all-MiniLM-L6-v27-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v26-pair_score on the pairs_with_scores_v23_typos_and_false_negatives 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
• Base model: KhaledReda/all-MiniLM-L6-v26-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v23_typos_and_false_negatives
• Language: en
• License: apache-2.0

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 = [
    'dry dog food with salmon skin',
    'pregnancy dress',
    'pantene prov',
]
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, 0.3194, 0.5020],
#         [0.3194, 1.0000, 0.4384],
#         [0.5020, 0.4384, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v23_typos_and_false_negatives

• Dataset: pairs_with_scores_v23_typos_and_false_negatives at 4fd5085
• Size: 153,453,064 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 6.38 tokens max: 47 tokens	min: 3 tokens mean: 6.5 tokens max: 23 tokens	min: 0.15 mean: 0.48 max: 1.0

• Samples:

  sentence1	sentence2	score
  pear cut rin	ribbed cardigan	0.3205174803733825
  microfoam espresso	cappuccino krind	0.8974514007568359
  private maxi night with wings	cleansing sponge	0.235989898443222

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v23_typos_and_false_negatives

• Dataset: pairs_with_scores_v23_typos_and_false_negatives at 4fd5085
• Size: 771,121 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 6.29 tokens max: 81 tokens	min: 3 tokens mean: 6.6 tokens max: 104 tokens	min: 0.15 mean: 0.49 max: 1.0

• Samples:

  sentence1	sentence2	score
  roast beef	herbs omelette wrap	0.8023932576179504
  dressings mustard seed ouil	titania	0.2587255835533142
  crispy chiicken wrap	cheese hotdog	0.850070834159851

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: 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: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 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: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.5423	650100	7.5403	-
0.5424	650200	7.5464	-
0.5424	650300	7.5191	-
0.5425	650400	7.4911	-
0.5426	650500	7.5165	-
0.5427	650600	7.4877	-
0.5428	650700	7.5055	-
0.5429	650800	7.5217	-
0.5429	650900	7.5382	-
0.5430	651000	7.5197	-
0.5431	651100	7.4905	-
0.5432	651200	7.5415	-
0.5433	651300	7.5211	-
0.5434	651400	7.5842	-
0.5434	651500	7.5047	-
0.5435	651600	7.5532	-
0.5436	651700	7.5197	-
0.5437	651800	7.4727	-
0.5438	651900	7.5208	-
0.5439	652000	7.5155	-
0.5439	652100	7.5214	-
0.5440	652200	7.5124	-
0.5441	652300	7.5211	-
0.5442	652400	7.5295	-
0.5443	652500	7.4952	-
0.5444	652600	7.5317	-
0.5444	652700	7.5317	-
0.5445	652800	7.5268	-
0.5446	652900	7.5222	-
0.5447	653000	7.5449	-
0.5448	653100	7.4931	-
0.5449	653200	7.5625	-
0.5449	653300	7.5088	-
0.5450	653400	7.5173	-
0.5451	653500	7.585	-
0.5452	653600	7.5306	-
0.5453	653700	7.5052	-
0.5454	653800	7.5435	-
0.5454	653900	7.5178	-
0.5455	654000	7.6444	-
0.5456	654100	7.5319	-
0.5457	654200	7.4966	-
0.5458	654300	7.5143	-
0.5459	654400	7.5387	-
0.5459	654500	7.5235	-
0.5460	654600	7.4846	-
0.5461	654700	7.5398	-
0.5462	654800	7.4958	-
0.5463	654900	7.4867	-
0.5464	655000	7.4991	-
0.5464	655100	7.5139	-
0.5465	655200	7.4915	-
0.5466	655300	7.5902	-
0.5467	655400	7.5223	-
0.5468	655500	7.4967	-
0.5469	655600	7.5176	-
0.5469	655700	7.5018	-
0.5470	655800	7.532	-
0.5471	655900	7.4933	-
0.5472	656000	7.4873	-
0.5473	656100	7.5104	-
0.5474	656200	7.4828	-
0.5474	656300	7.5228	-
0.5475	656400	7.5095	-
0.5476	656500	7.4997	-
0.5477	656600	7.5355	-
0.5478	656700	7.4857	-
0.5479	656800	7.5428	-
0.5479	656900	7.4691	-
0.5480	657000	7.5121	-
0.5481	657100	7.4973	-
0.5482	657200	7.5133	-
0.5483	657300	7.5217	-
0.5484	657400	7.5385	-
0.5484	657500	7.5411	-
0.5485	657600	7.5117	-
0.5486	657700	7.4899	-
0.5487	657800	7.548	-
0.5488	657900	7.5667	-
0.5489	658000	7.5286	-
0.5489	658100	7.5086	-
0.5490	658200	7.5322	-
0.5491	658300	7.5122	-
0.5492	658400	7.5398	-
0.5493	658500	7.493	-
0.5494	658600	7.5056	-
0.5494	658700	7.5269	-
0.5495	658800	7.5269	-
0.5496	658900	7.565	-
0.5497	659000	7.5101	-
0.5498	659100	7.5056	-
0.5499	659200	7.5415	-
0.5499	659300	7.5371	-
0.5500	659400	7.5687	-
0.5501	659500	7.4999	-
0.5502	659600	7.5654	-
0.5503	659700	7.5061	-
0.5504	659800	7.4993	-
0.5504	659900	7.5308	-
0.5505	660000	7.5174	-
0.5506	660100	7.4988	-
0.5507	660200	7.5392	-
0.5508	660300	7.5402	-
0.5509	660400	7.5313	-
0.5509	660500	7.6125	-
0.5510	660600	7.5142	-
0.5511	660700	7.5015	-
0.5512	660800	7.5096	-
0.5513	660900	7.5443	-
0.5514	661000	7.5435	-
0.5514	661100	7.5402	-
0.5515	661200	7.509	-
0.5516	661300	7.4901	-
0.5517	661400	7.5178	-
0.5518	661500	7.5112	-
0.5519	661600	7.5351	-
0.5519	661700	7.4901	-
0.5520	661800	7.5047	-
0.5521	661900	7.5023	-
0.5522	662000	7.5726	-
0.5523	662100	7.5045	-
0.5524	662200	7.5634	-
0.5524	662300	7.5282	-
0.5525	662400	7.5528	-
0.5526	662500	7.5826	-
0.5527	662600	7.4777	-
0.5528	662700	7.5077	-
0.5529	662800	7.5217	-
0.5529	662900	7.5245	-
0.5530	663000	7.5283	-
0.5531	663100	7.5255	-
0.5532	663200	7.4926	-
0.5533	663300	7.5278	-
0.5534	663400	7.553	-
0.5534	663500	7.5923	-
0.5535	663600	7.5465	-
0.5536	663700	7.5313	-
0.5537	663800	7.4793	-
0.5538	663900	7.5082	-
0.5539	664000	7.5073	-
0.5539	664100	7.4921	-
0.5540	664200	7.5128	-
0.5541	664300	7.5187	-
0.5542	664400	7.5154	-
0.5543	664500	7.5869	-
0.5544	664600	7.4945	-
0.5544	664700	7.5546	-
0.5545	664800	7.5061	-
0.5546	664900	7.5131	-
0.5547	665000	7.5816	-
0.5548	665100	7.5162	-
0.5549	665200	7.5296	-
0.5549	665300	7.5636	-
0.5550	665400	7.5348	-
0.5551	665500	7.5332	-
0.5552	665600	7.5007	-
0.5553	665700	7.509	-
0.5554	665800	7.4975	-
0.5554	665900	7.5212	-
0.5555	666000	7.5375	-
0.5556	666100	7.5387	-
0.5557	666200	7.4996	-
0.5558	666300	7.4717	-
0.5559	666400	7.5312	-
0.5559	666500	7.4876	-
0.5560	666600	7.5286	-
0.5561	666700	7.498	-
0.5562	666800	7.5167	-
0.5563	666900	7.5082	-
0.5564	667000	7.51	-
0.5564	667100	7.5142	-
0.5565	667200	7.509	-
0.5566	667300	7.507	-
0.5567	667400	7.497	-
0.5568	667500	7.5471	-
0.5569	667600	7.5029	-
0.5569	667700	7.5099	-
0.5570	667800	7.5225	-
0.5571	667900	7.4792	-
0.5572	668000	7.5572	-
0.5573	668100	7.5282	-
0.5574	668200	7.5376	-
0.5574	668300	7.553	-
0.5575	668400	7.5404	-
0.5576	668500	7.5557	-
0.5577	668600	7.5046	-
0.5578	668700	7.5714	-
0.5579	668800	7.5136	-
0.5579	668900	7.5164	-
0.5580	669000	7.4621	-
0.5581	669100	7.5456	-
0.5582	669200	7.5115	-
0.5583	669300	7.5359	-
0.5584	669400	7.5144	-
0.5585	669500	7.4787	-
0.5585	669600	7.5299	-
0.5586	669700	7.5356	-
0.5587	669800	7.4971	-
0.5588	669900	7.5091	-
0.5589	670000	7.5514	-
0.5590	670100	7.4793	-
0.5590	670200	7.5007	-
0.5591	670300	7.5059	-
0.5592	670400	7.5259	-
0.5593	670500	7.4734	-
0.5594	670600	7.5109	-
0.5595	670700	7.5031	-
0.5595	670800	7.5198	-
0.5596	670900	7.5174	-
0.5597	671000	7.4827	-
0.5598	671100	7.5322	-
0.5599	671200	7.4955	-
0.5600	671300	7.55	-
0.5600	671400	7.5324	-
0.5601	671500	7.5408	-
0.5602	671600	7.5381	-
0.5603	671700	7.4994	-
0.5604	671800	7.5465	-
0.5605	671900	7.4955	-
0.5605	672000	7.5609	-
0.5606	672100	7.5067	-
0.5607	672200	7.4934	-
0.5608	672300	7.5279	-
0.5609	672400	7.5252	-
0.5610	672500	7.5275	-
0.5610	672600	7.5213	-
0.5611	672700	7.5279	-
0.5612	672800	7.4761	-
0.5613	672900	7.5254	-
0.5614	673000	7.4789	-
0.5615	673100	7.4828	-
0.5615	673200	7.5715	-
0.5616	673300	7.4902	-
0.5617	673400	7.5369	-
0.5618	673500	7.4969	-
0.5619	673600	7.4896	-
0.5620	673700	7.5684	-
0.5620	673800	7.5109	-
0.5621	673900	7.5008	-
0.5622	674000	7.4952	-
0.5623	674100	7.5079	-
0.5624	674200	7.5364	-
0.5625	674300	7.5108	-
0.5625	674400	7.4842	-
0.5626	674500	7.4761	-
0.5627	674600	7.4972	-
0.5628	674700	7.5797	-
0.5629	674800	7.5227	-
0.5630	674900	7.4937	-
0.5630	675000	7.5408	-
0.5631	675100	7.4821	-
0.5632	675200	7.5289	-
0.5633	675300	7.5213	-
0.5634	675400	7.5409	-
0.5635	675500	7.5578	-
0.5635	675600	7.5824	-
0.5636	675700	7.5495	-
0.5637	675800	7.4936	-
0.5638	675900	7.4656	-
0.5639	676000	7.5695	-
0.5640	676100	7.5228	-
0.5640	676200	7.4795	-
0.5641	676300	7.5339	-
0.5642	676400	7.536	-
0.5643	676500	7.4999	-
0.5644	676600	7.5422	-
0.5645	676700	7.4906	-
0.5645	676800	7.5239	-
0.5646	676900	7.5293	-
0.5647	677000	7.579	-
0.5648	677100	7.5065	-
0.5649	677200	7.5266	-
0.5650	677300	7.5319	-
0.5650	677400	7.5023	-
0.5651	677500	7.515	-
0.5652	677600	7.5834	-
0.5653	677700	7.5003	-
0.5654	677800	7.5819	-
0.5655	677900	7.5035	-
0.5655	678000	7.4913	-
0.5656	678100	7.4853	-
0.5657	678200	7.5257	-
0.5658	678300	7.5043	-
0.5659	678400	7.5347	-
0.5660	678500	7.5562	-
0.5660	678600	7.4706	-
0.5661	678700	7.5192	-
0.5662	678800	7.5501	-
0.5663	678900	7.4859	-
0.5664	679000	7.5775	-
0.5665	679100	7.4817	-
0.5665	679200	7.484	-
0.5666	679300	7.5434	-
0.5667	679400	7.5421	-
0.5668	679500	7.5403	-
0.5669	679600	7.5737	-
0.5670	679700	7.5658	-
0.5670	679800	7.5015	-
0.5671	679900	7.5381	-
0.5672	680000	7.5189	-
0.5673	680100	7.5385	-
0.5674	680200	7.5544	-
0.5675	680300	7.5193	-
0.5675	680400	7.5774	-
0.5676	680500	7.5158	-
0.5677	680600	7.4993	-
0.5678	680700	7.5194	-
0.5679	680800	7.5192	-
0.5680	680900	7.5088	-
0.5680	681000	7.5608	-
0.5681	681100	7.516	-
0.5682	681200	7.4829	-
0.5683	681300	7.5006	-
0.5684	681400	7.5191	-
0.5685	681500	7.5005	-
0.5685	681600	7.5232	-
0.5686	681700	7.5025	-
0.5687	681800	7.4995	-
0.5688	681900	7.5291	-
0.5689	682000	7.5163	-
0.5690	682100	7.5608	-
0.5690	682200	7.519	-
0.5691	682300	7.4992	-
0.5692	682400	7.57	-
0.5693	682500	7.5042	-
0.5694	682600	7.5458	-
0.5695	682700	7.5212	-
0.5695	682800	7.5435	-
0.5696	682900	7.4802	-
0.5697	683000	7.552	-
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0.9793	1174000	7.4628	-
0.9794	1174100	7.4827	-
0.9794	1174200	7.4703	-
0.9795	1174300	7.5014	-
0.9796	1174400	7.452	-
0.9797	1174500	7.4813	-
0.9798	1174600	7.5024	-
0.9799	1174700	7.5008	-
0.9799	1174800	7.4496	-
0.9800	1174900	7.483	-
0.9801	1175000	7.4372	-
0.9802	1175100	7.4234	-
0.9803	1175200	7.4643	-
0.9804	1175300	7.5091	-
0.9804	1175400	7.4852	-
0.9805	1175500	7.4594	-
0.9806	1175600	7.4496	-
0.9807	1175700	7.46	-
0.9808	1175800	7.4323	-
0.9809	1175900	7.4761	-
0.9809	1176000	7.4435	-
0.9810	1176100	7.4948	-
0.9811	1176200	7.4875	-
0.9812	1176300	7.4644	-
0.9813	1176400	7.4586	-
0.9814	1176500	7.4698	-
0.9814	1176600	7.4884	-
0.9815	1176700	7.4787	-
0.9816	1176800	7.478	-
0.9817	1176900	7.5333	-
0.9818	1177000	7.4812	-
0.9819	1177100	7.5232	-
0.9819	1177200	7.4812	-
0.9820	1177300	7.4629	-
0.9821	1177400	7.47	-
0.9822	1177500	7.4916	-
0.9823	1177600	7.5256	-
0.9824	1177700	7.495	-
0.9824	1177800	7.5483	-
0.9825	1177900	7.4651	-
0.9826	1178000	7.4818	-
0.9827	1178100	7.4386	-
0.9828	1178200	7.4604	-
0.9829	1178300	7.4989	-
0.9829	1178400	7.4044	-
0.9830	1178500	7.5713	-
0.9831	1178600	7.511	-
0.9832	1178700	7.5099	-
0.9833	1178800	7.5331	-
0.9834	1178900	7.445	-
0.9834	1179000	7.5051	-
0.9835	1179100	7.4682	-
0.9836	1179200	7.4647	-
0.9837	1179300	7.4357	-
0.9838	1179400	7.4534	-
0.9839	1179500	7.4801	-
0.9839	1179600	7.4718	-
0.9840	1179700	7.4493	-
0.9841	1179800	7.4643	-
0.9842	1179900	7.4993	-
0.9843	1180000	7.4264	-
0.9844	1180100	7.4662	-
0.9844	1180200	7.4724	-
0.9845	1180300	7.4457	-
0.9846	1180400	7.4905	-
0.9847	1180500	7.487	-
0.9848	1180600	7.5207	-
0.9849	1180700	7.4731	-
0.9849	1180800	7.4732	-
0.9850	1180900	7.4579	-
0.9851	1181000	7.4563	-
0.9852	1181100	7.4925	-
0.9853	1181200	7.4933	-
0.9854	1181300	7.4952	-
0.9854	1181400	7.4332	-
0.9855	1181500	7.4776	-
0.9856	1181600	7.4585	-
0.9857	1181700	7.5252	-
0.9858	1181800	7.4424	-
0.9859	1181900	7.4639	-
0.9859	1182000	7.4977	-
0.9860	1182100	7.4608	-
0.9861	1182200	7.501	-
0.9862	1182300	7.4508	-
0.9863	1182400	7.4664	-
0.9864	1182500	7.4888	-
0.9864	1182600	7.5189	-
0.9865	1182700	7.5095	-
0.9866	1182800	7.5069	-
0.9867	1182900	7.485	-
0.9868	1183000	7.4792	-
0.9869	1183100	7.4584	-
0.9869	1183200	7.4697	-
0.9870	1183300	7.5038	-
0.9871	1183400	7.4489	-
0.9872	1183500	7.5768	-
0.9873	1183600	7.4423	-
0.9874	1183700	7.4944	-
0.9874	1183800	7.4784	-
0.9875	1183900	7.4513	-
0.9876	1184000	7.4758	-
0.9877	1184100	7.4479	-
0.9878	1184200	7.489	-
0.9879	1184300	7.4695	-
0.9879	1184400	7.4829	-
0.9880	1184500	7.4821	-
0.9881	1184600	7.4685	-
0.9882	1184700	7.5019	-
0.9883	1184800	7.5374	-
0.9884	1184900	7.4569	-
0.9884	1185000	7.4326	-
0.9885	1185100	7.4699	-
0.9886	1185200	7.4913	-
0.9887	1185300	7.4338	-
0.9888	1185400	7.5051	-
0.9889	1185500	7.488	-
0.9889	1185600	7.4697	-
0.9890	1185700	7.4757	-
0.9891	1185800	7.453	-
0.9892	1185900	7.4615	-
0.9893	1186000	7.5218	-
0.9894	1186100	7.4421	-
0.9894	1186200	7.4963	-
0.9895	1186300	7.5566	-
0.9896	1186400	7.4856	-
0.9897	1186500	7.4593	-
0.9898	1186600	7.5238	-
0.9899	1186700	7.4338	-
0.9899	1186800	7.4559	-
0.9900	1186900	7.4844	-
0.9901	1187000	7.5008	-
0.9902	1187100	7.4687	-
0.9903	1187200	7.4743	-
0.9904	1187300	7.4879	-
0.9904	1187400	7.5065	-
0.9905	1187500	7.4506	-
0.9906	1187600	7.488	-
0.9907	1187700	7.4459	-
0.9908	1187800	7.4744	-
0.9909	1187900	7.4538	-
0.9909	1188000	7.4569	-
0.9910	1188100	7.442	-
0.9911	1188200	7.5327	-
0.9912	1188300	7.461	-
0.9913	1188400	7.5117	-
0.9914	1188500	7.5032	-
0.9914	1188600	7.5017	-
0.9915	1188700	7.4537	-
0.9916	1188800	7.4522	-
0.9917	1188900	7.4887	-
0.9918	1189000	7.4199	-
0.9919	1189100	7.5211	-
0.9919	1189200	7.4524	-
0.9920	1189300	7.4594	-
0.9921	1189400	7.5251	-
0.9922	1189500	7.4684	-
0.9923	1189600	7.521	-
0.9924	1189700	7.521	-
0.9924	1189800	7.4499	-
0.9925	1189900	7.5022	-
0.9926	1190000	7.4699	-
0.9927	1190100	7.5021	-
0.9928	1190200	7.4517	-
0.9929	1190300	7.4863	-
0.9929	1190400	7.5126	-
0.9930	1190500	7.4253	-
0.9931	1190600	7.5008	-
0.9932	1190700	7.4796	-
0.9933	1190800	7.5048	-
0.9934	1190900	7.464	-
0.9934	1191000	7.4838	-
0.9935	1191100	7.4805	-
0.9936	1191200	7.4459	-
0.9937	1191300	7.4606	-
0.9938	1191400	7.5309	-
0.9939	1191500	7.5578	-
0.9940	1191600	7.4797	-
0.9940	1191700	7.5284	-
0.9941	1191800	7.4622	-
0.9942	1191900	7.4341	-
0.9943	1192000	7.4798	-
0.9944	1192100	7.46	-
0.9945	1192200	7.4379	-
0.9945	1192300	7.4446	-
0.9946	1192400	7.4449	-
0.9947	1192500	7.4473	-
0.9948	1192600	7.5137	-
0.9949	1192700	7.4884	-
0.9950	1192800	7.4707	-
0.9950	1192900	7.4671	-
0.9951	1193000	7.4905	-
0.9952	1193100	7.4972	-
0.9953	1193200	7.4725	-
0.9954	1193300	7.4751	-
0.9955	1193400	7.4427	-
0.9955	1193500	7.4278	-
0.9956	1193600	7.4521	-
0.9957	1193700	7.4682	-
0.9958	1193800	7.4455	-
0.9959	1193900	7.4466	-
0.9960	1194000	7.4928	-
0.9960	1194100	7.4776	-
0.9961	1194200	7.5041	-
0.9962	1194300	7.4574	-
0.9963	1194400	7.4785	-
0.9964	1194500	7.4738	-
0.9965	1194600	7.4717	-
0.9965	1194700	7.4675	-
0.9966	1194800	7.5071	-
0.9967	1194900	7.4858	-
0.9968	1195000	7.5066	-
0.9969	1195100	7.5233	-
0.9970	1195200	7.4778	-
0.9970	1195300	7.4445	-
0.9971	1195400	7.4573	-
0.9972	1195500	7.4997	-
0.9973	1195600	7.4938	-
0.9974	1195700	7.4706	-
0.9975	1195800	7.4839	-
0.9975	1195900	7.461	-
0.9976	1196000	7.4501	-
0.9977	1196100	7.5115	-
0.9978	1196200	7.4967	-
0.9979	1196300	7.456	-
0.9980	1196400	7.4645	-
0.9980	1196500	7.5131	-
0.9981	1196600	7.5292	-
0.9982	1196700	7.5136	-
0.9983	1196800	7.5054	-
0.9984	1196900	7.4933	-
0.9985	1197000	7.4885	-
0.9985	1197100	7.5386	-
0.9986	1197200	7.5436	-
0.9987	1197300	7.5493	-
0.9988	1197400	7.4799	-
0.9989	1197500	7.5061	-
0.9990	1197600	7.4529	-
0.9990	1197700	7.5185	-
0.9991	1197800	7.4649	-
0.9992	1197900	7.4967	-
0.9993	1198000	7.4244	-
0.9994	1198100	7.4293	-
0.9995	1198200	7.4412	-
0.9995	1198300	7.5261	-
0.9996	1198400	7.433	-
0.9997	1198500	7.5074	-
0.9998	1198600	7.4741	-
0.9999	1198700	7.5244	-
1.0000	1198800	7.4818	-

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.1.0
• Transformers: 4.55.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}