.7923	34900	3.2506	-	-
1.7975	35000	3.2256	-	-
1.8026	35100	3.2459	-	-
1.8077	35200	3.2359	-	-
1.8129	35300	3.2682	-	-
1.8180	35400	3.2565	-	-
1.8231	35500	3.2289	-	-
1.8283	35600	3.2463	-	-
1.8334	35700	3.2471	-	-
1.8385	35800	3.2575	-	-
1.8437	35900	3.2530	-	-
1.8488	36000	3.2100	-	-
1.8539	36100	3.2687	-	-
1.8591	36200	3.2193	-	-
1.8642	36300	3.2408	-	-
1.8694	36400	3.2672	-	-
1.8745	36500	3.2109	-	-
1.8796	36600	3.2391	-	-
1.8848	36700	3.2574	-	-
1.8899	36800	3.2464	-	-
1.8950	36900	3.2318	-	-
1.9002	37000	3.2399	-	-
1.9053	37100	3.2121	-	-
1.9104	37200	3.2323	-	-
1.9156	37300	3.2276	-	-
1.9207	37400	3.1921	-	-
1.9258	37500	3.2060	-	-
1.9310	37600	3.2497	-	-
1.9361	37700	3.2246	-	-
1.9412	37800	3.2075	-	-
1.9464	37900	3.2400	-	-
1.9515	38000	3.2182	-	-
1.9567	38100	3.2270	-	-
1.9618	38200	3.2055	-	-
1.9669	38300	3.2271	-	-
1.9721	38400	3.1932	-	-
1.9772	38500	3.2215	-	-
1.9823	38600	3.2090	-	-
1.9875	38700	3.1988	-	-
1.9926	38800	3.2289	-	-
1.9977	38900	3.1708	-	-
2.0	38944	-	3.2276	0.1394
2.0029	39000	3.1882	-	-
2.0080	39100	3.1463	-	-
2.0131	39200	3.1377	-	-
2.0183	39300	3.1699	-	-
2.0234	39400	3.1728	-	-
2.0286	39500	3.1545	-	-
2.0337	39600	3.1297	-	-
2.0388	39700	3.1352	-	-
2.0440	39800	3.1369	-	-
2.0491	39900	3.1535	-	-
2.0542	40000	3.1272	-	-
2.0594	40100	3.1203	-	-
2.0645	40200	3.1267	-	-
2.0696	40300	3.1201	-	-
2.0748	40400	3.1348	-	-
2.0799	40500	3.1173	-	-
2.0850	40600	3.1569	-	-
2.0902	40700	3.1609	-	-
2.0953	40800	3.1360	-	-
2.1005	40900	3.1404	-	-
2.1056	41000	3.1431	-	-
2.1107	41100	3.1521	-	-
2.1159	41200	3.1307	-	-
2.1210	41300	3.1283	-	-
2.1261	41400	3.1620	-	-
2.1313	41500	3.1772	-	-
2.1364	41600	3.1068	-	-
2.1415	41700	3.1308	-	-
2.1467	41800	3.1363	-	-
2.1518	41900	3.1178	-	-
2.1569	42000	3.1257	-	-
2.1621	42100	3.1282	-	-
2.1672	42200	3.1398	-	-
2.1724	42300	3.1340	-	-
2.1775	42400	3.1337	-	-
2.1826	42500	3.1381	-	-
2.1878	42600	3.1170	-	-
2.1929	42700	3.1388	-	-
2.1980	42800	3.1249	-	-
2.2032	42900	3.1430	-	-
2.2083	43000	3.1322	-	-
2.2134	43100	3.1168	-	-
2.2186	43200	3.1664	-	-
2.2237	43300	3.1328	-	-
2.2288	43400	3.1495	-	-
2.2340	43500	3.1461	-	-
2.2391	43600	3.1469	-	-
2.2442	43700	3.1372	-	-
2.2494	43800	3.1412	-	-
2.2545	43900	3.1035	-	-
2.2597	44000	3.1214	-	-
2.2648	44100	3.1274	-	-
2.2699	44200	3.1499	-	-
2.2751	44300	3.1157	-	-
2.2802	44400	3.1371	-	-
2.2853	44500	3.1527	-	-
2.2905	44600	3.1207	-	-
2.2956	44700	3.1403	-	-
2.3007	44800	3.1411	-	-
2.3059	44900	3.1441	-	-
2.3110	45000	3.1332	-	-
2.3161	45100	3.0979	-	-
2.3213	45200	3.1598	-	-
2.3264	45300	3.1524	-	-
2.3316	45400	3.1501	-	-
2.3367	45500	3.1267	-	-
2.3418	45600	3.1263	-	-
2.3470	45700	3.1666	-	-
2.3521	45800	3.1086	-	-
2.3572	45900	3.1391	-	-
2.3624	46000	3.1315	-	-
2.3675	46100	3.1195	-	-
2.3726	46200	3.1664	-	-
2.3778	46300	3.1392	-	-
2.3829	46400	3.1575	-	-
2.3880	46500	3.1331	-	-
2.3932	46600	3.1081	-	-
2.3983	46700	3.1558	-	-
2.4035	46800	3.1225	-	-
2.4086	46900	3.1600	-	-
2.4137	47000	3.1143	-	-
2.4189	47100	3.1247	-	-
2.4240	47200	3.0973	-	-
2.4291	47300	3.1414	-	-
2.4343	47400	3.0867	-	-
2.4394	47500	3.0712	-	-
2.4445	47600	3.1147	-	-
2.4497	47700	3.1046	-	-
2.4548	47800	3.1277	-	-
2.4599	47900	3.1144	-	-
2.4651	48000	3.1495	-	-
2.4702	48100	3.1383	-	-
2.4753	48200	3.1464	-	-
2.4805	48300	3.1404	-	-
2.4856	48400	3.1418	-	-
2.4908	48500	3.1606	-	-
2.4959	48600	3.1284	-	-
2.5010	48700	3.1179	-	-
2.5062	48800	3.1250	-	-
2.5113	48900	3.1278	-	-
2.5164	49000	3.1039	-	-
2.5216	49100	3.1184	-	-
2.5267	49200	3.1054	-	-
2.5318	49300	3.1040	-	-
2.5370	49400	3.1288	-	-
2.5421	49500	3.1336	-	-
2.5472	49600	3.1549	-	-
2.5524	49700	3.1267	-	-
2.5575	49800	3.1436	-	-
2.5627	49900	3.1425	-	-
2.5678	50000	3.1311	-	-
2.5729	50100	3.1286	-	-
2.5781	50200	3.1323	-	-
2.5832	50300	3.1376	-	-
2.5883	50400	3.1169	-	-
2.5935	50500	3.1381	-	-
2.5986	50600	3.1360	-	-
2.6037	50700	3.0985	-	-
2.6089	50800	3.1348	-	-
2.6140	50900	3.1055	-	-
2.6191	51000	3.1187	-	-
2.6243	51100	3.0934	-	-
2.6294	51200	3.1354	-	-
2.6346	51300	3.1180	-	-
2.6397	51400	3.1117	-	-
2.6448	51500	3.1131	-	-
2.6500	51600	3.1244	-	-
2.6551	51700	3.1432	-	-
2.6602	51800	3.0923	-	-
2.6654	51900	3.1306	-	-
2.6705	52000	3.1142	-	-
2.6756	52100	3.1271	-	-
2.6808	52200	3.1134	-	-
2.6859	52300	3.1034	-	-
2.6910	52400	3.0792	-	-
2.6962	52500	3.1257	-	-
2.7013	52600	3.1233	-	-
2.7065	52700	3.1243	-	-
2.7116	52800	3.1261	-	-
2.7167	52900	3.1526	-	-
2.7219	53000	3.1170	-	-
2.7270	53100	3.1009	-	-
2.7321	53200	3.0747	-	-
2.7373	53300	3.1165	-	-
2.7424	53400	3.1110	-	-
2.7475	53500	3.0896	-	-
2.7527	53600	3.1018	-	-
2.7578	53700	3.1117	-	-
2.7629	53800	3.0938	-	-
2.7681	53900	3.1162	-	-
2.7732	54000	3.1149	-	-
2.7783	54100	3.1169	-	-
2.7835	54200	3.1151	-	-
2.7886	54300	3.1384	-	-
2.7938	54400	3.1014	-	-
2.7989	54500	3.1371	-	-
2.8040	54600	3.1048	-	-
2.8092	54700	3.1017	-	-
2.8143	54800	3.0951	-	-
2.8194	54900	3.1053	-	-
2.8246	55000	3.0709	-	-
2.8297	55100	3.1129	-	-
2.8348	55200	3.0956	-	-
2.8400	55300	3.1415	-	-
2.8451	55400	3.1124	-	-
2.8502	55500	3.1001	-	-
2.8554	55600	3.1247	-	-
2.8605	55700	3.1019	-	-
2.8657	55800	3.0833	-	-
2.8708	55900	3.1085	-	-
2.8759	56000	3.1009	-	-
2.8811	56100	3.1342	-	-
2.8862	56200	3.1057	-	-
2.8913	56300	3.1073	-	-
2.8965	56400	3.0886	-	-
2.9016	56500	3.1292	-	-
2.9067	56600	3.1067	-	-
2.9119	56700	3.1106	-	-
2.9170	56800	3.1288	-	-
2.9221	56900	3.1290	-	-
2.9273	57000	3.1053	-	-
2.9324	57100	3.1001	-	-
2.9376	57200	3.1055	-	-
2.9427	57300	3.1210	-	-
2.9478	57400	3.1053	-	-
2.9530	57500	3.0862	-	-
2.9581	57600	3.0945	-	-
2.9632	57700	3.0870	-	-
2.9684	57800	3.1169	-	-
2.9735	57900	3.0935	-	-
2.9786	58000	3.0855	-	-
2.9838	58100	3.0938	-	-
2.9889	58200	3.0877	-	-
2.9940	58300	3.0870	-	-
2.9992	58400	3.1194	-	-
2.9999	58415	-	3.1599	0.1504
3.0044	58500	3.0355	-	-
3.0095	58600	3.0071	-	-
3.0146	58700	3.0568	-	-
3.0198	58800	3.0307	-	-
3.0249	58900	3.0245	-	-
3.0300	59000	3.0135	-	-
3.0352	59100	3.0028	-	-
3.0403	59200	3.0177	-	-
3.0454	59300	3.0060	-	-
3.0506	59400	3.0153	-	-
3.0557	59500	3.0061	-	-
3.0609	59600	2.9978	-	-
3.0660	59700	3.0310	-	-
3.0711	59800	2.9871	-	-
3.0763	59900	2.9926	-	-
3.0814	60000	3.0069	-	-
3.0865	60100	2.9860	-	-
3.0917	60200	3.0367	-	-
3.0968	60300	2.9840	-	-
3.1019	60400	3.0605	-	-
3.1071	60500	3.0183	-	-
3.1122	60600	3.0009	-	-
3.1173	60700	3.0382	-	-
3.1225	60800	3.0109	-	-
3.1276	60900	3.0043	-	-
3.1328	61000	3.0259	-	-
3.1379	61100	2.9948	-	-
3.1430	61200	3.0071	-	-
3.1482	61300	3.0309	-	-
3.1533	61400	3.0244	-	-
3.1584	61500	2.9854	-	-
3.1636	61600	3.0300	-	-
3.1687	61700	3.0025	-	-
3.1738	61800	3.0198	-	-
3.1790	61900	3.0151	-	-
3.1841	62000	2.9979	-	-
3.1892	62100	2.9986	-	-
3.1944	62200	2.9793	-	-
3.1995	62300	2.9911	-	-
3.2047	62400	3.0060	-	-
3.2098	62500	3.0162	-	-
3.2149	62600	3.0157	-	-
3.2201	62700	3.0084	-	-
3.2252	62800	3.0235	-	-
3.2303	62900	3.0048	-	-
3.2355	63000	2.9878	-	-
3.2406	63100	3.0091	-	-
3.2457	63200	3.0393	-	-
3.2509	63300	2.9866	-	-
3.2560	63400	2.9945	-	-
3.2611	63500	2.9931	-	-
3.2663	63600	3.0062	-	-
3.2714	63700	2.9868	-	-
3.2766	63800	3.0139	-	-
3.2817	63900	3.0233	-	-
3.2868	64000	3.0038	-	-
3.2920	64100	3.0069	-	-
3.2971	64200	3.0062	-	-
3.3022	64300	3.0103	-	-
3.3074	64400	3.0354	-	-
3.3125	64500	3.0273	-	-
3.3176	64600	3.0110	-	-
3.3228	64700	3.0210	-	-
3.3279	64800	3.0067	-	-
3.3330	64900	3.0129	-	-
3.3382	65000	3.0292	-	-
3.3433	65100	3.0552	-	-
3.3484	65200	3.0094	-	-
3.3536	65300	2.9867	-	-
3.3587	65400	3.0159	-	-
3.3639	65500	3.0094	-	-
3.3690	65600	3.0060	-	-
3.3741	65700	3.0094	-	-
3.3793	65800	2.9979	-	-
3.3844	65900	3.0337	-	-
3.3895	66000	3.0645	-	-
3.3947	66100	3.0293	-	-
3.3998	66200	2.9948	-	-
3.4049	66300	2.9735	-	-
3.4101	66400	3.0049	-	-
3.4152	66500	3.0134	-	-
3.4203	66600	2.9973	-	-
3.4255	66700	2.9915	-	-
3.4306	66800	2.9951	-	-
3.4358	66900	3.0064	-	-
3.4409	67000	3.0053	-	-
3.4460	67100	2.9910	-	-
3.4512	67200	3.0080	-	-
3.4563	67300	2.9947	-	-
3.4614	67400	3.0153	-	-
3.4666	67500	3.0014	-	-
3.4717	67600	2.9971	-	-
3.4768	67700	2.9759	-	-
3.4820	67800	3.0092	-	-
3.4871	67900	3.0095	-	-
3.4922	68000	3.0009	-	-
3.4974	68100	3.0234	-	-
3.5025	68200	2.9999	-	-
3.5077	68300	2.9870	-	-
3.5128	68400	3.0004	-	-
3.5179	68500	2.9838	-	-
3.5231	68600	3.0328	-	-
3.5282	68700	3.0074	-	-
3.5333	68800	2.9899	-	-
3.5385	68900	2.9713	-	-
3.5436	69000	3.0444	-	-
3.5487	69100	3.0027	-	-
3.5539	69200	2.9755	-	-
3.5590	69300	2.9821	-	-
3.5641	69400	2.9833	-	-
3.5693	69500	2.9969	-	-
3.5744	69600	2.9865	-	-
3.5796	69700	3.0079	-	-
3.5847	69800	2.9652	-	-
3.5898	69900	3.0098	-	-
3.5950	70000	2.9989	-	-
3.6001	70100	3.0185	-	-
3.6052	70200	3.0113	-	-
3.6104	70300	2.9871	-	-
3.6155	70400	3.0280	-	-
3.6206	70500	3.0122	-	-
3.6258	70600	2.9845	-	-
3.6309	70700	2.9969	-	-
3.6360	70800	2.9938	-	-
3.6412	70900	3.0023	-	-
3.6463	71000	2.9884	-	-
3.6514	71100	3.0220	-	-
3.6566	71200	3.0320	-	-
3.6617	71300	2.9969	-	-
3.6669	71400	2.9875	-	-
3.6720	71500	2.9873	-	-
3.6771	71600	2.9891	-	-
3.6823	71700	3.0213	-	-
3.6874	71800	2.9963	-	-
3.6925	71900	3.0188	-	-
3.6977	72000	3.0422	-	-
3.7028	72100	2.9617	-	-
3.7079	72200	3.0014	-	-
3.7131	72300	3.0026	-	-
3.7182	72400	3.0054	-	-
3.7233	72500	2.9784	-	-
3.7285	72600	2.9841	-	-
3.7336	72700	2.9781	-	-
3.7388	72800	2.9717	-	-
3.7439	72900	3.0019	-	-
3.7490	73000	2.9659	-	-
3.7542	73100	2.9822	-	-
3.7593	73200	2.9559	-	-
3.7644	73300	3.0205	-	-
3.7696	73400	3.0101	-	-
3.7747	73500	2.9759	-	-
3.7798	73600	2.9652	-	-
3.7850	73700	2.9964	-	-
3.7901	73800	2.9949	-	-
3.7952	73900	2.9637	-	-
3.8004	74000	3.0252	-	-
3.8055	74100	2.9950	-	-
3.8107	74200	2.9935	-	-
3.8158	74300	3.0032	-	-
3.8209	74400	2.9971	-	-
3.8261	74500	3.005	-	-
3.8312	74600	2.9988	-	-
3.8363	74700	3.0106	-	-
3.8415	74800	3.0076	-	-
3.8466	74900	3.0007	-	-
3.8517	75000	2.9654	-	-
3.8569	75100	2.9584	-	-
3.8620	75200	2.9984	-	-
3.8671	75300	3.0095	-	-
3.8723	75400	2.9818	-	-
3.8774	75500	2.9658	-	-
3.8825	75600	3.0216	-	-
3.8877	75700	2.9711	-	-
3.8928	75800	3.0085	-	-
3.8980	75900	2.9589	-	-
3.9031	76000	2.9581	-	-
3.9082	76100	2.9963	-	-
3.9134	76200	2.9921	-	-
3.9185	76300	2.9848	-	-
3.9236	76400	2.9619	-	-
3.9288	76500	2.9812	-	-
3.9339	76600	2.9768	-	-
3.9390	76700	3.0026	-	-
3.9442	76800	3.0018	-	-
3.9493	76900	2.9838	-	-
3.9544	77000	2.9626	-	-
3.9596	77100	2.9895	-	-
3.9647	77200	3.0085	-	-
3.9699	77300	2.9911	-	-
3.9750	77400	2.9567	-	-
3.9801	77500	2.9594	-	-
3.9853	77600	2.9889	-	-
3.9904	77700	2.9806	-	-
3.9955	77800	2.9980	-	-
4.0	77887	-	3.1425	0.1532
4.0007	77900	2.9742	-	-
4.0058	78000	2.9137	-	-
4.0109	78100	2.9268	-	-
4.0161	78200	2.8930	-	-
4.0212	78300	2.9060	-	-
4.0263	78400	2.9186	-	-
4.0315	78500	2.8942	-	-
4.0366	78600	2.9795	-	-
4.0418	78700	2.9352	-	-
4.0469	78800	2.9247	-	-
4.0520	78900	2.9100	-	-
4.0572	79000	2.9573	-	-
4.0623	79100	2.9422	-	-
4.0674	79200	2.9448	-	-
4.0726	79300	2.9164	-	-
4.0777	79400	2.9256	-	-
4.0828	79500	2.9139	-	-
4.0880	79600	2.9387	-	-
4.0931	79700	2.9244	-	-
4.0982	79800	2.9316	-	-
4.1034	79900	2.9536	-	-
4.1085	80000	2.9186	-	-
4.1137	80100	2.9319	-	-
4.1188	80200	2.9549	-	-
4.1239	80300	2.9059	-	-
4.1291	80400	2.9380	-	-
4.1342	80500	2.9053	-	-
4.1393	80600	2.8942	-	-
4.1445	80700	2.9154	-	-
4.1496	80800	2.9192	-	-
4.1547	80900	2.9226	-	-
4.1599	81000	2.8921	-	-
4.1650	81100	2.9286	-	-
4.1701	81200	2.9093	-	-
4.1753	81300	2.9504	-	-
4.1804	81400	2.9074	-	-
4.1855	81500	2.9062	-	-
4.1907	81600	2.8998	-	-
4.1958	81700	2.9382	-	-
4.2010	81800	2.9189	-	-
4.2061	81900	2.9008	-	-
4.2112	82000	2.9246	-	-
4.2164	82100	2.9075	-	-
4.2215	82200	2.8957	-	-
4.2266	82300	2.9181	-	-
4.2318	82400	2.9345	-	-
4.2369	82500	2.9494	-	-
4.2420	82600	2.9156	-	-
4.2472	82700	2.9210	-	-
4.2523	82800	2.9478	-	-
4.2574	82900	2.9217	-	-
4.2626	83000	2.9631	-	-
4.2677	83100	2.8982	-	-
4.2729	83200	2.9079	-	-
4.2780	83300	2.9569	-	-
4.2831	83400	2.9178	-	-
4.2883	83500	2.9169	-	-
4.2934	83600	2.9307	-	-
4.2985	83700	2.9262	-	-
4.3037	83800	2.9356	-	-
4.3088	83900	2.9257	-	-
4.3139	84000	2.9341	-	-
4.3191	84100	2.9504	-	-
4.3242	84200	2.9221	-	-
4.3293	84300	2.8998	-	-
4.3345	84400	2.9109	-	-
4.3396	84500	2.8854	-	-
4.3448	84600	2.9162	-	-
4.3499	84700	2.8912	-	-
4.3550	84800	2.9326	-	-
4.3602	84900	2.9347	-	-
4.3653	85000	2.8955	-	-
4.3704	85100	2.9036	-	-
4.3756	85200	2.9321	-	-
4.3807	85300	2.9125	-	-
4.3858	85400	2.9583	-	-
4.3910	85500	2.9275	-	-
4.3961	85600	2.8883	-	-
4.4012	85700	2.9447	-	-
4.4064	85800	2.9215	-	-
4.4115	85900	2.9008	-	-
4.4166	86000	2.8996	-	-
4.4218	86100	2.9417	-	-
4.4269	86200	2.8917	-	-
4.4321	86300	2.8983	-	-
4.4372	86400	2.9119	-	-
4.4423	86500	2.9104	-	-
4.4475	86600	2.9267	-	-
4.4526	86700	2.9262	-	-
4.4577	86800	2.9254	-	-
4.4629	86900	2.9265	-	-
4.4680	87000	2.9411	-	-
4.4731	87100	2.8932	-	-
4.4783	87200	2.9457	-	-
4.4834	87300	2.9185	-	-
4.4885	87400	2.9180	-	-
4.4937	87500	2.9064	-	-
4.4988	87600	2.8989	-	-
4.5040	87700	2.9299	-	-
4.5091	87800	2.9528	-	-
4.5142	87900	2.9076	-	-
4.5194	88000	2.9399	-	-
4.5245	88100	2.9620	-	-
4.5296	88200	2.9148	-	-
4.5348	88300	2.9416	-	-
4.5399	88400	2.9340	-	-
4.5450	88500	2.9021	-	-
4.5502	88600	2.8962	-	-
4.5553	88700	2.9384	-	-
4.5604	88800	2.8830	-	-
4.5656	88900	2.8832	-	-
4.5707	89000	2.9307	-	-
4.5759	89100	2.8887	-	-
4.5810	89200	2.9310	-	-
4.5861	89300	2.9370	-	-
4.5913	89400	2.8833	-	-
4.5964	89500	2.9124	-	-
4.6015	89600	2.9274	-	-
4.6067	89700	2.9302	-	-
4.6118	89800	2.9285	-	-
4.6169	89900	2.9199	-	-
4.6221	90000	2.9214	-	-
4.6272	90100	2.8718	-	-
4.6323	90200	2.9027	-	-
4.6375	90300	2.9045	-	-
4.6426	90400	2.9310	-	-
4.6478	90500	2.8972	-	-
4.6529	90600	2.9554	-	-
4.6580	90700	2.9405	-	-
4.6632	90800	2.9354	-	-
4.6683	90900	2.9335	-	-
4.6734	91000	2.9067	-	-
4.6786	91100	2.8963	-	-
4.6837	91200	2.9162	-	-
4.6888	91300	2.9430	-	-
4.6940	91400	2.9054	-	-
4.6991	91500	2.9236	-	-
4.7042	91600	2.9107	-	-
4.7094	91700	2.9085	-	-
4.7145	91800	2.9404	-	-
4.7196	91900	2.9219	-	-
4.7248	92000	2.9392	-	-
4.7299	92100	2.9215	-	-
4.7351	92200	2.8988	-	-
4.7402	92300	2.9256	-	-
4.7453	92400	2.9119	-	-
4.7505	92500	2.9435	-	-
4.7556	92600	2.8680	-	-
4.7607	92700	2.9324	-	-
4.7659	92800	2.9114	-	-
4.7710	92900	2.9083	-	-
4.7761	93000	2.8909	-	-
4.7813	93100	2.8947	-	-
4.7864	93200	2.9211	-	-
4.7915	93300	2.9227	-	-
4.7967	93400	2.9322	-	-
4.8018	93500	2.8943	-	-
4.8070	93600	2.9422	-	-
4.8121	93700	2.9318	-	-
4.8172	93800	2.9047	-	-
4.8224	93900	2.9241	-	-
4.8275	94000	2.9452	-	-
4.8326	94100	2.8805	-	-
4.8378	94200	2.9399	-	-
4.8429	94300	2.9311	-	-
4.8480	94400	2.9288	-	-
4.8532	94500	2.9159	-	-
4.8583	94600	2.8819	-	-
4.8634	94700	2.9181	-	-
4.8686	94800	2.9371	-	-
4.8737	94900	2.8987	-	-
4.8789	95000	2.9366	-	-
4.8840	95100	2.9122	-	-
4.8891	95200	2.8947	-	-
4.8943	95300	2.9010	-	-
4.8994	95400	2.9216	-	-
4.9045	95500	2.9147	-	-
4.9097	95600	2.9253	-	-
4.9148	95700	2.8845	-	-
4.9199	95800	2.9024	-	-
4.9251	95900	2.8893	-	-
4.9302	96000	2.9173	-	-
4.9353	96100	2.9090	-	-
4.9405	96200	2.9066	-	-
4.9456	96300	2.8933	-	-
4.9507	96400	2.9325	-	-
4.9559	96500	2.9075	-	-
4.9610	96600	2.9066	-	-
4.9662	96700	2.9076	-	-
4.9713	96800	2.8993	-	-
4.9764	96900	2.9094	-	-
4.9816	97000	2.9136	-	-
4.9867	97100	2.9575	-	-
4.9918	97200	2.9087	-	-
4.9970	97300	2.9094	-	-
5.0	97359	-	3.1542	0.1511

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.2
• Transformers: 5.1.0
• PyTorch: 2.10.0
• Accelerate: 1.12.0
• Datasets: 4.5.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}