pankajrajdeo/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3-MSMARCO-Epoch-5

SentenceTransformer

This is a sentence-transformers model trained on the parquet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • parquet

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': 1024, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("pankajrajdeo/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3-MSMARCO-Epoch-5")
# Run inference
sentences = [
    'does the columbus zoo sell beer',
    'No glass and/or alcohol are permitted at the Columbus Zoo. This means that they do not sell alcoholic beverages.',
    'Eviction law allows landlords to still ask you to move out, but you must be afforded some extra protections. First, for eviction notices without cause, the landlord must give you a longer period of notice to vacate, generally 30 or 60 days. This lengthened time period is designed to allow you to find another place to live.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

parquet

• Dataset: parquet
• Size: 39,780,704 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 4 tokens mean: 9.85 tokens max: 38 tokens	min: 15 tokens mean: 87.54 tokens max: 246 tokens

• Samples:

  anchor	positive
  is a little caffeine ok during pregnancy	We don’t know a lot about the effects of caffeine during pregnancy on you and your baby. So it’s best to limit the amount you get each day. If you’re pregnant, limit caffeine to 200 milligrams each day. This is about the amount in 1½ 8-ounce cups of coffee or one 12-ounce cup of coffee.
  what fruit is native to australia	Passiflora herbertiana. A rare passion fruit native to Australia. Fruits are green-skinned, white fleshed, with an unknown edible rating. Some sources list the fruit as edible, sweet and tasty, while others list the fruits as being bitter and inedible.assiflora herbertiana. A rare passion fruit native to Australia. Fruits are green-skinned, white fleshed, with an unknown edible rating. Some sources list the fruit as edible, sweet and tasty, while others list the fruits as being bitter and inedible.
  how large is the canadian military	The Canadian Armed Forces. 1 The first large-scale Canadian peacekeeping mission started in Egypt on November 24, 1956. 2 There are approximately 65,000 Regular Force and 25,000 reservist members in the Canadian military. 3 In Canada, August 9 is designated as National Peacekeepers’ Day.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

parquet

• Dataset: parquet
• Size: 39,780,704 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 4 tokens mean: 9.97 tokens max: 28 tokens	min: 28 tokens mean: 85.19 tokens max: 228 tokens

• Samples:

  anchor	positive
  chemical weathering definition	Chemical weathering is the process where rocks and minerals, which originally formed deep underground at much higher temperatures and pressures, gradually transform into different chemical compounds once they are exposed to air and water at the surface.
  what is the difference between breathe and breath	• The word breath is used as noun. • On the other hand, the word breathe is used as verb. This is the main difference between the two words. • The word breath is used in the sense of ‘air taken in and out during breathing’. • On the other hand, the word breathe is used in the sense of ‘take air into the lungs and then let it out’. • The word breathe is sometimes used with the expression ‘his/her last’, and it gives the meaning of ‘die.’ This is used for both breath and breathe. His last breath, breathed her last.
  what is natural neck tightening	Use Sunscreen: One of the best, and a natural method for tightening skin includes applying sunscreen on the face and neck area. This will help to protect against UV rays that can be harmful and help to prevent the premature aging of your skin.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 5
• max_steps: 1476235
• log_level: info
• fp16: True
• dataloader_num_workers: 16
• load_best_model_at_end: True
• resume_from_checkpoint: 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: 128
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 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: 5
• max_steps: 1476235
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: info
• 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: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 16
• 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: True
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.739	-
0.0034	1000	0.3494	-
0.0068	2000	0.2813	-
0.0102	3000	0.22	-
0.0135	4000	0.1988	-
0.0169	5000	0.1842	-
0.0203	6000	0.1848	-
0.0237	7000	0.1719	-
0.0271	8000	0.1778	-
0.0305	9000	0.1742	-
0.0339	10000	0.1414	-
0.0373	11000	0.1648	-
0.0406	12000	0.1479	-
0.0440	13000	0.1383	-
0.0474	14000	0.1361	-
0.0508	15000	0.1337	-
0.0542	16000	0.1355	-
0.0576	17000	0.1345	-
0.0610	18000	0.1253	-
0.0644	19000	0.1211	-
0.0677	20000	0.1161	-
0.0711	21000	0.1223	-
0.0745	22000	0.1169	-
0.0779	23000	0.1137	-
0.0813	24000	0.1273	-
0.0847	25000	0.1229	-
0.0881	26000	0.1159	-
0.0914	27000	0.0974	-
0.0948	28000	0.0976	-
0.0982	29000	0.1079	-
0.1016	30000	0.105	-
0.1050	31000	0.0977	-
0.1084	32000	0.0941	-
0.1118	33000	0.1124	-
0.1152	34000	0.0994	-
0.1185	35000	0.0979	-
0.1219	36000	0.1038	-
0.1253	37000	0.0877	-
0.1287	38000	0.0862	-
0.1321	39000	0.0928	-
0.1355	40000	0.0907	-
0.1389	41000	0.0942	-
0.1423	42000	0.0878	-
0.1456	43000	0.088	-
0.1490	44000	0.0851	-
0.1524	45000	0.0742	-
0.1558	46000	0.0856	-
0.1592	47000	0.0804	-
0.1626	48000	0.081	-
0.1660	49000	0.0622	-
0.1693	50000	0.087	-
0.1727	51000	0.0785	-
0.1761	52000	0.0803	-
0.1795	53000	0.0709	-
0.1829	54000	0.0871	-
0.1863	55000	0.0728	-
0.1897	56000	0.0799	-
0.1931	57000	0.0703	-
0.1964	58000	0.0765	-
0.1998	59000	0.0814	-
0.2032	60000	0.0745	-
0.2066	61000	0.0712	-
0.2100	62000	0.0746	-
0.2134	63000	0.0723	-
0.2168	64000	0.0578	-
0.2202	65000	0.0809	-
0.2235	66000	0.0745	-
0.2269	67000	0.0709	-
0.2303	68000	0.0697	-
0.2337	69000	0.0807	-
0.2371	70000	0.0725	-
0.2405	71000	0.0696	-
0.2439	72000	0.0677	-
0.2472	73000	0.0649	-
0.2506	74000	0.0594	-
0.2540	75000	0.0661	-
0.2574	76000	0.0688	-
0.2608	77000	0.0629	-
0.2642	78000	0.0671	-
0.2676	79000	0.0569	-
0.2710	80000	0.0551	-
0.2743	81000	0.0588	-
0.2777	82000	0.0566	-
0.2811	83000	0.0554	-
0.2845	84000	0.0557	-
0.2879	85000	0.0543	-
0.2913	86000	0.0594	-
0.2947	87000	0.0506	-
0.2981	88000	0.0643	-
0.3014	89000	0.0582	-
0.3048	90000	0.0558	-
0.3082	91000	0.0615	-
0.3116	92000	0.0555	-
0.3150	93000	0.0537	-
0.3184	94000	0.0606	-
0.3218	95000	0.059	-
0.3252	96000	0.0599	-
0.3285	97000	0.0525	-
0.3319	98000	0.0595	-
0.3353	99000	0.0514	-
0.3387	100000	0.055	-
0.3421	101000	0.0551	-
0.3455	102000	0.0586	-
0.3489	103000	0.0507	-
0.3522	104000	0.0494	-
0.3556	105000	0.0546	-
0.3590	106000	0.0572	-
0.3624	107000	0.0594	-
0.3658	108000	0.0553	-
0.3692	109000	0.0533	-
0.3726	110000	0.0489	-
0.3760	111000	0.0512	-
0.3793	112000	0.0516	-
0.3827	113000	0.0538	-
0.3861	114000	0.0459	-
0.3895	115000	0.0523	-
0.3929	116000	0.0464	-
0.3963	117000	0.0546	-
0.3997	118000	0.0552	-
0.4031	119000	0.0546	-
0.4064	120000	0.05	-
0.4098	121000	0.0469	-
0.4132	122000	0.0485	-
0.4166	123000	0.0534	-
0.4200	124000	0.0525	-
0.4234	125000	0.0493	-
0.4268	126000	0.0453	-
0.4301	127000	0.0491	-
0.4335	128000	0.0535	-
0.4369	129000	0.0512	-
0.4403	130000	0.0447	-
0.4437	131000	0.0484	-
0.4471	132000	0.0407	-
0.4505	133000	0.0466	-
0.4539	134000	0.0414	-
0.4572	135000	0.0483	-
0.4606	136000	0.0465	-
0.4640	137000	0.0493	-
0.4674	138000	0.0535	-
0.4708	139000	0.0422	-
0.4742	140000	0.047	-
0.4776	141000	0.0469	-
0.4810	142000	0.0498	-
0.4843	143000	0.0448	-
0.4877	144000	0.0407	-
0.4911	145000	0.0445	-
0.4945	146000	0.0455	-
0.4979	147000	0.0495	-
0.5013	148000	0.0452	-
0.5047	149000	0.0442	-
0.5080	150000	0.0443	-
0.5114	151000	0.05	-
0.5148	152000	0.0477	-
0.5182	153000	0.0441	-
0.5216	154000	0.0388	-
0.5250	155000	0.0432	-
0.5284	156000	0.0413	-
0.5318	157000	0.0459	-
0.5351	158000	0.0401	-
0.5385	159000	0.0426	-
0.5419	160000	0.0398	-
0.5453	161000	0.0441	-
0.5487	162000	0.0408	-
0.5521	163000	0.0384	-
0.5555	164000	0.042	-
0.5589	165000	0.0406	-
0.5622	166000	0.0406	-
0.5656	167000	0.0475	-
0.5690	168000	0.0407	-
0.5724	169000	0.0384	-
0.5758	170000	0.0379	-
0.5792	171000	0.0413	-
0.5826	172000	0.0456	-
0.5859	173000	0.0408	-
0.5893	174000	0.0369	-
0.5927	175000	0.0419	-
0.5961	176000	0.0394	-
0.5995	177000	0.0426	-
0.6029	178000	0.0425	-
0.6063	179000	0.0368	-
0.6097	180000	0.0498	-
0.6130	181000	0.039	-
0.6164	182000	0.0432	-
0.6198	183000	0.0369	-
0.6232	184000	0.0374	-
0.6266	185000	0.0443	-
0.6300	186000	0.0393	-
0.6334	187000	0.0413	-
0.6368	188000	0.0393	-
0.6401	189000	0.0443	-
0.6435	190000	0.0415	-
0.6469	191000	0.037	-
0.6503	192000	0.0322	-
0.6537	193000	0.0403	-
0.6571	194000	0.0405	-
0.6605	195000	0.0441	-
0.6638	196000	0.0356	-
0.6672	197000	0.0406	-
0.6706	198000	0.0393	-
0.6740	199000	0.0384	-
0.6774	200000	0.0377	-
0.6808	201000	0.0388	-
0.6842	202000	0.0346	-
0.6876	203000	0.0418	-
0.6909	204000	0.0349	-
0.6943	205000	0.0365	-
0.6977	206000	0.0425	-
0.7011	207000	0.0386	-
0.7045	208000	0.0336	-
0.7079	209000	0.0317	-
0.7113	210000	0.0377	-
0.7147	211000	0.0376	-
0.7180	212000	0.0393	-
0.7214	213000	0.0368	-
0.7248	214000	0.0364	-
0.7282	215000	0.0319	-
0.7316	216000	0.0332	-
0.7350	217000	0.0341	-
0.7384	218000	0.0337	-
0.7417	219000	0.0372	-
0.7451	220000	0.0352	-
0.7485	221000	0.0347	-
0.7519	222000	0.0343	-
0.7553	223000	0.0362	-
0.7587	224000	0.0315	-
0.7621	225000	0.0361	-
0.7655	226000	0.0394	-
0.7688	227000	0.0297	-
0.7722	228000	0.0379	-
0.7756	229000	0.0377	-
0.7790	230000	0.0303	-
0.7824	231000	0.0361	-
0.7858	232000	0.0338	-
0.7892	233000	0.0345	-
0.7926	234000	0.037	-
0.7959	235000	0.0344	-
0.7993	236000	0.0341	-
0.8027	237000	0.035	-
0.8061	238000	0.0311	-
0.8095	239000	0.0329	-
0.8129	240000	0.0335	-
0.8163	241000	0.0312	-
0.8196	242000	0.0323	-
0.8230	243000	0.0377	-
0.8264	244000	0.0338	-
0.8298	245000	0.0362	-
0.8332	246000	0.0319	-
0.8366	247000	0.0303	-
0.8400	248000	0.0328	-
0.8434	249000	0.0318	-
0.8467	250000	0.0348	-
0.8501	251000	0.0345	-
0.8535	252000	0.0359	-
0.8569	253000	0.0282	-
0.8603	254000	0.0295	-
0.8637	255000	0.0338	-
0.8671	256000	0.028	-
0.8705	257000	0.0367	-
0.8738	258000	0.0327	-
0.8772	259000	0.035	-
0.8806	260000	0.0295	-
0.8840	261000	0.036	-
0.8874	262000	0.0308	-
0.8908	263000	0.0321	-
0.8942	264000	0.0343	-
0.8976	265000	0.0308	-
0.9009	266000	0.0321	-
0.9043	267000	0.0317	-
0.9077	268000	0.0299	-
0.9111	269000	0.029	-
0.9145	270000	0.0308	-
0.9179	271000	0.0343	-
0.9213	272000	0.033	-
0.9246	273000	0.0294	-
0.9280	274000	0.0309	-
0.9314	275000	0.0301	-
0.9348	276000	0.0321	-
0.9382	277000	0.0274	-
0.9416	278000	0.0298	-
0.9450	279000	0.0259	-
0.9484	280000	0.03	-
0.9517	281000	0.0319	-
0.9551	282000	0.032	-
0.9585	283000	0.0319	-
0.9619	284000	0.0309	-
0.9653	285000	0.0284	-
0.9687	286000	0.0286	-
0.9721	287000	0.033	-
0.9755	288000	0.0274	-
0.9788	289000	0.0304	-
0.9822	290000	0.0313	-
0.9856	291000	0.0302	-
0.9890	292000	0.028	-
0.9924	293000	0.0332	-
0.9958	294000	0.0302	-
0.9992	295000	0.0306	-
1.0000	295247	-	0.0001
1.0025	296000	0.0301	-
1.0059	297000	0.0312	-
1.0093	298000	0.0297	-
1.0127	299000	0.0268	-
1.0161	300000	0.0231	-
1.0195	301000	0.0312	-
1.0229	302000	0.0245	-
1.0263	303000	0.0327	-
1.0296	304000	0.0308	-
1.0330	305000	0.0247	-
1.0364	306000	0.029	-
1.0398	307000	0.0296	-
1.0432	308000	0.0271	-
1.0466	309000	0.0266	-
1.0500	310000	0.0283	-
1.0534	311000	0.0273	-
1.0567	312000	0.0303	-
1.0601	313000	0.0286	-
1.0635	314000	0.0272	-
1.0669	315000	0.0255	-
1.0703	316000	0.031	-
1.0737	317000	0.028	-
1.0771	318000	0.0265	-
1.0804	319000	0.0314	-
1.0838	320000	0.0315	-
1.0872	321000	0.0288	-
1.0906	322000	0.027	-
1.0940	323000	0.0232	-
1.0974	324000	0.0307	-
1.1008	325000	0.0275	-
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1.1109	328000	0.0303	-
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1.1177	330000	0.0277	-
1.1211	331000	0.0313	-
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1.1279	333000	0.0228	-
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1.1380	336000	0.0294	-
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1.1550	341000	0.0246	-
1.1583	342000	0.0268	-
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1.1685	345000	0.0271	-
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1.1821	349000	0.0285	-
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1.1888	351000	0.0251	-
1.1922	352000	0.0241	-
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1.1990	354000	0.0297	-
1.2024	355000	0.027	-
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1.2125	358000	0.0275	-
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1.2227	361000	0.027	-
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1.2566	371000	0.0264	-
1.2600	372000	0.0236	-
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1.2904	381000	0.0251	-
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1.3480	398000	0.0231	-
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1.3582	401000	0.027	-
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1.6969	501000	0.0234	-
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1.8595	549000	0.0188	-
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1.9915	588000	0.0191	-
1.9949	589000	0.0201	-
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2.0000	590494	-	0.0000
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2.0390	602000	0.019	-
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2.6622	786000	0.0163	-
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2.6689	788000	0.0163	-
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2.6893	794000	0.0155	-
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2.7028	798000	0.0143	-
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2.7739	819000	0.0168	-
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2.8044	828000	0.0149	-
2.8078	829000	0.0148	-
2.8112	830000	0.0152	-
2.8146	831000	0.0143	-
2.8180	832000	0.0128	-
2.8214	833000	0.0168	-
2.8247	834000	0.0165	-
2.8281	835000	0.0167	-
2.8315	836000	0.0142	-
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2.8383	838000	0.0133	-
2.8417	839000	0.0151	-
2.8451	840000	0.0145	-
2.8485	841000	0.0154	-
2.8518	842000	0.0151	-
2.8552	843000	0.0154	-
2.8586	844000	0.0129	-
2.8620	845000	0.0156	-
2.8654	846000	0.0138	-
2.8688	847000	0.0148	-
2.8722	848000	0.016	-
2.8755	849000	0.0164	-
2.8789	850000	0.015	-
2.8823	851000	0.015	-
2.8857	852000	0.0147	-
2.8891	853000	0.0152	-
2.8925	854000	0.0147	-
2.8959	855000	0.0156	-
2.8993	856000	0.0132	-
2.9026	857000	0.0158	-
2.9060	858000	0.0137	-
2.9094	859000	0.0139	-
2.9128	860000	0.0144	-
2.9162	861000	0.0137	-
2.9196	862000	0.0173	-
2.9230	863000	0.0141	-
2.9264	864000	0.0143	-
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2.9399	868000	0.0128	-
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2.9467	870000	0.0137	-
2.9501	871000	0.014	-
2.9534	872000	0.0153	-
2.9568	873000	0.0141	-
2.9602	874000	0.0158	-
2.9636	875000	0.0123	-
2.9670	876000	0.0152	-
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2.9738	878000	0.0152	-
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2.9839	881000	0.0152	-
2.9873	882000	0.0139	-
2.9907	883000	0.0145	-
2.9941	884000	0.016	-
2.9975	885000	0.0147	-
3.0000	885741	-	0.0000
3.0009	886000	0.014	-
3.0043	887000	0.0155	-
3.0076	888000	0.0158	-
3.0110	889000	0.0141	-
3.0144	890000	0.0116	-
3.0178	891000	0.0134	-
3.0212	892000	0.0131	-
3.0246	893000	0.0146	-
3.0280	894000	0.0153	-
3.0313	895000	0.0148	-
3.0347	896000	0.0126	-
3.0381	897000	0.0149	-
3.0415	898000	0.0136	-
3.0449	899000	0.0136	-
3.0483	900000	0.0145	-
3.0517	901000	0.014	-
3.0551	902000	0.0151	-
3.0584	903000	0.0142	-
3.0618	904000	0.0144	-
3.0652	905000	0.0139	-
3.0686	906000	0.0137	-
3.0720	907000	0.0148	-
3.0754	908000	0.0146	-
3.0788	909000	0.0149	-
3.0822	910000	0.0151	-
3.0855	911000	0.015	-
3.0889	912000	0.0149	-
3.0923	913000	0.0126	-
3.0957	914000	0.0139	-
3.0991	915000	0.0138	-
3.1025	916000	0.0137	-
3.1059	917000	0.0127	-
3.1093	918000	0.0138	-
3.1126	919000	0.0165	-
3.1160	920000	0.0142	-
3.1194	921000	0.015	-
3.1228	922000	0.0145	-
3.1262	923000	0.0131	-
3.1296	924000	0.0136	-
3.1330	925000	0.0136	-
3.1363	926000	0.0141	-
3.1397	927000	0.0138	-
3.1431	928000	0.0145	-
3.1465	929000	0.0139	-
3.1499	930000	0.013	-
3.1533	931000	0.0109	-
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3.1601	933000	0.0142	-
3.1634	934000	0.0137	-
3.1668	935000	0.0122	-
3.1702	936000	0.0131	-
3.1736	937000	0.0133	-
3.1770	938000	0.0135	-
3.1804	939000	0.0132	-
3.1838	940000	0.0161	-
3.1872	941000	0.0118	-
3.1905	942000	0.0138	-
3.1939	943000	0.0131	-
3.1973	944000	0.0147	-
3.2007	945000	0.0156	-
3.2041	946000	0.0135	-
3.2075	947000	0.0129	-
3.2109	948000	0.0155	-
3.2142	949000	0.013	-
3.2176	950000	0.0124	-
3.2210	951000	0.0138	-
3.2244	952000	0.0137	-
3.2278	953000	0.0143	-
3.2312	954000	0.0133	-
3.2346	955000	0.0167	-
3.2380	956000	0.0147	-
3.2413	957000	0.0124	-
3.2447	958000	0.0151	-
3.2481	959000	0.0131	-
3.2515	960000	0.0117	-
3.2549	961000	0.0143	-
3.2583	962000	0.0142	-
3.2617	963000	0.014	-
3.2651	964000	0.0135	-
3.2684	965000	0.0121	-
3.2718	966000	0.0118	-
3.2752	967000	0.0124	-
3.2786	968000	0.0118	-
3.2820	969000	0.0126	-
3.2854	970000	0.0118	-
3.2888	971000	0.0128	-
3.2921	972000	0.0118	-
3.2955	973000	0.0119	-
3.2989	974000	0.0151	-
3.3023	975000	0.0132	-
3.3057	976000	0.0127	-
3.3091	977000	0.0141	-
3.3125	978000	0.0121	-
3.3159	979000	0.0125	-
3.3192	980000	0.0132	-
3.3226	981000	0.0135	-
3.3260	982000	0.0141	-
3.3294	983000	0.0122	-
3.3328	984000	0.0131	-
3.3362	985000	0.0136	-
3.3396	986000	0.0137	-
3.3430	987000	0.0122	-
3.3463	988000	0.0136	-
3.3497	989000	0.0115	-
3.3531	990000	0.0124	-
3.3565	991000	0.0145	-
3.3599	992000	0.0138	-
3.3633	993000	0.0126	-
3.3667	994000	0.0142	-
3.3700	995000	0.0131	-
3.3734	996000	0.0118	-
3.3768	997000	0.0133	-
3.3802	998000	0.0127	-
3.3836	999000	0.0132	-
3.3870	1000000	0.0119	-
3.3904	1001000	0.0131	-
3.3938	1002000	0.0128	-
3.3971	1003000	0.0137	-
3.4005	1004000	0.014	-
3.4039	1005000	0.0138	-
3.4073	1006000	0.0126	-
3.4107	1007000	0.0128	-
3.4141	1008000	0.0133	-
3.4175	1009000	0.0142	-
3.4209	1010000	0.0141	-
3.4242	1011000	0.0122	-
3.4276	1012000	0.0133	-
3.4310	1013000	0.0122	-
3.4344	1014000	0.0146	-
3.4378	1015000	0.0131	-
3.4412	1016000	0.0123	-
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3.4479	1018000	0.0107	-
3.4513	1019000	0.0142	-
3.4547	1020000	0.0119	-
3.4581	1021000	0.0132	-
3.4615	1022000	0.0125	-
3.4649	1023000	0.014	-
3.4683	1024000	0.0142	-
3.4717	1025000	0.0116	-
3.4750	1026000	0.0126	-
3.4784	1027000	0.0141	-
3.4818	1028000	0.0133	-
3.4852	1029000	0.013	-
3.4886	1030000	0.0122	-
3.4920	1031000	0.0131	-
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3.5021	1034000	0.0129	-
3.5055	1035000	0.0133	-
3.5089	1036000	0.0132	-
3.5123	1037000	0.0148	-
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3.5191	1039000	0.0127	-
3.5225	1040000	0.0111	-
3.5258	1041000	0.012	-
3.5292	1042000	0.013	-
3.5326	1043000	0.0134	-
3.5360	1044000	0.0127	-
3.5394	1045000	0.0123	-
3.5428	1046000	0.0125	-
3.5462	1047000	0.0138	-
3.5496	1048000	0.0116	-
3.5529	1049000	0.0114	-
3.5563	1050000	0.014	-
3.5597	1051000	0.0123	-
3.5631	1052000	0.0137	-
3.5665	1053000	0.0131	-
3.5699	1054000	0.0123	-
3.5733	1055000	0.0114	-
3.5767	1056000	0.0114	-
3.5800	1057000	0.0134	-
3.5834	1058000	0.0139	-
3.5868	1059000	0.0125	-
3.5902	1060000	0.0121	-
3.5936	1061000	0.0134	-
3.5970	1062000	0.0128	-
3.6004	1063000	0.0126	-
3.6038	1064000	0.013	-
3.6071	1065000	0.0124	-
3.6105	1066000	0.0146	-
3.6139	1067000	0.0134	-
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3.6241	1070000	0.0127	-
3.6275	1071000	0.0138	-
3.6308	1072000	0.0139	-
3.6342	1073000	0.0133	-
3.6376	1074000	0.0125	-
3.6410	1075000	0.0146	-
3.6444	1076000	0.0135	-
3.6478	1077000	0.0119	-
3.6512	1078000	0.0108	-
3.6546	1079000	0.012	-
3.6579	1080000	0.014	-
3.6613	1081000	0.0141	-
3.6647	1082000	0.0109	-
3.6681	1083000	0.0139	-
3.6715	1084000	0.0125	-
3.6749	1085000	0.013	-
3.6783	1086000	0.0121	-
3.6817	1087000	0.0135	-
3.6850	1088000	0.0125	-
3.6884	1089000	0.0131	-
3.6918	1090000	0.0116	-
3.6952	1091000	0.0126	-
3.6986	1092000	0.0147	-
3.7020	1093000	0.0125	-
3.7054	1094000	0.0116	-
3.7087	1095000	0.0113	-
3.7121	1096000	0.0128	-
3.7155	1097000	0.0133	-
3.7189	1098000	0.0135	-
3.7223	1099000	0.0121	-
3.7257	1100000	0.0121	-
3.7291	1101000	0.0117	-
3.7325	1102000	0.011	-
3.7358	1103000	0.0121	-
3.7392	1104000	0.0114	-
3.7426	1105000	0.0125	-
3.7460	1106000	0.0121	-
3.7494	1107000	0.0123	-
3.7528	1108000	0.0119	-
3.7562	1109000	0.0124	-
3.7596	1110000	0.0116	-
3.7629	1111000	0.0127	-
3.7663	1112000	0.013	-
3.7697	1113000	0.012	-
3.7731	1114000	0.0134	-
3.7765	1115000	0.0127	-
3.7799	1116000	0.0118	-
3.7833	1117000	0.0126	-
3.7866	1118000	0.012	-
3.7900	1119000	0.0129	-
3.7934	1120000	0.0133	-
3.7968	1121000	0.0128	-
3.8002	1122000	0.0127	-
3.8036	1123000	0.0127	-
3.8070	1124000	0.0114	-
3.8104	1125000	0.0123	-
3.8137	1126000	0.0121	-
3.8171	1127000	0.0113	-
3.8205	1128000	0.0124	-
3.8239	1129000	0.0141	-
3.8273	1130000	0.0125	-
3.8307	1131000	0.012	-
3.8341	1132000	0.0128	-
3.8375	1133000	0.011	-
3.8408	1134000	0.0123	-
3.8442	1135000	0.0111	-
3.8476	1136000	0.0133	-
3.8510	1137000	0.0126	-
3.8544	1138000	0.0131	-
3.8578	1139000	0.0114	-
3.8612	1140000	0.0115	-
3.8645	1141000	0.0125	-
3.8679	1142000	0.0111	-
3.8713	1143000	0.0134	-
3.8747	1144000	0.0126	-
3.8781	1145000	0.0135	-
3.8815	1146000	0.0114	-
3.8849	1147000	0.0132	-
3.8883	1148000	0.0118	-
3.8916	1149000	0.0125	-
3.8950	1150000	0.0127	-
3.8984	1151000	0.0109	-
3.9018	1152000	0.0131	-
3.9052	1153000	0.0116	-
3.9086	1154000	0.0116	-
3.9120	1155000	0.0113	-
3.9154	1156000	0.0115	-
3.9187	1157000	0.0141	-
3.9221	1158000	0.012	-
3.9255	1159000	0.0121	-
3.9289	1160000	0.0117	-
3.9323	1161000	0.012	-
3.9357	1162000	0.0114	-
3.9391	1163000	0.0111	-
3.9424	1164000	0.011	-
3.9458	1165000	0.012	-
3.9492	1166000	0.0108	-
3.9526	1167000	0.0129	-
3.9560	1168000	0.012	-
3.9594	1169000	0.0127	-
3.9628	1170000	0.0112	-
3.9662	1171000	0.0117	-
3.9695	1172000	0.0112	-
3.9729	1173000	0.0132	-
3.9763	1174000	0.0106	-
3.9797	1175000	0.0126	-
3.9831	1176000	0.0122	-
3.9865	1177000	0.0125	-
3.9899	1178000	0.0115	-
3.9933	1179000	0.0135	-
3.9966	1180000	0.0125	-
4.0000	1180988	-	0.0000
4.0000	1181000	0.0122	-
4.0034	1182000	0.0121	-
4.0068	1183000	0.013	-
4.0102	1184000	0.012	-
4.0136	1185000	0.0102	-
4.0170	1186000	0.0103	-
4.0203	1187000	0.0112	-
4.0237	1188000	0.0115	-
4.0271	1189000	0.0125	-
4.0305	1190000	0.0128	-
4.0339	1191000	0.0102	-
4.0373	1192000	0.0122	-
4.0407	1193000	0.0116	-
4.0441	1194000	0.0114	-
4.0474	1195000	0.0114	-
4.0508	1196000	0.0119	-
4.0542	1197000	0.0125	-
4.0576	1198000	0.0124	-
4.0610	1199000	0.0122	-
4.0644	1200000	0.0112	-
4.0678	1201000	0.012	-
4.0712	1202000	0.0124	-
4.0745	1203000	0.0124	-
4.0779	1204000	0.0112	-
4.0813	1205000	0.0133	-
4.0847	1206000	0.0128	-
4.0881	1207000	0.0125	-
4.0915	1208000	0.0103	-
4.0949	1209000	0.0113	-
4.0982	1210000	0.0119	-
4.1016	1211000	0.0123	-
4.1050	1212000	0.0108	-
4.1084	1213000	0.0106	-
4.1118	1214000	0.0137	-
4.1152	1215000	0.0122	-
4.1186	1216000	0.0122	-
4.1220	1217000	0.0128	-
4.1253	1218000	0.0112	-
4.1287	1219000	0.0112	-
4.1321	1220000	0.012	-
4.1355	1221000	0.0116	-
4.1389	1222000	0.0123	-
4.1423	1223000	0.0119	-
4.1457	1224000	0.0117	-
4.1491	1225000	0.0113	-
4.1524	1226000	0.0097	-
4.1558	1227000	0.0121	-
4.1592	1228000	0.0114	-
4.1626	1229000	0.0116	-
4.1660	1230000	0.0089	-
4.1694	1231000	0.012	-
4.1728	1232000	0.0108	-
4.1762	1233000	0.0119	-
4.1795	1234000	0.0103	-
4.1829	1235000	0.0133	-
4.1863	1236000	0.0112	-
4.1897	1237000	0.0115	-
4.1931	1238000	0.011	-
4.1965	1239000	0.0119	-
4.1999	1240000	0.0135	-
4.2032	1241000	0.0115	-
4.2066	1242000	0.0116	-
4.2100	1243000	0.0128	-
4.2134	1244000	0.011	-
4.2168	1245000	0.0099	-
4.2202	1246000	0.012	-
4.2236	1247000	0.0116	-
4.2270	1248000	0.0119	-
4.2303	1249000	0.0115	-
4.2337	1250000	0.014	-
4.2371	1251000	0.0123	-
4.2405	1252000	0.0118	-
4.2439	1253000	0.0119	-
4.2473	1254000	0.0117	-
4.2507	1255000	0.0099	-
4.2541	1256000	0.0121	-
4.2574	1257000	0.0122	-
4.2608	1258000	0.0115	-
4.2642	1259000	0.0121	-
4.2676	1260000	0.0102	-
4.2710	1261000	0.0101	-
4.2744	1262000	0.0101	-
4.2778	1263000	0.0107	-
4.2811	1264000	0.0102	-
4.2845	1265000	0.0102	-
4.2879	1266000	0.0104	-
4.2913	1267000	0.0111	-
4.2947	1268000	0.0095	-
4.2981	1269000	0.0127	-
4.3015	1270000	0.0111	-
4.3049	1271000	0.0111	-
4.3082	1272000	0.0115	-
4.3116	1273000	0.0109	-
4.3150	1274000	0.0102	-
4.3184	1275000	0.0117	-
4.3218	1276000	0.0114	-
4.3252	1277000	0.0117	-
4.3286	1278000	0.0104	-
4.3320	1279000	0.0118	-
4.3353	1280000	0.0108	-
4.3387	1281000	0.0114	-
4.3421	1282000	0.0113	-
4.3455	1283000	0.012	-
4.3489	1284000	0.0097	-
4.3523	1285000	0.0103	-
4.3557	1286000	0.0111	-
4.3590	1287000	0.0119	-
4.3624	1288000	0.0119	-
4.3658	1289000	0.0112	-
4.3692	1290000	0.0111	-
4.3726	1291000	0.0099	-
4.3760	1292000	0.0113	-
4.3794	1293000	0.0106	-
4.3828	1294000	0.0118	-
4.3861	1295000	0.0103	-
4.3895	1296000	0.0111	-
4.3929	1297000	0.0107	-
4.3963	1298000	0.0121	-
4.3997	1299000	0.0117	-
4.4031	1300000	0.0121	-
4.4065	1301000	0.0109	-
4.4099	1302000	0.0103	-
4.4132	1303000	0.0115	-
4.4166	1304000	0.0121	-
4.4200	1305000	0.0121	-
4.4234	1306000	0.0114	-
4.4268	1307000	0.0103	-
4.4302	1308000	0.0113	-
4.4336	1309000	0.0124	-
4.4369	1310000	0.0119	-
4.4403	1311000	0.0098	-
4.4437	1312000	0.011	-
4.4471	1313000	0.0097	-
4.4505	1314000	0.0112	-
4.4539	1315000	0.0101	-
4.4573	1316000	0.0113	-
4.4607	1317000	0.0111	-
4.4640	1318000	0.0116	-
4.4674	1319000	0.0123	-
4.4708	1320000	0.0097	-
4.4742	1321000	0.011	-
4.4776	1322000	0.0112	-
4.4810	1323000	0.012	-
4.4844	1324000	0.011	-
4.4878	1325000	0.0103	-
4.4911	1326000	0.0105	-
4.4945	1327000	0.0115	-
4.4979	1328000	0.0118	-
4.5013	1329000	0.0117	-
4.5047	1330000	0.011	-
4.5081	1331000	0.0111	-
4.5115	1332000	0.0124	-
4.5148	1333000	0.0121	-
4.5182	1334000	0.0114	-
4.5216	1335000	0.0094	-
4.5250	1336000	0.0107	-
4.5284	1337000	0.0106	-
4.5318	1338000	0.0123	-
4.5352	1339000	0.0108	-
4.5386	1340000	0.0108	-
4.5419	1341000	0.0108	-
4.5453	1342000	0.0115	-
4.5487	1343000	0.0109	-
4.5521	1344000	0.0102	-
4.5555	1345000	0.0112	-
4.5589	1346000	0.011	-
4.5623	1347000	0.0112	-
4.5657	1348000	0.0121	-
4.5690	1349000	0.0108	-
4.5724	1350000	0.0099	-
4.5758	1351000	0.01	-
4.5792	1352000	0.0111	-
4.5826	1353000	0.0117	-
4.5860	1354000	0.0112	-
4.5894	1355000	0.0104	-
4.5927	1356000	0.0109	-
4.5961	1357000	0.0106	-
4.5995	1358000	0.0119	-
4.6029	1359000	0.0115	-
4.6063	1360000	0.0099	-
4.6097	1361000	0.0137	-
4.6131	1362000	0.0108	-
4.6165	1363000	0.0123	-
4.6198	1364000	0.0108	-
4.6232	1365000	0.0105	-
4.6266	1366000	0.0124	-
4.6300	1367000	0.0113	-
4.6334	1368000	0.0121	-
4.6368	1369000	0.0108	-
4.6402	1370000	0.0127	-
4.6436	1371000	0.0117	-
4.6469	1372000	0.0105	-
4.6503	1373000	0.0092	-
4.6537	1374000	0.0112	-
4.6571	1375000	0.0114	-
4.6605	1376000	0.0126	-
4.6639	1377000	0.0096	-
4.6673	1378000	0.0116	-
4.6706	1379000	0.0113	-
4.6740	1380000	0.0111	-
4.6774	1381000	0.0107	-
4.6808	1382000	0.0115	-
4.6842	1383000	0.0108	-
4.6876	1384000	0.012	-
4.6910	1385000	0.0103	-
4.6944	1386000	0.0108	-
4.6977	1387000	0.0124	-
4.7011	1388000	0.0117	-
4.7045	1389000	0.0101	-
4.7079	1390000	0.0099	-
4.7113	1391000	0.0112	-
4.7147	1392000	0.0116	-
4.7181	1393000	0.0123	-
4.7215	1394000	0.011	-
4.7248	1395000	0.0107	-
4.7282	1396000	0.01	-
4.7316	1397000	0.0099	-
4.7350	1398000	0.0102	-
4.7384	1399000	0.0102	-
4.7418	1400000	0.0113	-
4.7452	1401000	0.0105	-
4.7486	1402000	0.0107	-
4.7519	1403000	0.0104	-
4.7553	1404000	0.0114	-
4.7587	1405000	0.0101	-
4.7621	1406000	0.0115	-
4.7655	1407000	0.0117	-
4.7689	1408000	0.0094	-
4.7723	1409000	0.0122	-
4.7756	1410000	0.0114	-
4.7790	1411000	0.01	-
4.7824	1412000	0.0114	-
4.7858	1413000	0.0106	-
4.7892	1414000	0.0111	-
4.7926	1415000	0.0117	-
4.7960	1416000	0.0111	-
4.7994	1417000	0.0111	-
4.8027	1418000	0.0112	-
4.8061	1419000	0.0103	-
4.8095	1420000	0.0113	-
4.8129	1421000	0.0108	-
4.8163	1422000	0.0096	-
4.8197	1423000	0.0106	-
4.8231	1424000	0.0126	-
4.8265	1425000	0.0109	-
4.8298	1426000	0.0115	-
4.8332	1427000	0.0111	-
4.8366	1428000	0.0098	-
4.8400	1429000	0.0106	-
4.8434	1430000	0.01	-
4.8468	1431000	0.0117	-
4.8502	1432000	0.011	-
4.8535	1433000	0.012	-
4.8569	1434000	0.0097	-
4.8603	1435000	0.01	-
4.8637	1436000	0.0116	-
4.8671	1437000	0.0094	-
4.8705	1438000	0.0125	-
4.8739	1439000	0.0109	-
4.8773	1440000	0.0122	-
4.8806	1441000	0.01	-
4.8840	1442000	0.0121	-
4.8874	1443000	0.0105	-
4.8908	1444000	0.0107	-
4.8942	1445000	0.0114	-
4.8976	1446000	0.0107	-
4.9010	1447000	0.0111	-
4.9044	1448000	0.0106	-
4.9077	1449000	0.0105	-
4.9111	1450000	0.0098	-
4.9145	1451000	0.0109	-
4.9179	1452000	0.0113	-
4.9213	1453000	0.0115	-
4.9247	1454000	0.0103	-
4.9281	1455000	0.0107	-
4.9314	1456000	0.0108	-
4.9348	1457000	0.011	-
4.9382	1458000	0.0092	-
4.9416	1459000	0.0102	-
4.9450	1460000	0.0094	-
4.9484	1461000	0.0103	-
4.9518	1462000	0.0114	-
4.9552	1463000	0.0112	-
4.9585	1464000	0.0109	-
4.9619	1465000	0.0108	-
4.9653	1466000	0.0103	-
4.9687	1467000	0.0103	-
4.9721	1468000	0.0117	-
4.9755	1469000	0.0099	-
4.9789	1470000	0.011	-
4.9823	1471000	0.0109	-
4.9856	1472000	0.011	-
4.9890	1473000	0.01	-
4.9924	1474000	0.012	-
4.9958	1475000	0.0116	-
4.9992	1476000	0.0108	-
5.0000	1476235	-	0.0000

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

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

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}
}