all-MiniLM-L6-v20-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v18-pair_score on the pairs_with_scores_v16_tag_sim_synonyms_typo_augmented 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-v18-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v16_tag_sim_synonyms_typo_augmented
• 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 = [
    'sapphire choker',
    'semi natural neckllace',
    'vanilla bodyy wahs',
]
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, 0.7462, 0.3102],
#         [0.7462, 1.0000, 0.4186],
#         [0.3102, 0.4186, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v16_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v16_tag_sim_synonyms_typo_augmented at a17fe62
• Size: 42,500,432 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: 6.17 tokens max: 14 tokens	min: 3 tokens mean: 6.8 tokens max: 101 tokens	min: 0.14 mean: 0.29 max: 1.0

• Samples:

  sentence1	sentence2	score
  kitchen container	mosquito killer	0.21
  liquid mascara	handmade skillet	0.18
  key neclkace	metallic b>ouse	0.27

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v16_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v16_tag_sim_synonyms_typo_augmented at a17fe62
• Size: 213,571 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: 6.24 tokens max: 43 tokens	min: 3 tokens mean: 6.76 tokens max: 93 tokens	min: 0.13 mean: 0.3 max: 1.0

• Samples:

  sentence1	sentence2	score
  vegetables	vegetables bwol	0.34
  escalopee	onion chhicken	0.49
  mozzarella pastrami pizza	peony fragrance lotion	0.21

• 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
0.0003	100	9.4158	-
0.0006	200	9.2352	-
0.0009	300	9.3321	-
0.0012	400	9.3289	-
0.0015	500	9.3221	-
0.0018	600	9.2566	-
0.0021	700	9.144	-
0.0024	800	9.1095	-
0.0027	900	9.1163	-
0.0030	1000	9.0541	-
0.0033	1100	9.0222	-
0.0036	1200	8.9942	-
0.0039	1300	8.9214	-
0.0042	1400	8.9557	-
0.0045	1500	9.0198	-
0.0048	1600	8.9887	-
0.0051	1700	8.9428	-
0.0054	1800	8.781	-
0.0057	1900	8.7545	-
0.0060	2000	8.7841	-
0.0063	2100	8.8059	-
0.0066	2200	8.7287	-
0.0069	2300	8.7247	-
0.0072	2400	8.7033	-
0.0075	2500	8.6408	-
0.0078	2600	8.61	-
0.0081	2700	8.6618	-
0.0084	2800	8.5861	-
0.0087	2900	8.5313	-
0.0090	3000	8.5726	-
0.0093	3100	8.5203	-
0.0096	3200	8.4618	-
0.0099	3300	8.4772	-
0.0102	3400	8.4455	-
0.0105	3500	8.487	-
0.0108	3600	8.4442	-
0.0111	3700	8.4334	-
0.0114	3800	8.3673	-
0.0117	3900	8.3712	-
0.0120	4000	8.4295	-
0.0123	4100	8.3309	-
0.0126	4200	8.3085	-
0.0130	4300	8.2611	-
0.0133	4400	8.2855	-
0.0136	4500	8.2809	-
0.0139	4600	8.2863	-
0.0142	4700	8.2471	-
0.0145	4800	8.2407	-
0.0148	4900	8.2412	-
0.0151	5000	8.2269	-
0.0154	5100	8.2285	-
0.0157	5200	8.2133	-
0.0160	5300	8.1945	-
0.0163	5400	8.1934	-
0.0166	5500	8.1767	-
0.0169	5600	8.1933	-
0.0172	5700	8.1963	-
0.0175	5800	8.1741	-
0.0178	5900	8.1579	-
0.0181	6000	8.1783	-
0.0184	6100	8.1316	-
0.0187	6200	8.158	-
0.0190	6300	8.1237	-
0.0193	6400	8.166	-
0.0196	6500	8.1298	-
0.0199	6600	8.1419	-
0.0202	6700	8.1278	-
0.0205	6800	8.157	-
0.0208	6900	8.1118	-
0.0211	7000	8.1201	-
0.0214	7100	8.0999	-
0.0217	7200	8.1238	-
0.0220	7300	8.111	-
0.0223	7400	8.1245	-
0.0226	7500	8.0989	-
0.0229	7600	8.1019	-
0.0232	7700	8.1093	-
0.0235	7800	8.1294	-
0.0238	7900	8.1146	-
0.0241	8000	8.0976	-
0.0244	8100	8.1063	-
0.0247	8200	8.1009	-
0.0250	8300	8.0906	-
0.0253	8400	8.1266	-
0.0256	8500	8.0899	-
0.0259	8600	8.0847	-
0.0262	8700	8.0742	-
0.0265	8800	8.0641	-
0.0268	8900	8.0701	-
0.0271	9000	8.0722	-
0.0274	9100	8.0936	-
0.0277	9200	8.045	-
0.0280	9300	8.063	-
0.0283	9400	8.0674	-
0.0286	9500	8.0626	-
0.0289	9600	8.0543	-
0.0292	9700	8.0511	-
0.0295	9800	8.0668	-
0.0298	9900	8.0368	-
0.0301	10000	8.0709	-
0.0304	10100	8.0486	-
0.0307	10200	8.0301	-
0.0310	10300	8.0459	-
0.0313	10400	8.0314	-
0.0316	10500	8.0363	-
0.0319	10600	8.0623	-
0.0322	10700	8.0477	-
0.0325	10800	8.0675	-
0.0328	10900	8.0438	-
0.0331	11000	8.0096	-
0.0334	11100	8.0411	-
0.0337	11200	8.0185	-
0.0340	11300	8.0228	-
0.0343	11400	8.0367	-
0.0346	11500	8.0235	-
0.0349	11600	8.0285	-
0.0352	11700	7.9966	-
0.0355	11800	8.0151	-
0.0358	11900	8.0115	-
0.0361	12000	8.0166	-
0.0364	12100	8.0128	-
0.0367	12200	8.0087	-
0.0370	12300	8.025	-
0.0373	12400	8.0101	-
0.0376	12500	8.0011	-
0.0379	12600	8.0055	-
0.0382	12700	8.0166	-
0.0386	12800	8.0222	-
0.0389	12900	7.9978	-
0.0392	13000	7.9871	-
0.0395	13100	8.0112	-
0.0398	13200	8.011	-
0.0401	13300	7.987	-
0.0404	13400	8.0037	-
0.0407	13500	7.9878	-
0.0410	13600	7.9836	-
0.0413	13700	7.9833	-
0.0416	13800	7.9684	-
0.0419	13900	7.9964	-
0.0422	14000	7.9753	-
0.0425	14100	7.9766	-
0.0428	14200	7.9722	-
0.0431	14300	7.9866	-
0.0434	14400	7.9802	-
0.0437	14500	7.9698	-
0.0440	14600	7.9877	-
0.0443	14700	7.9951	-
0.0446	14800	7.989	-
0.0449	14900	7.9638	-
0.0452	15000	8.0078	-
0.0455	15100	7.9497	-
0.0458	15200	7.9835	-
0.0461	15300	7.994	-
0.0464	15400	7.9521	-
0.0467	15500	7.9767	-
0.0470	15600	7.9835	-
0.0473	15700	7.9548	-
0.0476	15800	7.9915	-
0.0479	15900	7.9372	-
0.0482	16000	7.9892	-
0.0485	16100	7.9625	-
0.0488	16200	7.9512	-
0.0491	16300	7.9499	-
0.0494	16400	7.9803	-
0.0497	16500	7.9413	-
0.0500	16600	7.9302	-
0.0503	16700	7.9404	-
0.0506	16800	7.9497	-
0.0509	16900	7.9794	-
0.0512	17000	7.9492	-
0.0515	17100	7.9529	-
0.0518	17200	7.9775	-
0.0521	17300	7.9693	-
0.0524	17400	7.9489	-
0.0527	17500	7.976	-
0.0530	17600	7.9335	-
0.0533	17700	7.9267	-
0.0536	17800	7.9266	-
0.0539	17900	7.9217	-
0.0542	18000	7.9454	-
0.0545	18100	7.9571	-
0.0548	18200	7.9231	-
0.0551	18300	7.94	-
0.0554	18400	7.9164	-
0.0557	18500	7.8974	-
0.0560	18600	7.954	-
0.0563	18700	7.9418	-
0.0566	18800	7.9583	-
0.0569	18900	7.93	-
0.0572	19000	7.939	-
0.0575	19100	7.9028	-
0.0578	19200	7.9374	-
0.0581	19300	7.9448	-
0.0584	19400	7.9394	-
0.0587	19500	7.923	-
0.0590	19600	7.93	-
0.0593	19700	7.9507	-
0.0596	19800	7.9169	-
0.0599	19900	7.9264	-
0.0602	20000	7.9328	-
0.0605	20100	7.9192	-
0.0608	20200	7.9316	-
0.0611	20300	7.9167	-
0.0614	20400	7.9166	-
0.0617	20500	7.9295	-
0.0620	20600	7.9353	-
0.0623	20700	7.8926	-
0.0626	20800	7.8973	-
0.0629	20900	7.9267	-
0.0632	21000	7.913	-
0.0635	21100	7.9195	-
0.0638	21200	7.932	-
0.0641	21300	7.9366	-
0.0645	21400	7.9092	-
0.0648	21500	7.9384	-
0.0651	21600	7.8713	-
0.0654	21700	7.8977	-
0.0657	21800	7.9005	-
0.0660	21900	7.896	-
0.0663	22000	7.8852	-
0.0666	22100	7.9189	-
0.0669	22200	7.9166	-
0.0672	22300	7.9054	-
0.0675	22400	7.8944	-
0.0678	22500	7.8948	-
0.0681	22600	7.9113	-
0.0684	22700	7.8649	-
0.0687	22800	7.8652	-
0.0690	22900	7.9418	-
0.0693	23000	7.8831	-
0.0696	23100	7.9044	-
0.0699	23200	7.8906	-
0.0702	23300	7.9095	-
0.0705	23400	7.8982	-
0.0708	23500	7.8884	-
0.0711	23600	7.895	-
0.0714	23700	7.8868	-
0.0717	23800	7.863	-
0.0720	23900	7.8839	-
0.0723	24000	7.9148	-
0.0726	24100	7.9215	-
0.0729	24200	7.899	-
0.0732	24300	7.9026	-
0.0735	24400	7.9195	-
0.0738	24500	7.8908	-
0.0741	24600	7.9105	-
0.0744	24700	7.902	-
0.0747	24800	7.8816	-
0.0750	24900	7.8838	-
0.0753	25000	7.8482	-
0.0756	25100	7.8846	-
0.0759	25200	7.883	-
0.0762	25300	7.8687	-
0.0765	25400	7.8758	-
0.0768	25500	7.8918	-
0.0771	25600	7.8515	-
0.0774	25700	7.8704	-
0.0777	25800	7.9072	-
0.0780	25900	7.8879	-
0.0783	26000	7.8724	-
0.0786	26100	7.8566	-
0.0789	26200	7.8593	-
0.0792	26300	7.8834	-
0.0795	26400	7.9005	-
0.0798	26500	7.8855	-
0.0801	26600	7.8896	-
0.0804	26700	7.8756	-
0.0807	26800	7.8713	-
0.0810	26900	7.8738	-
0.0813	27000	7.8733	-
0.0816	27100	7.8652	-
0.0819	27200	7.8843	-
0.0822	27300	7.8694	-
0.0825	27400	7.8628	-
0.0828	27500	7.858	-
0.0831	27600	7.8633	-
0.0834	27700	7.8478	-
0.0837	27800	7.8679	-
0.0840	27900	7.8504	-
0.0843	28000	7.8346	-
0.0846	28100	7.8655	-
0.0849	28200	7.8706	-
0.0852	28300	7.8715	-
0.0855	28400	7.8518	-
0.0858	28500	7.8727	-
0.0861	28600	7.8625	-
0.0864	28700	7.8597	-
0.0867	28800	7.8707	-
0.0870	28900	7.8562	-
0.0873	29000	7.8858	-
0.0876	29100	7.8588	-
0.0879	29200	7.8572	-
0.0882	29300	7.8431	-
0.0885	29400	7.8727	-
0.0888	29500	7.8446	-
0.0891	29600	7.8696	-
0.0894	29700	7.8678	-
0.0897	29800	7.8265	-
0.0901	29900	7.8527	-
0.0904	30000	7.8825	-
0.0907	30100	7.8595	-
0.0910	30200	7.8487	-
0.0913	30300	7.8166	-
0.0916	30400	7.9071	-
0.0919	30500	7.8608	-
0.0922	30600	7.8326	-
0.0925	30700	7.8232	-
0.0928	30800	7.8594	-
0.0931	30900	7.862	-
0.0934	31000	7.8758	-
0.0937	31100	7.8627	-
0.0940	31200	7.8527	-
0.0943	31300	7.8295	-
0.0946	31400	7.8348	-
0.0949	31500	7.8444	-
0.0952	31600	7.8388	-
0.0955	31700	7.8434	-
0.0958	31800	7.8577	-
0.0961	31900	7.8619	-
0.0964	32000	7.8093	-
0.0967	32100	7.8213	-
0.0970	32200	7.8375	-
0.0973	32300	7.8528	-
0.0976	32400	7.8327	-
0.0979	32500	7.8978	-
0.0982	32600	7.8558	-
0.0985	32700	7.8494	-
0.0988	32800	7.8485	-
0.0991	32900	7.8494	-
0.0994	33000	7.851	-
0.0997	33100	7.8352	-
0.1000	33200	7.8456	-
0.1003	33300	7.8633	-
0.1006	33400	7.8541	-
0.1009	33500	7.8683	-
0.1012	33600	7.8099	-
0.1015	33700	7.8352	-
0.1018	33800	7.8594	-
0.1021	33900	7.8391	-
0.1024	34000	7.8447	-
0.1027	34100	7.8434	-
0.1030	34200	7.8462	-
0.1033	34300	7.8156	-
0.1036	34400	7.8245	-
0.1039	34500	7.8232	-
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0.1928	64000	7.7835	-
0.1931	64100	7.7676	-
0.1934	64200	7.7343	-
0.1937	64300	7.7246	-
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0.1949	64700	7.7703	-
0.1952	64800	7.7632	-
0.1955	64900	7.7336	-
0.1958	65000	7.7159	-
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0.1967	65300	7.7485	-
0.1970	65400	7.7264	-
0.1973	65500	7.7397	-
0.1976	65600	7.7239	-
0.1979	65700	7.7638	-
0.1982	65800	7.7246	-
0.1985	65900	7.7236	-
0.1988	66000	7.7298	-
0.1991	66100	7.7501	-
0.1994	66200	7.728	-
0.1997	66300	7.756	-
0.2000	66400	7.7807	-
0.2003	66500	7.7552	-
0.2006	66600	7.7573	-
0.2009	66700	7.78	-
0.2012	66800	7.7346	-
0.2015	66900	7.7333	-
0.2018	67000	7.7148	-
0.2021	67100	7.7436	-
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0.5554	184400	7.6178	-
0.5557	184500	7.6632	-
0.5560	184600	7.6192	-
0.5563	184700	7.6545	-
0.5566	184800	7.6323	-
0.5569	184900	7.6383	-
0.5572	185000	7.6424	-
0.5575	185100	7.6427	-
0.5578	185200	7.6622	-
0.5581	185300	7.6245	-
0.5584	185400	7.6726	-
0.5587	185500	7.6291	-
0.5590	185600	7.6459	-
0.5593	185700	7.6427	-
0.5596	185800	7.6605	-
0.5599	185900	7.641	-
0.5602	186000	7.6712	-
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0.5611	186300	7.638	-
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0.5620	186600	7.6291	-
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0.5632	187000	7.6255	-
0.5635	187100	7.6592	-
0.5638	187200	7.6551	-
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0.5647	187500	7.6261	-
0.5650	187600	7.6319	-
0.5653	187700	7.6829	-
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0.5665	188100	7.6398	-
0.5668	188200	7.6241	-
0.5671	188300	7.6773	-
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0.5680	188600	7.6435	-
0.5683	188700	7.6441	-
0.5686	188800	7.6496	-
0.5689	188900	7.6475	-
0.5692	189000	7.6381	-
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0.5698	189200	7.6581	-
0.5701	189300	7.6486	-
0.5704	189400	7.7106	-
0.5707	189500	7.6635	-
0.5710	189600	7.6326	-
0.5713	189700	7.6715	-
0.5716	189800	7.6354	-
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0.5725	190100	7.6398	-
0.5728	190200	7.6723	-
0.5731	190300	7.6344	-
0.5734	190400	7.6239	-
0.5737	190500	7.6578	-
0.5740	190600	7.6845	-
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0.5746	190800	7.6583	-
0.5749	190900	7.6296	-
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0.5758	191200	7.6332	-
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0.5764	191400	7.6367	-
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0.5770	191600	7.6038	-
0.5773	191700	7.6303	-
0.5776	191800	7.6755	-
0.5780	191900	7.6555	-
0.5783	192000	7.6392	-
0.5786	192100	7.6692	-
0.5789	192200	7.6697	-
0.5792	192300	7.6697	-
0.5795	192400	7.5915	-
0.5798	192500	7.6682	-
0.5801	192600	7.6375	-
0.5804	192700	7.6388	-
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0.5810	192900	7.6489	-
0.5813	193000	7.6601	-
0.5816	193100	7.6363	-
0.5819	193200	7.5975	-
0.5822	193300	7.6216	-
0.5825	193400	7.6541	-
0.5828	193500	7.6364	-
0.5831	193600	7.648	-
0.5834	193700	7.6407	-
0.5837	193800	7.701	-
0.5840	193900	7.677	-
0.5843	194000	7.6329	-
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0.5849	194200	7.6316	-
0.5852	194300	7.6323	-
0.5855	194400	7.5996	-
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0.5861	194600	7.6434	-
0.5864	194700	7.6824	-
0.5867	194800	7.6339	-
0.5870	194900	7.6181	-
0.5873	195000	7.5822	-
0.5876	195100	7.6332	-
0.5879	195200	7.6314	-
0.5882	195300	7.6352	-
0.5885	195400	7.6265	-
0.5888	195500	7.6048	-
0.5891	195600	7.6947	-
0.5894	195700	7.6239	-
0.5897	195800	7.6272	-
0.5900	195900	7.6166	-
0.5903	196000	7.6421	-
0.5906	196100	7.6602	-
0.5909	196200	7.5973	-
0.5912	196300	7.6265	-
0.5915	196400	7.629	-
0.5918	196500	7.6633	-
0.5921	196600	7.6509	-
0.5924	196700	7.6317	-
0.5927	196800	7.6541	-
0.5930	196900	7.6915	-
0.5933	197000	7.6393	-
0.5936	197100	7.6396	-
0.5939	197200	7.6456	-
0.5942	197300	7.6332	-
0.5945	197400	7.6095	-
0.5948	197500	7.6093	-
0.5951	197600	7.6854	-
0.5954	197700	7.6273	-
0.5957	197800	7.6502	-
0.5960	197900	7.65	-
0.5963	198000	7.6819	-
0.5966	198100	7.623	-
0.5969	198200	7.6852	-
0.5972	198300	7.6712	-
0.5975	198400	7.6046	-
0.5978	198500	7.6144	-
0.5981	198600	7.631	-
0.5984	198700	7.6242	-
0.5987	198800	7.6349	-
0.5990	198900	7.6403	-
0.5993	199000	7.6244	-
0.5996	199100	7.6297	-
0.5999	199200	7.6413	-
0.6002	199300	7.6357	-
0.6005	199400	7.6756	-
0.6008	199500	7.6853	-
0.6011	199600	7.6435	-
0.6014	199700	7.6557	-
0.6017	199800	7.6782	-
0.6020	199900	7.6506	-
0.6023	200000	7.616	7.5402
0.6026	200100	7.6618	-
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0.6032	200300	7.6642	-
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0.6042	200600	7.6649	-
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0.6054	201000	7.6257	-
0.6057	201100	7.6333	-
0.6060	201200	7.637	-
0.6063	201300	7.6789	-
0.6066	201400	7.5956	-
0.6069	201500	7.6583	-
0.6072	201600	7.6146	-
0.6075	201700	7.6371	-
0.6078	201800	7.6252	-
0.6081	201900	7.6604	-
0.6084	202000	7.6941	-
0.6087	202100	7.6555	-
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0.6400	212500	7.6649	-
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0.7035	233600	7.6084	-
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0.7047	234000	7.6257	-
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0.7053	234200	7.6543	-
0.7056	234300	7.6617	-
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0.8547	283800	7.669	-
0.8550	283900	7.6023	-
0.8553	284000	7.5978	-
0.8556	284100	7.6065	-
0.8559	284200	7.5823	-
0.8562	284300	7.5945	-
0.8565	284400	7.6599	-
0.8568	284500	7.5957	-
0.8571	284600	7.589	-
0.8574	284700	7.5905	-
0.8577	284800	7.6297	-
0.8580	284900	7.6051	-
0.8583	285000	7.5905	-
0.8586	285100	7.5764	-
0.8589	285200	7.6543	-
0.8592	285300	7.7012	-
0.8595	285400	7.6657	-
0.8598	285500	7.5873	-
0.8602	285600	7.6261	-
0.8605	285700	7.5795	-
0.8608	285800	7.6077	-
0.8611	285900	7.5872	-
0.8614	286000	7.5977	-
0.8617	286100	7.6562	-
0.8620	286200	7.6453	-
0.8623	286300	7.5789	-
0.8626	286400	7.5902	-
0.8629	286500	7.6009	-
0.8632	286600	7.6015	-
0.8635	286700	7.6788	-
0.8638	286800	7.5826	-
0.8641	286900	7.6486	-
0.8644	287000	7.6287	-
0.8647	287100	7.616	-
0.8650	287200	7.617	-
0.8653	287300	7.5918	-
0.8656	287400	7.5731	-
0.8659	287500	7.5812	-
0.8662	287600	7.6206	-
0.8665	287700	7.5809	-
0.8668	287800	7.628	-
0.8671	287900	7.6137	-
0.8674	288000	7.6072	-
0.8677	288100	7.6023	-
0.8680	288200	7.6229	-
0.8683	288300	7.643	-
0.8686	288400	7.6234	-
0.8689	288500	7.5661	-
0.8692	288600	7.641	-
0.8695	288700	7.6071	-
0.8698	288800	7.6014	-
0.8701	288900	7.628	-
0.8704	289000	7.5643	-
0.8707	289100	7.5972	-
0.8710	289200	7.5994	-
0.8713	289300	7.657	-
0.8716	289400	7.6347	-
0.8719	289500	7.6303	-
0.8722	289600	7.6641	-
0.8725	289700	7.6471	-
0.8728	289800	7.5959	-
0.8731	289900	7.5763	-
0.8734	290000	7.6128	-
0.8737	290100	7.5453	-
0.8740	290200	7.6315	-
0.8743	290300	7.6262	-
0.8746	290400	7.6137	-
0.8749	290500	7.6134	-
0.8752	290600	7.6302	-
0.8755	290700	7.6329	-
0.8758	290800	7.5985	-
0.8761	290900	7.6488	-
0.8764	291000	7.6322	-
0.8767	291100	7.5716	-
0.8770	291200	7.6252	-
0.8773	291300	7.6293	-
0.8776	291400	7.5727	-
0.8779	291500	7.6085	-
0.8782	291600	7.5726	-
0.8785	291700	7.6047	-
0.8788	291800	7.5609	-
0.8791	291900	7.6249	-
0.8794	292000	7.6008	-
0.8797	292100	7.6343	-
0.8800	292200	7.6267	-
0.8803	292300	7.6327	-
0.8806	292400	7.6531	-
0.8809	292500	7.6462	-
0.8812	292600	7.5981	-
0.8815	292700	7.5593	-
0.8818	292800	7.5896	-
0.8821	292900	7.6225	-
0.8824	293000	7.5625	-
0.8827	293100	7.5918	-
0.8830	293200	7.6204	-
0.8833	293300	7.6115	-
0.8836	293400	7.5453	-
0.8839	293500	7.6381	-
0.8842	293600	7.634	-
0.8845	293700	7.5891	-
0.8848	293800	7.6045	-
0.8851	293900	7.5968	-
0.8854	294000	7.7062	-
0.8857	294100	7.5846	-
0.8861	294200	7.6704	-
0.8864	294300	7.5832	-
0.8867	294400	7.6274	-
0.8870	294500	7.6125	-
0.8873	294600	7.6127	-
0.8876	294700	7.6095	-
0.8879	294800	7.6129	-
0.8882	294900	7.6353	-
0.8885	295000	7.5894	-
0.8888	295100	7.6099	-
0.8891	295200	7.6259	-
0.8894	295300	7.5599	-
0.8897	295400	7.6441	-
0.8900	295500	7.6679	-
0.8903	295600	7.5884	-
0.8906	295700	7.6393	-
0.8909	295800	7.6044	-
0.8912	295900	7.6155	-
0.8915	296000	7.6046	-
0.8918	296100	7.6157	-
0.8921	296200	7.6146	-
0.8924	296300	7.5975	-
0.8927	296400	7.5693	-
0.8930	296500	7.5937	-
0.8933	296600	7.5913	-
0.8936	296700	7.5676	-
0.8939	296800	7.611	-
0.8942	296900	7.6283	-
0.8945	297000	7.657	-
0.8948	297100	7.5838	-
0.8951	297200	7.5849	-
0.8954	297300	7.5921	-
0.8957	297400	7.6455	-
0.8960	297500	7.5649	-
0.8963	297600	7.584	-
0.8966	297700	7.6523	-
0.8969	297800	7.6094	-
0.8972	297900	7.5697	-
0.8975	298000	7.6476	-
0.8978	298100	7.6043	-
0.8981	298200	7.6013	-
0.8984	298300	7.6259	-
0.8987	298400	7.6216	-
0.8990	298500	7.612	-
0.8993	298600	7.555	-
0.8996	298700	7.5772	-
0.8999	298800	7.592	-
0.9002	298900	7.6627	-
0.9005	299000	7.5895	-
0.9008	299100	7.6301	-
0.9011	299200	7.5745	-
0.9014	299300	7.6201	-
0.9017	299400	7.6121	-
0.9020	299500	7.6393	-
0.9023	299600	7.6457	-
0.9026	299700	7.6234	-
0.9029	299800	7.5915	-
0.9032	299900	7.6508	-
0.9035	300000	7.5826	-
0.9038	300100	7.5793	-
0.9041	300200	7.602	-
0.9044	300300	7.5732	-
0.9047	300400	7.6375	-
0.9050	300500	7.5848	-
0.9053	300600	7.5719	-
0.9056	300700	7.6585	-
0.9059	300800	7.5933	-
0.9062	300900	7.5779	-
0.9065	301000	7.6202	-
0.9068	301100	7.6139	-
0.9071	301200	7.5858	-
0.9074	301300	7.607	-
0.9077	301400	7.6697	-
0.9080	301500	7.6764	-
0.9083	301600	7.5801	-
0.9086	301700	7.6109	-
0.9089	301800	7.6227	-
0.9092	301900	7.6377	-
0.9095	302000	7.5642	-
0.9098	302100	7.6441	-
0.9101	302200	7.5837	-
0.9104	302300	7.5928	-
0.9107	302400	7.5586	-
0.9110	302500	7.6026	-
0.9113	302600	7.5724	-
0.9117	302700	7.6141	-
0.9120	302800	7.6605	-
0.9123	302900	7.6024	-
0.9126	303000	7.578	-
0.9129	303100	7.5977	-
0.9132	303200	7.5734	-
0.9135	303300	7.6328	-
0.9138	303400	7.658	-
0.9141	303500	7.5993	-
0.9144	303600	7.6114	-
0.9147	303700	7.6315	-
0.9150	303800	7.5901	-
0.9153	303900	7.5811	-
0.9156	304000	7.6434	-
0.9159	304100	7.5809	-
0.9162	304200	7.5692	-
0.9165	304300	7.6127	-
0.9168	304400	7.5923	-
0.9171	304500	7.621	-
0.9174	304600	7.6016	-
0.9177	304700	7.6055	-
0.9180	304800	7.5905	-
0.9183	304900	7.632	-
0.9186	305000	7.6466	-
0.9189	305100	7.5659	-
0.9192	305200	7.5685	-
0.9195	305300	7.5977	-
0.9198	305400	7.5632	-
0.9201	305500	7.5929	-
0.9204	305600	7.6428	-
0.9207	305700	7.5776	-
0.9210	305800	7.6319	-
0.9213	305900	7.6294	-
0.9216	306000	7.5793	-
0.9219	306100	7.6294	-
0.9222	306200	7.5755	-
0.9225	306300	7.636	-
0.9228	306400	7.5934	-
0.9231	306500	7.6257	-
0.9234	306600	7.592	-
0.9237	306700	7.5771	-
0.9240	306800	7.6327	-
0.9243	306900	7.6179	-
0.9246	307000	7.6134	-
0.9249	307100	7.6309	-
0.9252	307200	7.5533	-
0.9255	307300	7.6399	-
0.9258	307400	7.577	-
0.9261	307500	7.5885	-
0.9264	307600	7.6096	-
0.9267	307700	7.6018	-
0.9270	307800	7.6036	-
0.9273	307900	7.6403	-
0.9276	308000	7.5927	-
0.9279	308100	7.6159	-
0.9282	308200	7.5911	-
0.9285	308300	7.6167	-
0.9288	308400	7.5863	-
0.9291	308500	7.574	-
0.9294	308600	7.6916	-
0.9297	308700	7.6479	-
0.9300	308800	7.6011	-
0.9303	308900	7.6138	-
0.9306	309000	7.6032	-
0.9309	309100	7.5661	-
0.9312	309200	7.6139	-
0.9315	309300	7.6769	-
0.9318	309400	7.6391	-
0.9321	309500	7.6697	-
0.9324	309600	7.6264	-
0.9327	309700	7.6169	-
0.9330	309800	7.5502	-
0.9333	309900	7.585	-
0.9336	310000	7.6105	-
0.9339	310100	7.5839	-
0.9342	310200	7.6067	-
0.9345	310300	7.6352	-
0.9348	310400	7.5961	-
0.9351	310500	7.5989	-
0.9354	310600	7.6079	-
0.9357	310700	7.5969	-
0.9360	310800	7.6125	-
0.9363	310900	7.5758	-
0.9366	311000	7.6555	-
0.9369	311100	7.5997	-
0.9373	311200	7.6023	-
0.9376	311300	7.5638	-
0.9379	311400	7.5778	-
0.9382	311500	7.6209	-
0.9385	311600	7.6217	-
0.9388	311700	7.6006	-
0.9391	311800	7.5926	-
0.9394	311900	7.5836	-
0.9397	312000	7.5839	-
0.9400	312100	7.5843	-
0.9403	312200	7.5838	-
0.9406	312300	7.5651	-
0.9409	312400	7.6141	-
0.9412	312500	7.5852	-
0.9415	312600	7.5967	-
0.9418	312700	7.6165	-
0.9421	312800	7.6185	-
0.9424	312900	7.5744	-
0.9427	313000	7.6286	-
0.9430	313100	7.5632	-
0.9433	313200	7.6189	-
0.9436	313300	7.6436	-
0.9439	313400	7.6087	-
0.9442	313500	7.5972	-
0.9445	313600	7.6286	-
0.9448	313700	7.5889	-
0.9451	313800	7.6105	-
0.9454	313900	7.6498	-
0.9457	314000	7.6012	-
0.9460	314100	7.6016	-
0.9463	314200	7.6055	-
0.9466	314300	7.5944	-
0.9469	314400	7.6118	-
0.9472	314500	7.6185	-
0.9475	314600	7.6245	-
0.9478	314700	7.6343	-
0.9481	314800	7.5689	-
0.9484	314900	7.6299	-
0.9487	315000	7.5983	-
0.9490	315100	7.6191	-
0.9493	315200	7.623	-
0.9496	315300	7.6239	-
0.9499	315400	7.5895	-
0.9502	315500	7.6321	-
0.9505	315600	7.6148	-
0.9508	315700	7.6041	-
0.9511	315800	7.5883	-
0.9514	315900	7.6234	-
0.9517	316000	7.6122	-
0.9520	316100	7.5889	-
0.9523	316200	7.5921	-
0.9526	316300	7.6201	-
0.9529	316400	7.6492	-
0.9532	316500	7.6454	-
0.9535	316600	7.5762	-
0.9538	316700	7.6201	-
0.9541	316800	7.6329	-
0.9544	316900	7.5998	-
0.9547	317000	7.5904	-
0.9550	317100	7.6228	-
0.9553	317200	7.6297	-
0.9556	317300	7.5642	-
0.9559	317400	7.6523	-
0.9562	317500	7.5863	-
0.9565	317600	7.6795	-
0.9568	317700	7.5645	-
0.9571	317800	7.5937	-
0.9574	317900	7.6439	-
0.9577	318000	7.6113	-
0.9580	318100	7.6105	-
0.9583	318200	7.6358	-
0.9586	318300	7.5878	-
0.9589	318400	7.6394	-
0.9592	318500	7.6129	-
0.9595	318600	7.578	-
0.9598	318700	7.6304	-
0.9601	318800	7.6083	-
0.9604	318900	7.5938	-
0.9607	319000	7.5985	-
0.9610	319100	7.5676	-
0.9613	319200	7.6104	-
0.9616	319300	7.6373	-
0.9619	319400	7.6054	-
0.9622	319500	7.5721	-
0.9625	319600	7.5665	-
0.9629	319700	7.5951	-
0.9632	319800	7.5857	-
0.9635	319900	7.6163	-
0.9638	320000	7.6036	-
0.9641	320100	7.6359	-
0.9644	320200	7.6532	-
0.9647	320300	7.6479	-
0.9650	320400	7.5908	-
0.9653	320500	7.5845	-
0.9656	320600	7.6105	-
0.9659	320700	7.6464	-
0.9662	320800	7.6213	-
0.9665	320900	7.573	-
0.9668	321000	7.6244	-
0.9671	321100	7.6373	-
0.9674	321200	7.56	-
0.9677	321300	7.577	-
0.9680	321400	7.5985	-
0.9683	321500	7.6525	-
0.9686	321600	7.6736	-
0.9689	321700	7.6686	-
0.9692	321800	7.5746	-
0.9695	321900	7.5597	-
0.9698	322000	7.5783	-
0.9701	322100	7.5939	-
0.9704	322200	7.546	-
0.9707	322300	7.6098	-
0.9710	322400	7.6292	-
0.9713	322500	7.5756	-
0.9716	322600	7.5842	-
0.9719	322700	7.6392	-
0.9722	322800	7.5363	-
0.9725	322900	7.5594	-
0.9728	323000	7.5909	-
0.9731	323100	7.5998	-
0.9734	323200	7.5408	-
0.9737	323300	7.6055	-
0.9740	323400	7.6458	-
0.9743	323500	7.5943	-
0.9746	323600	7.6445	-
0.9749	323700	7.6143	-
0.9752	323800	7.6482	-
0.9755	323900	7.5879	-
0.9758	324000	7.6059	-
0.9761	324100	7.5753	-
0.9764	324200	7.5731	-
0.9767	324300	7.5934	-
0.9770	324400	7.6099	-
0.9773	324500	7.6028	-
0.9776	324600	7.5739	-
0.9779	324700	7.6119	-
0.9782	324800	7.5986	-
0.9785	324900	7.6135	-
0.9788	325000	7.566	-
0.9791	325100	7.6584	-
0.9794	325200	7.5955	-
0.9797	325300	7.6381	-
0.9800	325400	7.5679	-
0.9803	325500	7.5675	-
0.9806	325600	7.5864	-
0.9809	325700	7.6142	-
0.9812	325800	7.5831	-
0.9815	325900	7.6115	-
0.9818	326000	7.5885	-
0.9821	326100	7.5961	-
0.9824	326200	7.6133	-
0.9827	326300	7.6212	-
0.9830	326400	7.5626	-
0.9833	326500	7.5785	-
0.9836	326600	7.6857	-
0.9839	326700	7.597	-
0.9842	326800	7.6011	-
0.9845	326900	7.5861	-
0.9848	327000	7.5884	-
0.9851	327100	7.6057	-
0.9854	327200	7.617	-
0.9857	327300	7.5807	-
0.9860	327400	7.6087	-
0.9863	327500	7.567	-
0.9866	327600	7.5842	-
0.9869	327700	7.6134	-
0.9872	327800	7.5767	-
0.9875	327900	7.5814	-
0.9878	328000	7.5986	-
0.9881	328100	7.6108	-
0.9885	328200	7.5743	-
0.9888	328300	7.6121	-
0.9891	328400	7.6848	-
0.9894	328500	7.595	-
0.9897	328600	7.6206	-
0.9900	328700	7.5916	-
0.9903	328800	7.5963	-
0.9906	328900	7.5808	-
0.9909	329000	7.605	-
0.9912	329100	7.5752	-
0.9915	329200	7.5783	-
0.9918	329300	7.5998	-
0.9921	329400	7.5785	-
0.9924	329500	7.5636	-
0.9927	329600	7.5847	-
0.9930	329700	7.5843	-
0.9933	329800	7.6309	-
0.9936	329900	7.5779	-
0.9939	330000	7.5518	-
0.9942	330100	7.5657	-
0.9945	330200	7.6162	-
0.9948	330300	7.6326	-
0.9951	330400	7.6319	-
0.9954	330500	7.5712	-
0.9957	330600	7.6288	-
0.9960	330700	7.6665	-
0.9963	330800	7.5786	-
0.9966	330900	7.5752	-
0.9969	331000	7.6303	-
0.9972	331100	7.6687	-
0.9975	331200	7.5742	-
0.9978	331300	7.6234	-
0.9981	331400	7.5756	-
0.9984	331500	7.6273	-
0.9987	331600	7.5816	-
0.9990	331700	7.5853	-
0.9993	331800	7.545	-
0.9996	331900	7.6314	-
0.9999	332000	7.622	-

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},
}