all-MiniLM-L6-v21-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v20-pair_score on the pairs_with_scores_v17_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-v20-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v17_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 = [
    'wooden mirror',
    'impact resistant bowl',
    'uv protection serum',
]
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, 0.4364, 0.3510],
#         [0.4364, 1.0000, 0.3455],
#         [0.3510, 0.3455, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v17_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v17_tag_sim_synonyms_typo_augmented at f4b4a46
• Size: 42,510,873 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: 19 tokens	min: 3 tokens mean: 6.73 tokens max: 115 tokens	min: 0.14 mean: 0.29 max: 1.0

• Samples:

  sentence1	sentence2	score
  elasticated waist skirt	pottery bowl	0.21
  crunchy almond	potttery madness mug	0.2
  always	centra collection glassware air bubble base drinkware	0.2

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v17_tag_sim_synonyms_typo_augmented

• Dataset: pairs_with_scores_v17_tag_sim_synonyms_typo_augmented at f4b4a46
• Size: 213,623 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: 6.22 tokens max: 45 tokens	min: 3 tokens mean: 6.55 tokens max: 105 tokens	min: 0.14 mean: 0.3 max: 1.0

• Samples:

  sentence1	sentence2	score
  apple muffin for kids	microwave safe plate set	0.25
  summer pqnts	light texture lipp balm	0.27
  hilk dry cat food	modern clutch	0.19

• 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	7.6308	-
0.0006	200	7.6371	-
0.0009	300	7.6316	-
0.0012	400	7.6294	-
0.0015	500	7.6041	-
0.0018	600	7.6455	-
0.0021	700	7.6331	-
0.0024	800	7.6192	-
0.0027	900	7.6658	-
0.0030	1000	7.6135	-
0.0033	1100	7.6292	-
0.0036	1200	7.5708	-
0.0039	1300	7.654	-
0.0042	1400	7.631	-
0.0045	1500	7.6994	-
0.0048	1600	7.5803	-
0.0051	1700	7.6061	-
0.0054	1800	7.5566	-
0.0057	1900	7.5778	-
0.0060	2000	7.5697	-
0.0063	2100	7.6666	-
0.0066	2200	7.6113	-
0.0069	2300	7.6425	-
0.0072	2400	7.6225	-
0.0075	2500	7.601	-
0.0078	2600	7.6311	-
0.0081	2700	7.6149	-
0.0084	2800	7.6265	-
0.0087	2900	7.5862	-
0.0090	3000	7.5768	-
0.0093	3100	7.567	-
0.0096	3200	7.5821	-
0.0099	3300	7.584	-
0.0102	3400	7.5668	-
0.0105	3500	7.6694	-
0.0108	3600	7.6745	-
0.0111	3700	7.6596	-
0.0114	3800	7.5637	-
0.0117	3900	7.6282	-
0.0120	4000	7.5851	-
0.0123	4100	7.6565	-
0.0126	4200	7.6161	-
0.0129	4300	7.6462	-
0.0132	4400	7.5954	-
0.0135	4500	7.5711	-
0.0139	4600	7.6026	-
0.0142	4700	7.7095	-
0.0145	4800	7.6165	-
0.0148	4900	7.6232	-
0.0151	5000	7.5623	-
0.0154	5100	7.5454	-
0.0157	5200	7.6551	-
0.0160	5300	7.6378	-
0.0163	5400	7.5849	-
0.0166	5500	7.5938	-
0.0169	5600	7.6079	-
0.0172	5700	7.5703	-
0.0175	5800	7.5987	-
0.0178	5900	7.5998	-
0.0181	6000	7.6921	-
0.0184	6100	7.6576	-
0.0187	6200	7.5852	-
0.0190	6300	7.6381	-
0.0193	6400	7.6119	-
0.0196	6500	7.5864	-
0.0199	6600	7.5809	-
0.0202	6700	7.696	-
0.0205	6800	7.5789	-
0.0208	6900	7.592	-
0.0211	7000	7.6444	-
0.0214	7100	7.6019	-
0.0217	7200	7.5919	-
0.0220	7300	7.6534	-
0.0223	7400	7.6398	-
0.0226	7500	7.5976	-
0.0229	7600	7.5861	-
0.0232	7700	7.6168	-
0.0235	7800	7.6387	-
0.0238	7900	7.5798	-
0.0241	8000	7.6401	-
0.0244	8100	7.5917	-
0.0247	8200	7.6276	-
0.0250	8300	7.6425	-
0.0253	8400	7.6246	-
0.0256	8500	7.6182	-
0.0259	8600	7.7071	-
0.0262	8700	7.5663	-
0.0265	8800	7.6511	-
0.0268	8900	7.6215	-
0.0271	9000	7.6053	-
0.0274	9100	7.6382	-
0.0277	9200	7.5814	-
0.0280	9300	7.6329	-
0.0283	9400	7.6104	-
0.0286	9500	7.6433	-
0.0289	9600	7.6233	-
0.0292	9700	7.6594	-
0.0295	9800	7.6336	-
0.0298	9900	7.6442	-
0.0301	10000	7.6532	-
0.0304	10100	7.5864	-
0.0307	10200	7.6175	-
0.0310	10300	7.5804	-
0.0313	10400	7.667	-
0.0316	10500	7.5732	-
0.0319	10600	7.6775	-
0.0322	10700	7.5949	-
0.0325	10800	7.5936	-
0.0328	10900	7.6235	-
0.0331	11000	7.6178	-
0.0334	11100	7.6265	-
0.0337	11200	7.6113	-
0.0340	11300	7.5753	-
0.0343	11400	7.5586	-
0.0346	11500	7.5823	-
0.0349	11600	7.6064	-
0.0352	11700	7.6304	-
0.0355	11800	7.6231	-
0.0358	11900	7.6582	-
0.0361	12000	7.5918	-
0.0364	12100	7.5791	-
0.0367	12200	7.5962	-
0.0370	12300	7.6202	-
0.0373	12400	7.655	-
0.0376	12500	7.6543	-
0.0379	12600	7.5928	-
0.0382	12700	7.6093	-
0.0385	12800	7.6295	-
0.0388	12900	7.6445	-
0.0391	13000	7.6523	-
0.0394	13100	7.6756	-
0.0397	13200	7.6178	-
0.0400	13300	7.6057	-
0.0403	13400	7.6137	-
0.0406	13500	7.6095	-
0.0409	13600	7.6014	-
0.0413	13700	7.5931	-
0.0416	13800	7.5746	-
0.0419	13900	7.6082	-
0.0422	14000	7.6115	-
0.0425	14100	7.6505	-
0.0428	14200	7.5974	-
0.0431	14300	7.5917	-
0.0434	14400	7.5982	-
0.0437	14500	7.5589	-
0.0440	14600	7.6568	-
0.0443	14700	7.6135	-
0.0446	14800	7.6058	-
0.0449	14900	7.6431	-
0.0452	15000	7.613	-
0.0455	15100	7.628	-
0.0458	15200	7.6159	-
0.0461	15300	7.656	-
0.0464	15400	7.5804	-
0.0467	15500	7.6189	-
0.0470	15600	7.5954	-
0.0473	15700	7.6066	-
0.0476	15800	7.6244	-
0.0479	15900	7.6132	-
0.0482	16000	7.5995	-
0.0485	16100	7.6704	-
0.0488	16200	7.6675	-
0.0491	16300	7.6281	-
0.0494	16400	7.6256	-
0.0497	16500	7.6863	-
0.0500	16600	7.6036	-
0.0503	16700	7.6323	-
0.0506	16800	7.6285	-
0.0509	16900	7.6464	-
0.0512	17000	7.6322	-
0.0515	17100	7.6098	-
0.0518	17200	7.5716	-
0.0521	17300	7.6378	-
0.0524	17400	7.6034	-
0.0527	17500	7.6626	-
0.0530	17600	7.5899	-
0.0533	17700	7.6363	-
0.0536	17800	7.638	-
0.0539	17900	7.6181	-
0.0542	18000	7.642	-
0.0545	18100	7.6485	-
0.0548	18200	7.6176	-
0.0551	18300	7.6356	-
0.0554	18400	7.5955	-
0.0557	18500	7.6115	-
0.0560	18600	7.633	-
0.0563	18700	7.6064	-
0.0566	18800	7.638	-
0.0569	18900	7.6266	-
0.0572	19000	7.601	-
0.0575	19100	7.645	-
0.0578	19200	7.6602	-
0.0581	19300	7.6116	-
0.0584	19400	7.6007	-
0.0587	19500	7.6457	-
0.0590	19600	7.6195	-
0.0593	19700	7.6237	-
0.0596	19800	7.6245	-
0.0599	19900	7.5999	-
0.0602	20000	7.6271	-
0.0605	20100	7.6042	-
0.0608	20200	7.6259	-
0.0611	20300	7.6378	-
0.0614	20400	7.633	-
0.0617	20500	7.631	-
0.0620	20600	7.6233	-
0.0623	20700	7.6204	-
0.0626	20800	7.6024	-
0.0629	20900	7.6061	-
0.0632	21000	7.6419	-
0.0635	21100	7.6671	-
0.0638	21200	7.6226	-
0.0641	21300	7.5853	-
0.0644	21400	7.6574	-
0.0647	21500	7.6089	-
0.0650	21600	7.6002	-
0.0653	21700	7.6857	-
0.0656	21800	7.6307	-
0.0659	21900	7.6109	-
0.0662	22000	7.5945	-
0.0665	22100	7.6158	-
0.0668	22200	7.645	-
0.0671	22300	7.6371	-
0.0674	22400	7.6391	-
0.0677	22500	7.6406	-
0.0680	22600	7.6399	-
0.0683	22700	7.6207	-
0.0687	22800	7.6249	-
0.0690	22900	7.6309	-
0.0693	23000	7.6048	-
0.0696	23100	7.6497	-
0.0699	23200	7.6424	-
0.0702	23300	7.6207	-
0.0705	23400	7.6619	-
0.0708	23500	7.6416	-
0.0711	23600	7.6309	-
0.0714	23700	7.6439	-
0.0717	23800	7.624	-
0.0720	23900	7.6191	-
0.0723	24000	7.6047	-
0.0726	24100	7.6635	-
0.0729	24200	7.6339	-
0.0732	24300	7.6705	-
0.0735	24400	7.6689	-
0.0738	24500	7.6276	-
0.0741	24600	7.6231	-
0.0744	24700	7.6329	-
0.0747	24800	7.5914	-
0.0750	24900	7.6025	-
0.0753	25000	7.5848	-
0.0756	25100	7.622	-
0.0759	25200	7.6373	-
0.0762	25300	7.6253	-
0.0765	25400	7.6396	-
0.0768	25500	7.6643	-
0.0771	25600	7.6712	-
0.0774	25700	7.5941	-
0.0777	25800	7.647	-
0.0780	25900	7.6188	-
0.0783	26000	7.6434	-
0.0786	26100	7.6103	-
0.0789	26200	7.5736	-
0.0792	26300	7.6321	-
0.0795	26400	7.7038	-
0.0798	26500	7.5918	-
0.0801	26600	7.6142	-
0.0804	26700	7.6938	-
0.0807	26800	7.6469	-
0.0810	26900	7.6991	-
0.0813	27000	7.6252	-
0.0816	27100	7.6213	-
0.0819	27200	7.6364	-
0.0822	27300	7.6212	-
0.0825	27400	7.6157	-
0.0828	27500	7.642	-
0.0831	27600	7.6735	-
0.0834	27700	7.6592	-
0.0837	27800	7.6333	-
0.0840	27900	7.64	-
0.0843	28000	7.6397	-
0.0846	28100	7.644	-
0.0849	28200	7.6496	-
0.0852	28300	7.6339	-
0.0855	28400	7.6429	-
0.0858	28500	7.6445	-
0.0861	28600	7.6123	-
0.0864	28700	7.6567	-
0.0867	28800	7.6417	-
0.0870	28900	7.6522	-
0.0873	29000	7.6689	-
0.0876	29100	7.6916	-
0.0879	29200	7.6044	-
0.0882	29300	7.6314	-
0.0885	29400	7.6054	-
0.0888	29500	7.6439	-
0.0891	29600	7.6435	-
0.0894	29700	7.6151	-
0.0897	29800	7.6341	-
0.0900	29900	7.6035	-
0.0903	30000	7.6519	-
0.0906	30100	7.6077	-
0.0909	30200	7.6519	-
0.0912	30300	7.6541	-
0.0915	30400	7.6458	-
0.0918	30500	7.6263	-
0.0921	30600	7.6855	-
0.0924	30700	7.6897	-
0.0927	30800	7.6712	-
0.0930	30900	7.6336	-
0.0933	31000	7.6323	-
0.0936	31100	7.6247	-
0.0939	31200	7.6533	-
0.0942	31300	7.633	-
0.0945	31400	7.7107	-
0.0948	31500	7.6422	-
0.0951	31600	7.6406	-
0.0954	31700	7.6089	-
0.0957	31800	7.6394	-
0.0961	31900	7.64	-
0.0964	32000	7.6398	-
0.0967	32100	7.6731	-
0.0970	32200	7.646	-
0.0973	32300	7.623	-
0.0976	32400	7.6383	-
0.0979	32500	7.6299	-
0.0982	32600	7.6477	-
0.0985	32700	7.6191	-
0.0988	32800	7.6658	-
0.0991	32900	7.642	-
0.0994	33000	7.6686	-
0.0997	33100	7.6487	-
0.1000	33200	7.6489	-
0.1003	33300	7.6333	-
0.1006	33400	7.627	-
0.1009	33500	7.6815	-
0.1012	33600	7.642	-
0.1015	33700	7.6195	-
0.1018	33800	7.6271	-
0.1021	33900	7.6472	-
0.1024	34000	7.679	-
0.1027	34100	7.6259	-
0.1030	34200	7.6383	-
0.1033	34300	7.6431	-
0.1036	34400	7.6507	-
0.1039	34500	7.6118	-
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0.1921	63800	7.6478	-
0.1924	63900	7.642	-
0.1927	64000	7.6453	-
0.1930	64100	7.6245	-
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0.1942	64500	7.6767	-
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0.1951	64800	7.6248	-
0.1954	64900	7.6222	-
0.1957	65000	7.6474	-
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0.1969	65400	7.6322	-
0.1972	65500	7.6443	-
0.1975	65600	7.6133	-
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0.1981	65800	7.6041	-
0.1984	65900	7.6436	-
0.1987	66000	7.6236	-
0.1990	66100	7.5995	-
0.1993	66200	7.6649	-
0.1996	66300	7.626	-
0.1999	66400	7.6307	-
0.2002	66500	7.6323	-
0.2005	66600	7.6159	-
0.2008	66700	7.6095	-
0.2011	66800	7.6025	-
0.2014	66900	7.6197	-
0.2017	67000	7.6732	-
0.2020	67100	7.5989	-
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0.5552	184400	7.5679	-
0.5555	184500	7.5958	-
0.5558	184600	7.5703	-
0.5561	184700	7.5988	-
0.5564	184800	7.6231	-
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0.5570	185000	7.5916	-
0.5573	185100	7.6308	-
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0.5591	185700	7.6066	-
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0.5600	186000	7.6585	-
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0.5685	188800	7.5925	-
0.5688	188900	7.6166	-
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0.5700	189300	7.5847	-
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0.5727	190200	7.5878	-
0.5730	190300	7.6036	-
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0.5760	191300	7.5732	-
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0.5781	192000	7.5828	-
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0.5823	193400	7.6527	-
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0.5841	194000	7.6189	-
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0.5862	194700	7.5884	-
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0.5871	195000	7.5549	-
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0.5880	195300	7.6039	-
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0.5902	196000	7.5592	-
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0.5911	196300	7.6158	-
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0.5920	196600	7.6407	-
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0.5926	196800	7.5818	-
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0.5932	197000	7.5883	-
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0.5938	197200	7.5891	-
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0.5947	197500	7.5856	-
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0.5962	198000	7.5912	-
0.5965	198100	7.6791	-
0.5968	198200	7.5697	-
0.5971	198300	7.5873	-
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0.5977	198500	7.6015	-
0.5980	198600	7.5459	-
0.5983	198700	7.5545	-
0.5986	198800	7.6268	-
0.5989	198900	7.5778	-
0.5992	199000	7.5584	-
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0.6001	199300	7.5939	-
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0.6007	199500	7.5943	-
0.6010	199600	7.5892	-
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0.6022	200000	7.5453	7.4895
0.6025	200100	7.5805	-
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0.6031	200300	7.5584	-
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0.6040	200600	7.5847	-
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0.6067	201500	7.5497	-
0.6070	201600	7.6027	-
0.6073	201700	7.5755	-
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0.8762	291000	7.5463	-
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0.9623	319600	7.5501	-
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0.9632	319900	7.5363	-
0.9635	320000	7.5456	-
0.9638	320100	7.5257	-
0.9641	320200	7.5628	-
0.9644	320300	7.56	-
0.9647	320400	7.5952	-
0.9650	320500	7.561	-
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0.9665	321000	7.5585	-
0.9668	321100	7.5485	-
0.9671	321200	7.5479	-
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0.9677	321400	7.5316	-
0.9680	321500	7.5684	-
0.9683	321600	7.5399	-
0.9686	321700	7.5844	-
0.9689	321800	7.6115	-
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0.9747	323700	7.5161	-
0.9750	323800	7.5488	-
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0.9765	324300	7.5211	-
0.9768	324400	7.5965	-
0.9771	324500	7.5595	-
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0.9780	324800	7.5785	-
0.9783	324900	7.5649	-
0.9786	325000	7.608	-
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0.9801	325500	7.5592	-
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0.9819	326100	7.5507	-
0.9822	326200	7.5737	-
0.9825	326300	7.5616	-
0.9828	326400	7.5451	-
0.9831	326500	7.5743	-
0.9834	326600	7.5702	-
0.9837	326700	7.5466	-
0.9840	326800	7.5913	-
0.9843	326900	7.5837	-
0.9846	327000	7.5403	-
0.9849	327100	7.5598	-
0.9852	327200	7.5549	-
0.9855	327300	7.5858	-
0.9858	327400	7.5199	-
0.9861	327500	7.5264	-
0.9864	327600	7.6027	-
0.9867	327700	7.5451	-
0.9870	327800	7.5451	-
0.9873	327900	7.6331	-
0.9876	328000	7.4803	-
0.9879	328100	7.5386	-
0.9882	328200	7.509	-
0.9885	328300	7.5585	-
0.9888	328400	7.5792	-
0.9891	328500	7.5167	-
0.9894	328600	7.5699	-
0.9897	328700	7.5426	-
0.9900	328800	7.5621	-
0.9903	328900	7.5369	-
0.9906	329000	7.5325	-
0.9909	329100	7.5628	-
0.9912	329200	7.5903	-
0.9915	329300	7.5664	-
0.9918	329400	7.5967	-
0.9921	329500	7.5678	-
0.9924	329600	7.5273	-
0.9927	329700	7.6059	-
0.9930	329800	7.6511	-
0.9933	329900	7.5565	-
0.9936	330000	7.5272	-
0.9939	330100	7.5549	-
0.9942	330200	7.5675	-
0.9945	330300	7.5454	-
0.9948	330400	7.5513	-
0.9951	330500	7.5694	-
0.9954	330600	7.5062	-
0.9957	330700	7.5566	-
0.9960	330800	7.5436	-
0.9963	330900	7.5601	-
0.9966	331000	7.5079	-
0.9969	331100	7.5702	-
0.9972	331200	7.6372	-
0.9975	331300	7.5362	-
0.9978	331400	7.6198	-
0.9981	331500	7.5657	-
0.9984	331600	7.6056	-
0.9987	331700	7.5695	-
0.9990	331800	7.5621	-
0.9993	331900	7.5706	-
0.9996	332000	7.5785	-
0.9999	332100	7.5215	-

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},
}