pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-2

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 tokens
• 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/UMLS-Pubmed-ST-TCE-Epoch-2")
# Run inference
sentences = [
    '[YEAR_RANGE] 2020-2024 [TEXT] Intraoperative Monitoring of the External Urethral Sphincter Reflex: A Novel Adjunct to Bulbocavernosus Reflex Neuromonitoring for Protecting the Sacral Neural Pathways Responsible for Urination, Defecation and Sexual Function.',
    '[YEAR_RANGE] 2020-2024 [TEXT] PURPOSE: Intraoperative bulbocavernosus reflex neuromonitoring has been utilized to protect bowel, bladder, and sexual function, providing a continuous functional assessment of the somatic sacral nervous system during surgeries where it is at risk. Bulbocavernosus reflex data may also provide additional functional insight, including an evaluation for spinal shock, distinguishing upper versus lower motor neuron injury (conus versus cauda syndromes) and prognosis for postoperative bowel and bladder function. Continuous intraoperative bulbocavernosus reflex monitoring has been utilized to provide the surgeon with an ongoing functional assessment of the anatomical elements involved in the S2-S4 mediated reflex arc including the conus, cauda equina and pudendal nerves. Intraoperative bulbocavernosus reflex monitoring typically includes the electrical activation of the dorsal nerves of the genitals to initiate the afferent component of the reflex, followed by recording the resulting muscle response using needle electromyography recordings from the external anal sphincter. METHODS: Herein we describe a complementary and novel technique that includes recording electromyography responses from the external urethral sphincter to monitor the external urethral sphincter reflex. Specialized foley catheters embedded with recording electrodes have recently become commercially available that provide the ability to perform intraoperative external urethral sphincter muscle recordings. RESULTS: We describe technical details and the potential utility of incorporating external urethral sphincter reflex recordings into existing sacral neuromonitoring paradigms to provide redundant yet complementary data streams. CONCLUSIONS: We present two illustrative neurosurgical oncology cases to demonstrate the utility of the external urethral sphincter reflex technique in the setting of the necessary surgical sacrifice of sacral nerve roots.',
    '[YEAR_RANGE] 2020-2024 [TEXT] Early menarche has been associated with adverse health outcomes, such as depressive symptoms. Discovering effect modifiers across these conditions in the pediatric population is a constant challenge. We tested whether movement behaviours modified the effect of the association between early menarche and depression symptoms among adolescents. This cross-sectional study included 2031 females aged 15-19 years across all Brazilian geographic regions. Data were collected using a self-administered questionnaire; 30.5% (n = 620) reported having experienced menarche before age 12 years (that is, early menarche). We used the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms. Accruing any moderate-vigorous physical activity during leisure time, limited recreational screen time, and having good sleep quality were the exposures investigated. Adolescents who experienced early menarche and met one (B: -4.45, 95% CI: (-5.38, -3.51)), two (B: -6.07 (-7.02, -5.12)), or three (B: -6.49 (-7.76, -5.21)), and adolescents who experienced not early menarche and met one (B: -5.33 (-6.20; -4.46)), two (B: -6.12 (-6.99; -5.24)), or three (B: -6.27 (-7.30; -5.24)) of the movement behaviour targets had lower PHQ-9 scores for depression symptoms than adolescents who experienced early menarche and did not meet any of the movement behaviours. The disparities in depressive symptoms among the adolescents (early menarche versus not early menarche) who adhered to all three target behaviours were not statistically significant (B: 0.41 (-0.19; 1.01)). Adherence to movement behaviours modified the effect of the association between early menarche and depression symptoms.',
]
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: 26,147,930 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 16 tokens mean: 45.85 tokens max: 137 tokens	min: 31 tokens mean: 307.52 tokens max: 1024 tokens

• Samples:

  anchor	positive
  [YEAR_RANGE] 1880-1884 [TEXT] ADDRESS OF COL. GARRICK MALLERY, U. S. ARMY.	[YEAR_RANGE] 1880-1884 [TEXT] It may be conceded that after man had all his present faculties, he did not choose between the adoption of voice and gesture, and never with those faculties, was in a state where the one was used, to the absolute exclusion of the other. The epoch, however, to which our speculations relate is that in which he had not reached the present symmetric development of his intellect and of his bodily organs, and the inquiry is: Which mode of communication was earliest adopted to his single wants and informed intelligence? With the voice he could imitate distinictively but few sounds of nature, while with gesture he could exhibit actions, motions, positions, forms, dimensions, directions and distances, with their derivations and analogues. It would seem from this unequal division of capacity that oral speech remained rudimentary long after gesture had become an efficient mode of communication. With due allowance for all purely imitative sounds, and for the spontaneous action of vocal organs under excitement, it appears that the connection between ideas and words is only to be explained by a compact between speaker and hearer which supposes the existence of a prior mode of communication. This was probably by gesture. At least we may accept it as a clew leading out of the labyrinth of philological confusion, and regulating the immemorial quest of man's primitive speech.
  [YEAR_RANGE] 1880-1884 [TEXT] How TO OBTAIN THE BRAIN OF THE CAT.	[YEAR_RANGE] 1880-1884 [TEXT] How to obtain the Brain of the Cat, (Wilder).-Correction: Page 158, second column, line 7, "grains," should be "grams;" page 159, near middle of 2nd column, "successily," should be "successively;" page 161, the number of Flower's paper is 3.
  [YEAR_RANGE] 1880-1884 [TEXT] DOLBEAR ON THE NATURE AND CONSTITUTION OF MATTER.	[YEAR_RANGE] 1880-1884 [TEXT] Mr. Dopp desires to make the following correction in his paper in the last issue: "In my article on page 200 of "Science", the expression and should have been and being the velocity of light.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

parquet

• Dataset: parquet
• Size: 26,147,930 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 15 tokens mean: 31.78 tokens max: 78 tokens	min: 16 tokens mean: 303.03 tokens max: 835 tokens

• Samples:

  anchor	positive
  [YEAR_RANGE] 2020-2024 [TEXT] Solubility and thermodynamics of mesalazine in aqueous mixtures of poly ethylene glycol 200/600 at 293.2-313.2K.	[YEAR_RANGE] 2020-2024 [TEXT] In this study, the solubility of mesalazine was investigated in binary solvent mixtures of poly ethylene glycols 200/600 and water at temperatures ranging from 293.2K to 313.2K. The solubility of mesalazine was determined using a shake-flask method, and its concentrations were measured using a UV-Vis spectrophotometer. The obtained solubility data were analyzed using mathematical models including the van't Hoff, Jouyban-Acree, Jouyban-Acree-van't Hoff, mixture response surface, and modified Wilson models. The experimental data obtained for mesalazine dissolution encompassed various thermodynamic properties, including ΔG°, ΔH°, ΔS°, and TΔS°. These properties offer valuable insights into the energetic aspects of the dissolution process and were calculated based on the van't Hoff equation.
  [YEAR_RANGE] 2020-2024 [TEXT] Safety and efficacy of remimazolam versus propofol during EUS: a multicenter randomized controlled study.	[YEAR_RANGE] 2020-2024 [TEXT] BACKGROUND AND AIMS: Propofol, a widely used sedative in GI endoscopic procedures, is associated with cardiorespiratory suppression. Remimazolam is a novel ultrashort-acting benzodiazepine sedative with rapid onset and minimal cardiorespiratory depression. This study compared the safety and efficacy of remimazolam and propofol during EUS procedures. METHODS: A multicenter randomized controlled study was conducted between October 2022 and March 2023 in patients who underwent EUS procedures. Patients were randomly assigned to receive either remimazolam or propofol as a sedative agent. The primary endpoint was cardiorespiratory adverse events.
  [YEAR_RANGE] 2020-2024 [TEXT] Ultrasound-Guided Vs Non-Guided Prolotherapy for Internal Derangement of Temporomandibular Joint. A Randomized Clinical Trial.	[YEAR_RANGE] 2020-2024 [TEXT] OBJECTIVES: This randomized clinical trial study aims to compare ultrasound-guided versus non-guided Dextrose 10% injections in patients suffering from internal derangement in the temporomandibular joint (TMJ). MATERIAL AND METHODS: The study population included 22 patients and 43 TMJs suffering from unilateral or bilateral TMJ painful clicking, magnetic resonance imaging (MRI) proved disc displacement with reduction (DDWR), refractory to or failed conservative treatment. The patients were divided randomly into two groups (non-guided and ultrasound (US)-guided groups). The procedure involved injection of 2 mL solution of a mixture of 0.75 mL 0.9% normal saline solution, 0.3 mL 2% lidocaine and 0.75 mL dextrose 10% using a 25G needle in the joint and 1 mL intramuscular injection to the masseter muscle at the most tender point. The Visual Analogue Score (VAS) was used to compare joint pain intensity over four different periods, beginning with pre-injection, 1-, 2-, and 6-months postinjection. RESULTS: Twenty-two patients 5 males (n = 5/22, 22.7%) and 17 females (n = 17/22, 77.2%) were included in this study. The mean age was 27.3 ± 7.4 years (30.2 ± 7.0) for the non-guided group and 24.3 ± 6.9 for the US-guided group. The dextrose injection reduced intensity over time in both groups with statistically significant improvement (P value <.05) at 2 and 6 months in both groups. There was no statistically significant difference in VAS assessment between both groups. CONCLUSION: Intra-articular injection of dextrose 10% for patients with painful clicking and DDWR resulted in reduced pain intensity in both US-guided and non-guided groups with significant symptomatic improvement over time in both groups. US guidance allowed accurate anatomical localization and safe procedure with a single joint puncture.

• 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: 970330
• 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: 970330
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0000	1	4.7032	-
0.0052	1000	0.6304	-
0.0103	2000	0.1763	-
0.0155	3000	0.1602	-
0.0206	4000	0.1494	-
0.0258	5000	0.1122	-
0.0309	6000	0.1225	-
0.0361	7000	0.1059	-
0.0412	8000	0.1002	-
0.0464	9000	0.0988	-
0.0515	10000	0.1148	-
0.0567	11000	0.1034	-
0.0618	12000	0.0758	-
0.0670	13000	0.1056	-
0.0721	14000	0.1123	-
0.0773	15000	0.0702	-
0.0824	16000	0.1633	-
0.0876	17000	0.0736	-
0.0928	18000	0.1132	-
0.0979	19000	0.0695	-
0.1031	20000	0.1339	-
0.1082	21000	0.0761	-
0.1134	22000	0.1311	-
0.1185	23000	0.0664	-
0.1237	24000	0.0807	-
0.1288	25000	0.0641	-
0.1340	26000	0.1327	-
0.1391	27000	0.0721	-
0.1443	28000	0.139	-
0.1494	29000	0.0694	-
0.1546	30000	0.1446	-
0.1597	31000	0.0651	-
0.1649	32000	0.1079	-
0.1700	33000	0.109	-
0.1752	34000	0.0741	-
0.1804	35000	0.144	-
0.1855	36000	0.0693	-
0.1907	37000	0.0762	-
0.1958	38000	0.1255	-
0.2010	39000	0.0764	-
0.2061	40000	0.1253	-
0.2113	41000	0.0861	-
0.2164	42000	0.0722	-
0.2216	43000	0.1178	-
0.2267	44000	0.0727	-
0.2319	45000	0.0827	-
0.2370	46000	0.0996	-
0.2422	47000	0.0657	-
0.2473	48000	0.0836	-
0.2525	49000	0.0913	-
0.2576	50000	0.0747	-
0.2628	51000	0.0649	-
0.2679	52000	0.0671	-
0.2731	53000	0.0905	-
0.2783	54000	0.0806	-
0.2834	55000	0.0574	-
0.2886	56000	0.0667	-
0.2937	57000	0.0634	-
0.2989	58000	0.0662	-
0.3040	59000	0.0607	-
0.3092	60000	0.0762	-
0.3143	61000	0.0474	-
0.3195	62000	0.0531	-
0.3246	63000	0.0579	-
0.3298	64000	0.0704	-
0.3349	65000	0.0688	-
0.3401	66000	0.0544	-
0.3452	67000	0.0424	-
0.3504	68000	0.0551	-
0.3555	69000	0.0717	-
0.3607	70000	0.0812	-
0.3659	71000	0.0882	-
0.3710	72000	0.0357	-
0.3762	73000	0.0448	-
0.3813	74000	0.0542	-
0.3865	75000	0.0456	-
0.3916	76000	0.1029	-
0.3968	77000	0.054	-
0.4019	78000	0.0673	-
0.4071	79000	0.0357	-
0.4122	80000	0.0601	-
0.4174	81000	0.0751	-
0.4225	82000	0.044	-
0.4277	83000	0.0489	-
0.4328	84000	0.0648	-
0.4380	85000	0.0308	-
0.4431	86000	0.0415	-
0.4483	87000	0.0468	-
0.4535	88000	0.0719	-
0.4586	89000	0.0577	-
0.4638	90000	0.0465	-
0.4689	91000	0.0357	-
0.4741	92000	0.0413	-
0.4792	93000	0.0482	-
0.4844	94000	0.0471	-
0.4895	95000	0.083	-
0.4947	96000	0.0313	-
0.4998	97000	0.0366	-
0.5050	98000	0.034	-
0.5101	99000	0.0366	-
0.5153	100000	0.0292	-
0.5204	101000	0.0423	-
0.5256	102000	0.0451	-
0.5307	103000	0.0243	-
0.5359	104000	0.0315	-
0.5411	105000	0.0288	-
0.5462	106000	0.0232	-
0.5514	107000	0.0533	-
0.5565	108000	0.0474	-
0.5617	109000	0.0312	-
0.5668	110000	0.0381	-
0.5720	111000	0.0407	-
0.5771	112000	0.0411	-
0.5823	113000	0.0285	-
0.5874	114000	0.0344	-
0.5926	115000	0.0471	-
0.5977	116000	0.0311	-
0.6029	117000	0.0671	-
0.6080	118000	0.0406	-
0.6132	119000	0.0342	-
0.6183	120000	0.0393	-
0.6235	121000	0.0288	-
0.6286	122000	0.0407	-
0.6338	123000	0.0385	-
0.6390	124000	0.0463	-
0.6441	125000	0.0419	-
0.6493	126000	0.0505	-
0.6544	127000	0.0426	-
0.6596	128000	0.0422	-
0.6647	129000	0.034	-
0.6699	130000	0.0266	-
0.6750	131000	0.0205	-
0.6802	132000	0.0412	-
0.6853	133000	0.0374	-
0.6905	134000	0.0338	-
0.6956	135000	0.0287	-
0.7008	136000	0.0364	-
0.7059	137000	0.0342	-
0.7111	138000	0.0406	-
0.7162	139000	0.0333	-
0.7214	140000	0.0408	-
0.7266	141000	0.0439	-
0.7317	142000	0.0327	-
0.7369	143000	0.028	-
0.7420	144000	0.0267	-
0.7472	145000	0.0286	-
0.7523	146000	0.0231	-
0.7575	147000	0.0291	-
0.7626	148000	0.0365	-
0.7678	149000	0.0345	-
0.7729	150000	0.0291	-
0.7781	151000	0.0364	-
0.7832	152000	0.0364	-
0.7884	153000	0.0291	-
0.7935	154000	0.0379	-
0.7987	155000	0.0343	-
0.8038	156000	0.0355	-
0.8090	157000	0.0334	-
0.8142	158000	0.0289	-
0.8193	159000	0.0366	-
0.8245	160000	0.0357	-
0.8296	161000	0.0265	-
0.8348	162000	0.0231	-
0.8399	163000	0.0177	-
0.8451	164000	0.022	-
0.8502	165000	0.0227	-
0.8554	166000	0.0179	-
0.8605	167000	0.0238	-
0.8657	168000	0.0225	-
0.8708	169000	0.0219	-
0.8760	170000	0.0254	-
0.8811	171000	0.0239	-
0.8863	172000	0.0267	-
0.8914	173000	0.0255	-
0.8966	174000	0.0234	-
0.9018	175000	0.0261	-
0.9069	176000	0.0235	-
0.9121	177000	0.0267	-
0.9172	178000	0.0232	-
0.9224	179000	0.0197	-
0.9275	180000	0.0189	-
0.9327	181000	0.0219	-
0.9378	182000	0.0226	-
0.9430	183000	0.021	-
0.9481	184000	0.0214	-
0.9533	185000	0.0219	-
0.9584	186000	0.021	-
0.9636	187000	0.0195	-
0.9687	188000	0.0188	-
0.9739	189000	0.0205	-
0.9790	190000	0.0199	-
0.9842	191000	0.0315	-
0.9893	192000	0.0214	-
0.9945	193000	0.0169	-
0.9997	194000	0.0182	-
1.0000	194066	-	0.0006
1.0048	195000	0.2355	-
1.0100	196000	0.0796	-
1.0151	197000	0.0853	-
1.0203	198000	0.0829	-
1.0254	199000	0.0628	-
1.0306	200000	0.0698	-
1.0357	201000	0.0601	-
1.0409	202000	0.0581	-
1.0460	203000	0.0577	-
1.0512	204000	0.0697	-
1.0563	205000	0.0515	-
1.0615	206000	0.0553	-
1.0666	207000	0.0613	-
1.0718	208000	0.0712	-
1.0769	209000	0.043	-
1.0821	210000	0.1127	-
1.0873	211000	0.0437	-
1.0924	212000	0.0737	-
1.0976	213000	0.0437	-
1.1027	214000	0.0916	-
1.1079	215000	0.0454	-
1.1130	216000	0.088	-
1.1182	217000	0.0442	-
1.1233	218000	0.0505	-
1.1285	219000	0.0414	-
1.1336	220000	0.0904	-
1.1388	221000	0.0466	-
1.1439	222000	0.0965	-
1.1491	223000	0.0459	-
1.1542	224000	0.0992	-
1.1594	225000	0.0435	-
1.1645	226000	0.0594	-
1.1697	227000	0.0857	-
1.1749	228000	0.049	-
1.1800	229000	0.0995	-
1.1852	230000	0.0471	-
1.1903	231000	0.0499	-
1.1955	232000	0.0866	-
1.2006	233000	0.0496	-
1.2058	234000	0.0854	-
1.2109	235000	0.0589	-
1.2161	236000	0.0461	-
1.2212	237000	0.0814	-
1.2264	238000	0.0489	-
1.2315	239000	0.0551	-
1.2367	240000	0.0695	-
1.2418	241000	0.043	-
1.2470	242000	0.0533	-
1.2521	243000	0.0556	-
1.2573	244000	0.0608	-
1.2625	245000	0.0426	-
1.2676	246000	0.0439	-
1.2728	247000	0.0638	-
1.2779	248000	0.0549	-
1.2831	249000	0.0377	-
1.2882	250000	0.0383	-
1.2934	251000	0.0472	-
1.2985	252000	0.0448	-
1.3037	253000	0.0387	-
1.3088	254000	0.0528	-
1.3140	255000	0.0331	-
1.3191	256000	0.0342	-
1.3243	257000	0.0362	-
1.3294	258000	0.0436	-
1.3346	259000	0.0524	-
1.3397	260000	0.0353	-
1.3449	261000	0.0274	-
1.3500	262000	0.0368	-
1.3552	263000	0.0486	-
1.3604	264000	0.0536	-
1.3655	265000	0.0595	-
1.3707	266000	0.024	-
1.3758	267000	0.0243	-
1.3810	268000	0.0393	-
1.3861	269000	0.029	-
1.3913	270000	0.0722	-
1.3964	271000	0.0366	-
1.4016	272000	0.0375	-
1.4067	273000	0.0289	-
1.4119	274000	0.0247	-
1.4170	275000	0.0695	-
1.4222	276000	0.0283	-
1.4273	277000	0.0328	-
1.4325	278000	0.0457	-
1.4376	279000	0.0204	-
1.4428	280000	0.0277	-
1.4480	281000	0.0255	-
1.4531	282000	0.0536	-
1.4583	283000	0.0411	-
1.4634	284000	0.0289	-
1.4686	285000	0.0244	-
1.4737	286000	0.0292	-
1.4789	287000	0.0334	-
1.4840	288000	0.0315	-
1.4892	289000	0.0408	-
1.4943	290000	0.0379	-
1.4995	291000	0.0243	-
1.5046	292000	0.0228	-
1.5098	293000	0.0235	-
1.5149	294000	0.0187	-
1.5201	295000	0.0256	-
1.5252	296000	0.031	-
1.5304	297000	0.0156	-
1.5356	298000	0.0216	-
1.5407	299000	0.0185	-
1.5459	300000	0.0146	-
1.5510	301000	0.0302	-
1.5562	302000	0.0346	-
1.5613	303000	0.0211	-
1.5665	304000	0.0211	-
1.5716	305000	0.0239	-
1.5768	306000	0.0265	-
1.5819	307000	0.018	-
1.5871	308000	0.0204	-
1.5922	309000	0.0288	-
1.5974	310000	0.0193	-
1.6025	311000	0.0443	-
1.6077	312000	0.0251	-
1.6128	313000	0.0209	-
1.6180	314000	0.0245	-
1.6232	315000	0.0179	-
1.6283	316000	0.026	-
1.6335	317000	0.025	-
1.6386	318000	0.0291	-
1.6438	319000	0.028	-
1.6489	320000	0.0351	-
1.6541	321000	0.0279	-
1.6592	322000	0.0285	-
1.6644	323000	0.0239	-
1.6695	324000	0.0171	-
1.6747	325000	0.0131	-
1.6798	326000	0.0252	-
1.6850	327000	0.0244	-
1.6901	328000	0.0234	-
1.6953	329000	0.0185	-
1.7004	330000	0.0248	-
1.7056	331000	0.0243	-
1.7107	332000	0.0282	-
1.7159	333000	0.0225	-
1.7211	334000	0.0256	-
1.7262	335000	0.03	-
1.7314	336000	0.0227	-
1.7365	337000	0.0192	-
1.7417	338000	0.0178	-
1.7468	339000	0.0187	-
1.7520	340000	0.0156	-
1.7571	341000	0.0186	-
1.7623	342000	0.0241	-
1.7674	343000	0.0252	-
1.7726	344000	0.0201	-
1.7777	345000	0.0251	-
1.7829	346000	0.0258	-
1.7880	347000	0.0216	-
1.7932	348000	0.0274	-
1.7983	349000	0.0244	-
1.8035	350000	0.0243	-
1.8087	351000	0.024	-
1.8138	352000	0.0182	-
1.8190	353000	0.0233	-
1.8241	354000	0.024	-
1.8293	355000	0.0177	-
1.8344	356000	0.0149	-
1.8396	357000	0.0113	-
1.8447	358000	0.0142	-
1.8499	359000	0.0147	-
1.8550	360000	0.0109	-
1.8602	361000	0.0155	-
1.8653	362000	0.0144	-
1.8705	363000	0.0131	-
1.8756	364000	0.0171	-
1.8808	365000	0.0156	-
1.8859	366000	0.0168	-
1.8911	367000	0.0167	-
1.8963	368000	0.0161	-
1.9014	369000	0.0168	-
1.9066	370000	0.0151	-
1.9117	371000	0.0178	-
1.9169	372000	0.0153	-
1.9220	373000	0.0133	-
1.9272	374000	0.0121	-
1.9323	375000	0.0141	-
1.9375	376000	0.0151	-
1.9426	377000	0.0142	-
1.9478	378000	0.0141	-
1.9529	379000	0.014	-
1.9581	380000	0.0144	-
1.9632	381000	0.0123	-
1.9684	382000	0.0128	-
1.9735	383000	0.0132	-
1.9787	384000	0.0135	-
1.9839	385000	0.0155	-
1.9890	386000	0.0214	-
1.9942	387000	0.0111	-
1.9993	388000	0.0121	-
2.0000	388132	-	0.0005

Framework Versions

• Python: 3.12.2
• Sentence Transformers: 3.2.1
• Transformers: 4.44.2
• PyTorch: 2.5.0
• Accelerate: 1.0.1
• Datasets: 3.0.2
• Tokenizers: 0.19.1

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",
}

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}
}