all-MiniLM-L6-v25-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v24-pair_score on the pairs_with_scores_v21_subcategory_added 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-v24-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v21_subcategory_added
• 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 = [
    'coconut oil body wash',
    'proxera cream',
    'never give up tshirt',
]
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, 0.6422, 0.4017],
#         [0.6422, 1.0000, 0.4948],
#         [0.4017, 0.4948, 1.0000]])

Training Details

Training Dataset

pairs_with_scores_v21_subcategory_added

• Dataset: pairs_with_scores_v21_subcategory_added at e846686
• Size: 90,103,985 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: 5.9 tokens max: 16 tokens	min: 3 tokens mean: 6.09 tokens max: 32 tokens	min: 0.15 mean: 0.44 max: 1.0

• Samples:

  sentence1	sentence2	score
  sephora body jelly	lips moisturizer	0.8327301740646362
  gym bottoms	natural fairness cream	0.2015494257211685
  mahshy	olive oil labna	0.209200233221054

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

pairs_with_scores_v21_subcategory_added

• Dataset: pairs_with_scores_v21_subcategory_added at e846686
• Size: 452,784 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: 5.94 tokens max: 49 tokens	min: 3 tokens mean: 5.95 tokens max: 29 tokens	min: 0.13 mean: 0.44 max: 1.0

• Samples:

  sentence1	sentence2	score
  pet shampoo for dogs & cats	cat wet wipes	0.8230481147766113
  face solution	vitamin face serum	0.8398916721343994
  diabetic friendly burger	mouse killer	0.1937324851751327

• 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
0.0001	100	12.8797	-
0.0003	200	12.7386	-
0.0004	300	12.8941	-
0.0006	400	12.9812	-
0.0007	500	12.6754	-
0.0009	600	12.6543	-
0.0010	700	12.433	-
0.0011	800	12.6393	-
0.0013	900	12.3547	-
0.0014	1000	12.4245	-
0.0016	1100	12.175	-
0.0017	1200	12.1567	-
0.0018	1300	12.1041	-
0.0020	1400	11.8394	-
0.0021	1500	11.7935	-
0.0023	1600	11.8706	-
0.0024	1700	11.5185	-
0.0026	1800	11.4821	-
0.0027	1900	11.4654	-
0.0028	2000	11.1878	-
0.0030	2100	11.3722	-
0.0031	2200	11.127	-
0.0033	2300	11.1537	-
0.0034	2400	10.9094	-
0.0036	2500	10.9864	-
0.0037	2600	10.7644	-
0.0038	2700	10.7725	-
0.0040	2800	10.5104	-
0.0041	2900	10.5577	-
0.0043	3000	10.5609	-
0.0044	3100	10.4292	-
0.0045	3200	10.3354	-
0.0047	3300	10.3423	-
0.0048	3400	10.398	-
0.0050	3500	10.4941	-
0.0051	3600	10.3296	-
0.0053	3700	10.2956	-
0.0054	3800	10.075	-
0.0055	3900	10.1718	-
0.0057	4000	10.0135	-
0.0058	4100	10.0241	-
0.0060	4200	9.8107	-
0.0061	4300	9.8966	-
0.0063	4400	9.7818	-
0.0064	4500	9.5902	-
0.0065	4600	9.5078	-
0.0067	4700	9.4763	-
0.0068	4800	9.4631	-
0.0070	4900	9.2127	-
0.0071	5000	9.1605	-
0.0072	5100	9.1179	-
0.0074	5200	8.9981	-
0.0075	5300	8.8844	-
0.0077	5400	8.8112	-
0.0078	5500	8.6897	-
0.0080	5600	8.642	-
0.0081	5700	8.5767	-
0.0082	5800	8.5181	-
0.0084	5900	8.5069	-
0.0085	6000	8.47	-
0.0087	6100	8.446	-
0.0088	6200	8.4183	-
0.0089	6300	8.4276	-
0.0091	6400	8.4203	-
0.0092	6500	8.3965	-
0.0094	6600	8.3805	-
0.0095	6700	8.3716	-
0.0097	6800	8.3782	-
0.0098	6900	8.3756	-
0.0099	7000	8.3674	-
0.0101	7100	8.3584	-
0.0102	7200	8.3415	-
0.0104	7300	8.3635	-
0.0105	7400	8.3533	-
0.0107	7500	8.3407	-
0.0108	7600	8.3423	-
0.0109	7700	8.3264	-
0.0111	7800	8.3187	-
0.0112	7900	8.3255	-
0.0114	8000	8.3129	-
0.0115	8100	8.328	-
0.0116	8200	8.3092	-
0.0118	8300	8.3285	-
0.0119	8400	8.3104	-
0.0121	8500	8.3005	-
0.0122	8600	8.2963	-
0.0124	8700	8.3018	-
0.0125	8800	8.3068	-
0.0126	8900	8.2953	-
0.0128	9000	8.2646	-
0.0129	9100	8.2832	-
0.0131	9200	8.2839	-
0.0132	9300	8.2588	-
0.0134	9400	8.28	-
0.0135	9500	8.2825	-
0.0136	9600	8.2932	-
0.0138	9700	8.2749	-
0.0139	9800	8.2639	-
0.0141	9900	8.2807	-
0.0142	10000	8.2705	-
0.0143	10100	8.2544	-
0.0145	10200	8.2722	-
0.0146	10300	8.2642	-
0.0148	10400	8.2551	-
0.0149	10500	8.2649	-
0.0151	10600	8.2497	-
0.0152	10700	8.2481	-
0.0153	10800	8.2688	-
0.0155	10900	8.235	-
0.0156	11000	8.2559	-
0.0158	11100	8.2342	-
0.0159	11200	8.2567	-
0.0161	11300	8.2242	-
0.0162	11400	8.2335	-
0.0163	11500	8.2614	-
0.0165	11600	8.2293	-
0.0166	11700	8.2415	-
0.0168	11800	8.2288	-
0.0169	11900	8.2403	-
0.0170	12000	8.2197	-
0.0172	12100	8.2172	-
0.0173	12200	8.2316	-
0.0175	12300	8.2397	-
0.0176	12400	8.2381	-
0.0178	12500	8.2341	-
0.0179	12600	8.2128	-
0.0180	12700	8.2285	-
0.0182	12800	8.2097	-
0.0183	12900	8.2111	-
0.0185	13000	8.2158	-
0.0186	13100	8.1993	-
0.0188	13200	8.2317	-
0.0189	13300	8.2195	-
0.0190	13400	8.2052	-
0.0192	13500	8.1861	-
0.0193	13600	8.2301	-
0.0195	13700	8.2149	-
0.0196	13800	8.2132	-
0.0197	13900	8.1969	-
0.0199	14000	8.1893	-
0.0200	14100	8.2118	-
0.0202	14200	8.212	-
0.0203	14300	8.2071	-
0.0205	14400	8.1876	-
0.0206	14500	8.2038	-
0.0207	14600	8.1939	-
0.0209	14700	8.1837	-
0.0210	14800	8.1837	-
0.0212	14900	8.203	-
0.0213	15000	8.1825	-
0.0215	15100	8.1712	-
0.0216	15200	8.1985	-
0.0217	15300	8.1934	-
0.0219	15400	8.183	-
0.0220	15500	8.2024	-
0.0222	15600	8.1631	-
0.0223	15700	8.1718	-
0.0224	15800	8.1816	-
0.0226	15900	8.1983	-
0.0227	16000	8.1777	-
0.0229	16100	8.1805	-
0.0230	16200	8.1865	-
0.0232	16300	8.1918	-
0.0233	16400	8.1747	-
0.0234	16500	8.1811	-
0.0236	16600	8.176	-
0.0237	16700	8.1675	-
0.0239	16800	8.1621	-
0.0240	16900	8.1705	-
0.0241	17000	8.18	-
0.0243	17100	8.167	-
0.0244	17200	8.1588	-
0.0246	17300	8.1673	-
0.0247	17400	8.171	-
0.0249	17500	8.1692	-
0.0250	17600	8.1555	-
0.0251	17700	8.1728	-
0.0253	17800	8.1639	-
0.0254	17900	8.1526	-
0.0256	18000	8.1725	-
0.0257	18100	8.1543	-
0.0259	18200	8.155	-
0.0260	18300	8.1672	-
0.0261	18400	8.1605	-
0.0263	18500	8.1544	-
0.0264	18600	8.1597	-
0.0266	18700	8.1692	-
0.0267	18800	8.1627	-
0.0268	18900	8.1394	-
0.0270	19000	8.1487	-
0.0271	19100	8.1518	-
0.0273	19200	8.1522	-
0.0274	19300	8.1537	-
0.0276	19400	8.1484	-
0.0277	19500	8.1471	-
0.0278	19600	8.1468	-
0.0280	19700	8.1536	-
0.0281	19800	8.1376	-
0.0283	19900	8.1422	-
0.0284	20000	8.1447	-
0.0286	20100	8.141	-
0.0287	20200	8.1637	-
0.0288	20300	8.1222	-
0.0290	20400	8.1365	-
0.0291	20500	8.1371	-
0.0293	20600	8.1354	-
0.0294	20700	8.1343	-
0.0295	20800	8.1454	-
0.0297	20900	8.1154	-
0.0298	21000	8.1515	-
0.0300	21100	8.1371	-
0.0301	21200	8.1402	-
0.0303	21300	8.1448	-
0.0304	21400	8.1311	-
0.0305	21500	8.1233	-
0.0307	21600	8.1186	-
0.0308	21700	8.126	-
0.0310	21800	8.119	-
0.0311	21900	8.131	-
0.0313	22000	8.1356	-
0.0314	22100	8.1381	-
0.0315	22200	8.1255	-
0.0317	22300	8.126	-
0.0318	22400	8.1102	-
0.0320	22500	8.1082	-
0.0321	22600	8.1111	-
0.0322	22700	8.1172	-
0.0324	22800	8.1282	-
0.0325	22900	8.0845	-
0.0327	23000	8.1191	-
0.0328	23100	8.1133	-
0.0330	23200	8.1073	-
0.0331	23300	8.1106	-
0.0332	23400	8.1138	-
0.0334	23500	8.0943	-
0.0335	23600	8.1125	-
0.0337	23700	8.1278	-
0.0338	23800	8.1178	-
0.0340	23900	8.1069	-
0.0341	24000	8.1183	-
0.0342	24100	8.1101	-
0.0344	24200	8.1043	-
0.0345	24300	8.0993	-
0.0347	24400	8.1198	-
0.0348	24500	8.094	-
0.0349	24600	8.1163	-
0.0351	24700	8.0876	-
0.0352	24800	8.0946	-
0.0354	24900	8.0915	-
0.0355	25000	8.0997	-
0.0357	25100	8.1226	-
0.0358	25200	8.0921	-
0.0359	25300	8.0927	-
0.0361	25400	8.0863	-
0.0362	25500	8.0879	-
0.0364	25600	8.0899	-
0.0365	25700	8.0873	-
0.0367	25800	8.1054	-
0.0368	25900	8.075	-
0.0369	26000	8.0825	-
0.0371	26100	8.0798	-
0.0372	26200	8.1017	-
0.0374	26300	8.0816	-
0.0375	26400	8.1103	-
0.0376	26500	8.0891	-
0.0378	26600	8.0759	-
0.0379	26700	8.0719	-
0.0381	26800	8.0798	-
0.0382	26900	8.0885	-
0.0384	27000	8.0776	-
0.0385	27100	8.1055	-
0.0386	27200	8.0992	-
0.0388	27300	8.0884	-
0.0389	27400	8.0815	-
0.0391	27500	8.0754	-
0.0392	27600	8.0923	-
0.0394	27700	8.0845	-
0.0395	27800	8.0824	-
0.0396	27900	8.0902	-
0.0398	28000	8.0788	-
0.0399	28100	8.0907	-
0.0401	28200	8.0676	-
0.0402	28300	8.083	-
0.0403	28400	8.0814	-
0.0405	28500	8.0821	-
0.0406	28600	8.0905	-
0.0408	28700	8.093	-
0.0409	28800	8.0536	-
0.0411	28900	8.0785	-
0.0412	29000	8.0863	-
0.0413	29100	8.0687	-
0.0415	29200	8.0752	-
0.0416	29300	8.0794	-
0.0418	29400	8.0572	-
0.0419	29500	8.0653	-
0.0420	29600	8.0764	-
0.0422	29700	8.0653	-
0.0423	29800	8.0676	-
0.0425	29900	8.0481	-
0.0426	30000	8.0789	-
0.0428	30100	8.0618	-
0.0429	30200	8.0495	-
0.0430	30300	8.0717	-
0.0432	30400	8.0742	-
0.0433	30500	8.0738	-
0.0435	30600	8.058	-
0.0436	30700	8.0656	-
0.0438	30800	8.0525	-
0.0439	30900	8.0605	-
0.0440	31000	8.0836	-
0.0442	31100	8.0871	-
0.0443	31200	8.0655	-
0.0445	31300	8.0533	-
0.0446	31400	8.0684	-
0.0447	31500	8.0634	-
0.0449	31600	8.0538	-
0.0450	31700	8.071	-
0.0452	31800	8.0746	-
0.0453	31900	8.0432	-
0.0455	32000	8.0569	-
0.0456	32100	8.058	-
0.0457	32200	8.0584	-
0.0459	32300	8.0527	-
0.0460	32400	8.0427	-
0.0462	32500	8.0652	-
0.0463	32600	8.0462	-
0.0465	32700	8.0612	-
0.0466	32800	8.0511	-
0.0467	32900	8.0579	-
0.0469	33000	8.05	-
0.0470	33100	8.0568	-
0.0472	33200	8.0463	-
0.0473	33300	8.0528	-
0.0474	33400	8.0596	-
0.0476	33500	8.031	-
0.0477	33600	8.0325	-
0.0479	33700	8.0405	-
0.0480	33800	8.0644	-
0.0482	33900	8.0462	-
0.0483	34000	8.0419	-
0.0484	34100	8.0335	-
0.0486	34200	8.0423	-
0.0487	34300	8.0348	-
0.0489	34400	8.0341	-
0.0490	34500	8.042	-
0.0492	34600	8.0324	-
0.0493	34700	8.0441	-
0.0494	34800	8.0383	-
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0.1923	135400	7.8322	-
0.1925	135500	7.8226	-
0.1926	135600	7.8285	-
0.1928	135700	7.8374	-
0.1929	135800	7.83	-
0.1931	135900	7.8089	-
0.1932	136000	7.8819	-
0.1933	136100	7.8315	-
0.1935	136200	7.8204	-
0.1936	136300	7.8357	-
0.1938	136400	7.8225	-
0.1939	136500	7.8215	-
0.1941	136600	7.8344	-
0.1942	136700	7.8412	-
0.1943	136800	7.832	-
0.1945	136900	7.8182	-
0.1946	137000	7.817	-
0.1948	137100	7.8187	-
0.1949	137200	7.8375	-
0.1950	137300	7.8256	-
0.1952	137400	7.8301	-
0.1953	137500	7.8163	-
0.1955	137600	7.8326	-
0.1956	137700	7.8649	-
0.1958	137800	7.8383	-
0.1959	137900	7.8069	-
0.1960	138000	7.8426	-
0.1962	138100	7.8382	-
0.1963	138200	7.8067	-
0.1965	138300	7.8387	-
0.1966	138400	7.8114	-
0.1968	138500	7.8261	-
0.1969	138600	7.81	-
0.1970	138700	7.8222	-
0.1972	138800	7.8277	-
0.1973	138900	7.8093	-
0.1975	139000	7.8348	-
0.1976	139100	7.8299	-
0.1977	139200	7.8423	-
0.1979	139300	7.8388	-
0.1980	139400	7.8211	-
0.1982	139500	7.8575	-
0.1983	139600	7.8097	-
0.1985	139700	7.8203	-
0.1986	139800	7.852	-
0.1987	139900	7.8331	-
0.1989	140000	7.8137	-
0.1990	140100	7.8258	-
0.1992	140200	7.8343	-
0.1993	140300	7.8122	-
0.1994	140400	7.8156	-
0.1996	140500	7.8194	-
0.1997	140600	7.855	-
0.1999	140700	7.837	-
0.2000	140800	7.8363	-
0.2002	140900	7.8326	-
0.2003	141000	7.8157	-
0.2004	141100	7.7954	-
0.2006	141200	7.8381	-
0.2007	141300	7.8461	-
0.2009	141400	7.8267	-
0.2010	141500	7.8601	-
0.2012	141600	7.8027	-
0.2013	141700	7.829	-
0.2014	141800	7.8144	-
0.2016	141900	7.8244	-
0.2017	142000	7.8204	-
0.2019	142100	7.8233	-
0.2020	142200	7.8197	-
0.2021	142300	7.8496	-
0.2023	142400	7.8464	-
0.2024	142500	7.8244	-
0.2026	142600	7.8491	-
0.2027	142700	7.8168	-
0.2029	142800	7.8407	-
0.2030	142900	7.8324	-
0.2031	143000	7.8372	-
0.2033	143100	7.8087	-
0.2034	143200	7.809	-
0.2036	143300	7.8012	-
0.2037	143400	7.8098	-
0.2039	143500	7.8359	-
0.2040	143600	7.8242	-
0.2041	143700	7.8171	-
0.2043	143800	7.8183	-
0.2044	143900	7.8161	-
0.2046	144000	7.8208	-
0.2047	144100	7.8071	-
0.2048	144200	7.8289	-
0.2050	144300	7.797	-
0.2051	144400	7.8203	-
0.2053	144500	7.7979	-
0.2054	144600	7.8342	-
0.2056	144700	7.8273	-
0.2057	144800	7.8377	-
0.2058	144900	7.8131	-
0.2060	145000	7.8321	-
0.2061	145100	7.7996	-
0.2063	145200	7.8072	-
0.2064	145300	7.829	-
0.2066	145400	7.8064	-
0.2067	145500	7.8535	-
0.2068	145600	7.8087	-
0.2070	145700	7.7906	-
0.2071	145800	7.7999	-
0.2073	145900	7.8227	-
0.2074	146000	7.8225	-
0.2075	146100	7.8085	-
0.2077	146200	7.8584	-
0.2078	146300	7.8044	-
0.2080	146400	7.8494	-
0.2081	146500	7.799	-
0.2083	146600	7.8125	-
0.2084	146700	7.822	-
0.2085	146800	7.7868	-
0.2087	146900	7.8301	-
0.2088	147000	7.8302	-
0.2090	147100	7.8236	-
0.2091	147200	7.8063	-
0.2093	147300	7.8118	-
0.2094	147400	7.7901	-
0.2095	147500	7.8023	-
0.2097	147600	7.8142	-
0.2098	147700	7.8411	-
0.2100	147800	7.8554	-
0.2101	147900	7.805	-
0.2102	148000	7.8168	-
0.2104	148100	7.82	-
0.2105	148200	7.8248	-
0.2107	148300	7.8127	-
0.2108	148400	7.809	-
0.2110	148500	7.8359	-
0.2111	148600	7.8011	-
0.2112	148700	7.7993	-
0.2114	148800	7.8165	-
0.2115	148900	7.8088	-
0.2117	149000	7.8146	-
0.2118	149100	7.8505	-
0.2120	149200	7.8007	-
0.2121	149300	7.8465	-
0.2122	149400	7.8237	-
0.2124	149500	7.8195	-
0.2125	149600	7.8136	-
0.2127	149700	7.8392	-
0.2128	149800	7.7947	-
0.2129	149900	7.8185	-
0.2131	150000	7.8085	-
0.2132	150100	7.8113	-
0.2134	150200	7.8165	-
0.2135	150300	7.8067	-
0.2137	150400	7.8167	-
0.2138	150500	7.8409	-
0.2139	150600	7.8105	-
0.2141	150700	7.8071	-
0.2142	150800	7.8107	-
0.2144	150900	7.796	-
0.2145	151000	7.8155	-
0.2146	151100	7.8336	-
0.2148	151200	7.827	-
0.2149	151300	7.8218	-
0.2151	151400	7.828	-
0.2152	151500	7.805	-
0.2154	151600	7.8401	-
0.2155	151700	7.8206	-
0.2156	151800	7.7919	-
0.2158	151900	7.8439	-
0.2159	152000	7.8307	-
0.2161	152100	7.7985	-
0.2162	152200	7.7635	-
0.2164	152300	7.8139	-
0.2165	152400	7.8103	-
0.2166	152500	7.804	-
0.2168	152600	7.8045	-
0.2169	152700	7.8191	-
0.2171	152800	7.8069	-
0.2172	152900	7.8158	-
0.2173	153000	7.8107	-
0.2175	153100	7.8201	-
0.2176	153200	7.8407	-
0.2178	153300	7.8202	-
0.2179	153400	7.8204	-
0.2181	153500	7.8053	-
0.2182	153600	7.8468	-
0.2183	153700	7.7898	-
0.2185	153800	7.8019	-
0.2186	153900	7.7882	-
0.2188	154000	7.7928	-
0.2189	154100	7.8163	-
0.2191	154200	7.8232	-
0.2192	154300	7.8576	-
0.2193	154400	7.8025	-
0.2195	154500	7.7968	-
0.2196	154600	7.8114	-
0.2198	154700	7.8243	-
0.2199	154800	7.7966	-
0.2200	154900	7.8082	-
0.2202	155000	7.7978	-
0.2203	155100	7.8008	-
0.2205	155200	7.859	-
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0.2210	155600	7.8075	-
0.2212	155700	7.823	-
0.2213	155800	7.8312	-
0.2215	155900	7.8125	-
0.2216	156000	7.8279	-
0.2218	156100	7.8325	-
0.2219	156200	7.8071	-
0.2220	156300	7.8143	-
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0.2225	156600	7.7968	-
0.2226	156700	7.7994	-
0.2227	156800	7.8432	-
0.2229	156900	7.803	-
0.2230	157000	7.8476	-
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0.2235	157300	7.8066	-
0.2236	157400	7.8379	-
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0.2239	157600	7.8251	-
0.2240	157700	7.8082	-
0.2242	157800	7.8015	-
0.2243	157900	7.7933	-
0.2245	158000	7.8213	-
0.2246	158100	7.8148	-
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0.2249	158300	7.8053	-
0.2250	158400	7.8159	-
0.2252	158500	7.839	-
0.2253	158600	7.8535	-
0.2254	158700	7.7961	-
0.2256	158800	7.7882	-
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0.2259	159000	7.7994	-
0.2260	159100	7.8188	-
0.2262	159200	7.8594	-
0.2263	159300	7.8117	-
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0.2266	159500	7.8081	-
0.2267	159600	7.7966	-
0.2269	159700	7.8012	-
0.2270	159800	7.8102	-
0.2272	159900	7.8106	-
0.2273	160000	7.815	-
0.2274	160100	7.7943	-
0.2276	160200	7.8256	-
0.2277	160300	7.7828	-
0.2279	160400	7.8119	-
0.2280	160500	7.7972	-
0.2281	160600	7.7953	-
0.2283	160700	7.8305	-
0.2284	160800	7.7971	-
0.2286	160900	7.8078	-
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0.2290	161200	7.7834	-
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0.2296	161600	7.8	-
0.2297	161700	7.8167	-
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0.2303	162100	7.8108	-
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0.2311	162700	7.7967	-
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0.2340	164700	7.7918	-
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0.2625	184800	7.7737	-
0.2627	184900	7.7737	-
0.2628	185000	7.8103	-
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0.2634	185400	7.8285	-
0.2635	185500	7.8074	-
0.2637	185600	7.7997	-
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0.2642	186000	7.7793	-
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0.2681	188700	7.7728	-
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0.2701	190100	7.786	-
0.2702	190200	7.7805	-
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0.2710	190800	7.8226	-
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0.2715	191100	7.841	-
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0.2740	192900	7.8263	-
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0.2745	193200	7.7782	-
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0.2749	193500	7.776	-
0.2750	193600	7.7748	-
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0.2755	193900	7.8403	-
0.2756	194000	7.7602	-
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0.2760	194300	7.764	-
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0.2764	194600	7.8381	-
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0.2770	195000	7.7832	-
0.2772	195100	7.7799	-
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0.2779	195600	7.7904	-
0.2780	195700	7.8013	-
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0.2784	196000	7.7612	-
0.2786	196100	7.7937	-
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0.2793	196600	7.7815	-
0.2794	196700	7.7796	-
0.2796	196800	7.79	-
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0.2800	197100	7.7774	-
0.2801	197200	7.8161	-
0.2803	197300	7.7769	-
0.2804	197400	7.7876	-
0.2806	197500	7.7757	-
0.2807	197600	7.7949	-
0.2808	197700	7.7877	-
0.2810	197800	7.7789	-
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0.2813	198000	7.7883	-
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0.2817	198300	7.7669	-
0.2818	198400	7.7899	-
0.2820	198500	7.8312	-
0.2821	198600	7.8139	-
0.2823	198700	7.789	-
0.2824	198800	7.805	-
0.2826	198900	7.8007	-
0.2827	199000	7.7846	-
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0.2830	199200	7.794	-
0.2831	199300	7.7843	-
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0.2834	199500	7.7934	-
0.2835	199600	7.7459	-
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0.2840	199900	7.8081	-
0.2841	200000	7.7789	7.7107
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0.2850	200600	7.7776	-
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0.2855	201000	7.7672	-
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0.2860	201300	7.7945	-
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0.2862	201500	7.7765	-
0.2864	201600	7.8011	-
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0.9700	682800	7.7105	-
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0.9703	683000	7.6607	-
0.9704	683100	7.6898	-
0.9705	683200	7.6895	-
0.9707	683300	7.7236	-
0.9708	683400	7.6455	-
0.9710	683500	7.6558	-
0.9711	683600	7.6621	-
0.9713	683700	7.7041	-
0.9714	683800	7.642	-
0.9715	683900	7.6648	-
0.9717	684000	7.6706	-
0.9718	684100	7.6468	-
0.9720	684200	7.6497	-
0.9721	684300	7.6986	-
0.9722	684400	7.6318	-
0.9724	684500	7.6702	-
0.9725	684600	7.6841	-
0.9727	684700	7.6414	-
0.9728	684800	7.688	-
0.9730	684900	7.6745	-
0.9731	685000	7.63	-
0.9732	685100	7.6503	-
0.9734	685200	7.6462	-
0.9735	685300	7.6186	-
0.9737	685400	7.686	-
0.9738	685500	7.6871	-
0.9739	685600	7.6361	-
0.9741	685700	7.6474	-
0.9742	685800	7.6368	-
0.9744	685900	7.6548	-
0.9745	686000	7.7255	-
0.9747	686100	7.6767	-
0.9748	686200	7.699	-
0.9749	686300	7.7176	-
0.9751	686400	7.6506	-
0.9752	686500	7.6556	-
0.9754	686600	7.6755	-
0.9755	686700	7.6586	-
0.9757	686800	7.6469	-
0.9758	686900	7.6683	-
0.9759	687000	7.6766	-
0.9761	687100	7.6851	-
0.9762	687200	7.6527	-
0.9764	687300	7.687	-
0.9765	687400	7.6579	-
0.9766	687500	7.6694	-
0.9768	687600	7.6531	-
0.9769	687700	7.668	-
0.9771	687800	7.6671	-
0.9772	687900	7.6954	-
0.9774	688000	7.6466	-
0.9775	688100	7.6412	-
0.9776	688200	7.7434	-
0.9778	688300	7.6681	-
0.9779	688400	7.7009	-
0.9781	688500	7.6258	-
0.9782	688600	7.6579	-
0.9784	688700	7.6651	-
0.9785	688800	7.6684	-
0.9786	688900	7.6563	-
0.9788	689000	7.6787	-
0.9789	689100	7.6679	-
0.9791	689200	7.6866	-
0.9792	689300	7.693	-
0.9793	689400	7.6859	-
0.9795	689500	7.6601	-
0.9796	689600	7.6592	-
0.9798	689700	7.6527	-
0.9799	689800	7.6688	-
0.9801	689900	7.6958	-
0.9802	690000	7.6649	-
0.9803	690100	7.6312	-
0.9805	690200	7.658	-
0.9806	690300	7.6887	-
0.9808	690400	7.712	-
0.9809	690500	7.6784	-
0.9811	690600	7.647	-
0.9812	690700	7.7072	-
0.9813	690800	7.6651	-
0.9815	690900	7.6695	-
0.9816	691000	7.6632	-
0.9818	691100	7.7496	-
0.9819	691200	7.6493	-
0.9820	691300	7.7016	-
0.9822	691400	7.6265	-
0.9823	691500	7.6702	-
0.9825	691600	7.647	-
0.9826	691700	7.6768	-
0.9828	691800	7.7055	-
0.9829	691900	7.6784	-
0.9830	692000	7.6568	-
0.9832	692100	7.6928	-
0.9833	692200	7.647	-
0.9835	692300	7.6688	-
0.9836	692400	7.6649	-
0.9838	692500	7.6452	-
0.9839	692600	7.6711	-
0.9840	692700	7.6903	-
0.9842	692800	7.6663	-
0.9843	692900	7.6525	-
0.9845	693000	7.6577	-
0.9846	693100	7.6335	-
0.9847	693200	7.7333	-
0.9849	693300	7.6753	-
0.9850	693400	7.7005	-
0.9852	693500	7.6911	-
0.9853	693600	7.7118	-
0.9855	693700	7.7011	-
0.9856	693800	7.6489	-
0.9857	693900	7.7132	-
0.9859	694000	7.6658	-
0.9860	694100	7.6606	-
0.9862	694200	7.6359	-
0.9863	694300	7.6713	-
0.9865	694400	7.6689	-
0.9866	694500	7.6218	-
0.9867	694600	7.6565	-
0.9869	694700	7.6659	-
0.9870	694800	7.6766	-
0.9872	694900	7.6615	-
0.9873	695000	7.6796	-
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0.9876	695200	7.6636	-
0.9877	695300	7.6862	-
0.9879	695400	7.6755	-
0.9880	695500	7.6747	-
0.9882	695600	7.7003	-
0.9883	695700	7.6546	-
0.9884	695800	7.6537	-
0.9886	695900	7.6542	-
0.9887	696000	7.6413	-
0.9889	696100	7.6903	-
0.9890	696200	7.6572	-
0.9891	696300	7.6426	-
0.9893	696400	7.6533	-
0.9894	696500	7.7282	-
0.9896	696600	7.668	-
0.9897	696700	7.6568	-
0.9899	696800	7.7053	-
0.9900	696900	7.6618	-
0.9901	697000	7.6735	-
0.9903	697100	7.7037	-
0.9904	697200	7.7088	-
0.9906	697300	7.7111	-
0.9907	697400	7.6683	-
0.9909	697500	7.6933	-
0.9910	697600	7.7223	-
0.9911	697700	7.7078	-
0.9913	697800	7.6762	-
0.9914	697900	7.7257	-
0.9916	698000	7.7086	-
0.9917	698100	7.6792	-
0.9918	698200	7.6459	-
0.9920	698300	7.6751	-
0.9921	698400	7.7502	-
0.9923	698500	7.6636	-
0.9924	698600	7.6602	-
0.9926	698700	7.6525	-
0.9927	698800	7.6924	-
0.9928	698900	7.6979	-
0.9930	699000	7.6429	-
0.9931	699100	7.7166	-
0.9933	699200	7.7077	-
0.9934	699300	7.6752	-
0.9936	699400	7.64	-
0.9937	699500	7.6824	-
0.9938	699600	7.7011	-
0.9940	699700	7.672	-
0.9941	699800	7.6523	-
0.9943	699900	7.6742	-
0.9944	700000	7.6816	-
0.9945	700100	7.6456	-
0.9947	700200	7.6623	-
0.9948	700300	7.6427	-
0.9950	700400	7.6748	-
0.9951	700500	7.6987	-
0.9953	700600	7.6831	-
0.9954	700700	7.6588	-
0.9955	700800	7.6849	-
0.9957	700900	7.6604	-
0.9958	701000	7.6924	-
0.9960	701100	7.6899	-
0.9961	701200	7.6544	-
0.9963	701300	7.6574	-
0.9964	701400	7.6488	-
0.9965	701500	7.6743	-
0.9967	701600	7.6902	-
0.9968	701700	7.7373	-
0.9970	701800	7.6437	-
0.9971	701900	7.6923	-
0.9972	702000	7.7009	-
0.9974	702100	7.6418	-
0.9975	702200	7.6535	-
0.9977	702300	7.6636	-
0.9978	702400	7.6614	-
0.9980	702500	7.6765	-
0.9981	702600	7.6893	-
0.9982	702700	7.6952	-
0.9984	702800	7.6514	-
0.9985	702900	7.6818	-
0.9987	703000	7.6814	-
0.9988	703100	7.6454	-
0.9990	703200	7.6587	-
0.9991	703300	7.6629	-
0.9992	703400	7.6319	-
0.9994	703500	7.6821	-
0.9995	703600	7.6568	-
0.9997	703700	7.6669	-
0.9998	703800	7.7164	-
0.9999	703900	7.6444	-

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},
}