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language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:42500432
  - loss:CoSENTLoss
base_model: KhaledReda/all-MiniLM-L6-v18-pair_score
widget:
  - source_sentence: boiled eegg
    sentences:
      - pigmented matte lipstiick
      - headphone
      - beveerage
  - source_sentence: ruffle sleeves dresss
    sentences:
      - summer lbouse
      - carved candle holder
      - hand drawn coaster
  - source_sentence: healthy cocktails
    sentences:
      - clean and simple wine glass
      - stroage tray
      - crystal incensse burner
  - source_sentence: aluminumfree deodorant
    sentences:
      - handwoven runner
      - hareesa with honey syrup
      - oven dish
  - source_sentence: sapphire choker
    sentences:
      - semi natural neckllace
      - vanilla bodyy wahs
      - optaminess tablets
datasets:
  - KhaledReda/pairs_with_scores_v16_tag_sim_synonyms_typo_augmented
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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

	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

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

	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

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	-
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0.1928	64000	7.7835	-
0.1931	64100	7.7676	-
0.1934	64200	7.7343	-
0.1937	64300	7.7246	-
0.1940	64400	7.7505	-
0.1943	64500	7.7408	-
0.1946	64600	7.7327	-
0.1949	64700	7.7703	-
0.1952	64800	7.7632	-
0.1955	64900	7.7336	-
0.1958	65000	7.7159	-
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0.1964	65200	7.7467	-
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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0.5454	181100	7.6536	-
0.5457	181200	7.6443	-
0.5460	181300	7.6246	-
0.5463	181400	7.6114	-
0.5466	181500	7.7186	-
0.5469	181600	7.6035	-
0.5472	181700	7.6385	-
0.5475	181800	7.656	-
0.5478	181900	7.6355	-
0.5481	182000	7.6602	-
0.5484	182100	7.6339	-
0.5487	182200	7.6425	-
0.5490	182300	7.6332	-
0.5493	182400	7.6581	-
0.5496	182500	7.6314	-
0.5499	182600	7.6321	-
0.5502	182700	7.6121	-
0.5505	182800	7.6627	-
0.5508	182900	7.6133	-
0.5511	183000	7.6276	-
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0.5517	183200	7.6672	-
0.5521	183300	7.6051	-
0.5524	183400	7.6776	-
0.5527	183500	7.6557	-
0.5530	183600	7.6455	-
0.5533	183700	7.6558	-
0.5536	183800	7.6389	-
0.5539	183900	7.6273	-
0.5542	184000	7.6761	-
0.5545	184100	7.6403	-
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0.5551	184300	7.6301	-
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0.5560	184600	7.6192	-
0.5563	184700	7.6545	-
0.5566	184800	7.6323	-
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0.5578	185200	7.6622	-
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0.5587	185500	7.6291	-
0.5590	185600	7.6459	-
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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	-
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0.5638	187200	7.6551	-
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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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0.5677	188500	7.6639	-
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	-
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0.5737	190500	7.6578	-
0.5740	190600	7.6845	-
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0.5746	190800	7.6583	-
0.5749	190900	7.6296	-
0.5752	191000	7.6323	-
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0.5758	191200	7.6332	-
0.5761	191300	7.6273	-
0.5764	191400	7.6367	-
0.5767	191500	7.6533	-
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	-
0.5807	192800	7.6138	-
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	-
0.5846	194100	7.6098	-
0.5849	194200	7.6316	-
0.5852	194300	7.6323	-
0.5855	194400	7.5996	-
0.5858	194500	7.6504	-
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	-
0.6029	200200	7.6458	-
0.6032	200300	7.6642	-
0.6036	200400	7.6886	-
0.6039	200500	7.5912	-
0.6042	200600	7.6649	-
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0.6051	200900	7.6549	-
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	-
0.6090	202200	7.6675	-
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0.6394	212300	7.6319	-
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0.6400	212500	7.6649	-
0.6403	212600	7.6683	-
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0.6409	212800	7.6296	-
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0.6415	213000	7.6263	-
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0.6825	226600	7.5663	-
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0.6900	229100	7.6064	-
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0.6912	229500	7.5945	-
0.6915	229600	7.6329	-
0.6918	229700	7.6066	-
0.6921	229800	7.6438	-
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0.6927	230000	7.6531	-
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0.6933	230200	7.6273	-
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0.6942	230500	7.6376	-
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0.6951	230800	7.5821	-
0.6954	230900	7.6174	-
0.6957	231000	7.6255	-
0.6960	231100	7.5892	-
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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},
}