jvanhoof/all-miniLM-L6-v2-BGEs-distilled-en-ja-64K-vocab-mean-21M-test

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

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': 128, 'do_lower_case': False}) with Transformer model: 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 = [
    'Nano-structure A nanostructure is an intermediate size between molecular and microscopic (micrometer-sized) structures.',
    'ナノ構造は、分子構造と微視的（マイクロメートルサイズ）構造との間の中間サイズの対象である。',
    '魔術のお話に戻りましょう。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Datasets: JSTS and stsb_multi_mt-en
• Evaluated with EmbeddingSimilarityEvaluator

Metric	JSTS	stsb_multi_mt-en
pearson_cosine	0.8235	0.8363
spearman_cosine	0.7815	0.8564

Training Details

Training Dataset

Unnamed Dataset

• Size: 21,210,762 training samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 1000 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 4 tokens mean: 16.46 tokens max: 92 tokens	min: 4 tokens mean: 21.99 tokens max: 128 tokens	size: 384 elements

• Samples:

  english	non_english	label
  We live the life of the project.	プロジェクトの生命を左右する。	[0.009600206278264523, 0.058811139315366745, 0.023707984015345573, -0.021880649030208588, 0.068634033203125, ...]
  Hold on here, Mr. Budget Director.	ここいろ編集長	[-0.04940887540578842, -0.013437069952487946, 0.024199623614549637, -0.02371774986386299, 0.06858911365270615, ...]
  So yes, biology has all the attributes of a transportation genius today.	そうです 生物は 今日話した最高の交通にある特性を 全て持ち合わせています	[0.031787291169166565, 0.011292539536952972, 0.03621761128306389, -0.04237872734665871, -0.030112963169813156, ...]

• Loss: MSELoss

Evaluation Dataset

Unnamed Dataset

• Size: 214,251 evaluation samples
• Columns: english, non_english, and label
• Approximate statistics based on the first 1000 samples:

  	english	non_english	label
  type	string	string	list
  details	min: 4 tokens mean: 16.24 tokens max: 88 tokens	min: 4 tokens mean: 22.18 tokens max: 128 tokens	size: 384 elements

• Samples:

  english	non_english	label
  Then the next step was the social bookmarking.	次のカテゴリはソーシャルブックマークです。	[-0.040418993681669235, 0.019537044689059258, -0.014964035712182522, -0.06385297328233719, 0.00023657231940887868, ...]
  Ooh! Scary word! Ahh!	なんと 恐ろしい言葉！	[0.023886308073997498, -0.04336044192314148, -0.057255394756793976, 0.05142980441451073, 0.06282227486371994, ...]
  Usually Ebates offers 1.	通常提示スプレッド*1	[0.00018616259330883622, -0.01999301090836525, 0.049356017261743546, 0.002617522142827511, -0.0540102981030941, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• gradient_accumulation_steps: 2
• learning_rate: 0.0003
• num_train_epochs: 8
• warmup_ratio: 0.15
• bf16: True
• dataloader_num_workers: 8

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 0.0003
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 8
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.15
• 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: True
• fp16: False
• 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: 8
• 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}
• tp_size: 0
• 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
• 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	JSTS_spearman_cosine	stsb_multi_mt-en_spearman_cosine
0.0241	500	0.0064	-	-	-
0.0483	1000	0.0045	-	-	-
0.0724	1500	0.0038	-	-	-
0.0966	2000	0.0035	0.0016	0.3008	0.2821
0.1207	2500	0.0033	-	-	-
0.1448	3000	0.0031	-	-	-
0.1690	3500	0.0029	-	-	-
0.1931	4000	0.0028	0.0013	0.4989	0.4681
0.2172	4500	0.0026	-	-	-
0.2414	5000	0.0025	-	-	-
0.2655	5500	0.0023	-	-	-
0.2897	6000	0.0022	0.0010	0.6554	0.6567
0.3138	6500	0.0021	-	-	-
0.3379	7000	0.002	-	-	-
0.3621	7500	0.0019	-	-	-
0.3862	8000	0.0018	0.0008	0.7038	0.7328
0.4104	8500	0.0017	-	-	-
0.4345	9000	0.0017	-	-	-
0.4586	9500	0.0016	-	-	-
0.4828	10000	0.0016	0.0007	0.7420	0.7662
0.5069	10500	0.0015	-	-	-
0.5310	11000	0.0015	-	-	-
0.5552	11500	0.0014	-	-	-
0.5793	12000	0.0014	0.0006	0.7559	0.7929
0.6035	12500	0.0014	-	-	-
0.6276	13000	0.0014	-	-	-
0.6517	13500	0.0013	-	-	-
0.6759	14000	0.0013	0.0006	0.7625	0.8056
0.7000	14500	0.0013	-	-	-
0.7241	15000	0.0013	-	-	-
0.7483	15500	0.0012	-	-	-
0.7724	16000	0.0012	0.0006	0.7652	0.8150
0.7966	16500	0.0012	-	-	-
0.8207	17000	0.0012	-	-	-
0.8448	17500	0.0012	-	-	-
0.8690	18000	0.0012	0.0005	0.7679	0.8209
0.8931	18500	0.0012	-	-	-
0.9173	19000	0.0011	-	-	-
0.9414	19500	0.0011	-	-	-
0.9655	20000	0.0011	0.0005	0.7727	0.8269
0.9897	20500	0.0011	-	-	-
1.0138	21000	0.0011	-	-	-
1.0379	21500	0.0011	-	-	-
1.0621	22000	0.0011	0.0005	0.7682	0.8319
1.0862	22500	0.0011	-	-	-
1.1104	23000	0.0011	-	-	-
1.1345	23500	0.0011	-	-	-
1.1586	24000	0.0011	0.0005	0.7718	0.8372
1.1828	24500	0.0011	-	-	-
1.2069	25000	0.0011	-	-	-
1.2311	25500	0.0011	-	-	-
1.2552	26000	0.001	0.0005	0.7751	0.8408
1.2793	26500	0.001	-	-	-
1.3035	27000	0.001	-	-	-
1.3276	27500	0.001	-	-	-
1.3517	28000	0.001	0.0005	0.7703	0.8437
1.3759	28500	0.001	-	-	-
1.4000	29000	0.001	-	-	-
1.4242	29500	0.001	-	-	-
1.4483	30000	0.001	0.0005	0.7730	0.8439
1.4724	30500	0.001	-	-	-
1.4966	31000	0.001	-	-	-
1.5207	31500	0.001	-	-	-
1.5448	32000	0.001	0.0005	0.7719	0.8456
1.5690	32500	0.001	-	-	-
1.5931	33000	0.001	-	-	-
1.6173	33500	0.001	-	-	-
1.6414	34000	0.001	0.0005	0.7719	0.8449
1.6655	34500	0.001	-	-	-
1.6897	35000	0.001	-	-	-
1.7138	35500	0.001	-	-	-
1.7380	36000	0.001	0.0004	0.7717	0.8455
1.7621	36500	0.001	-	-	-
1.7862	37000	0.001	-	-	-
1.8104	37500	0.001	-	-	-
1.8345	38000	0.001	0.0004	0.7714	0.8488
1.8586	38500	0.001	-	-	-
1.8828	39000	0.001	-	-	-
1.9069	39500	0.001	-	-	-
1.9311	40000	0.001	0.0004	0.7753	0.8474
1.9552	40500	0.001	-	-	-
1.9793	41000	0.001	-	-	-
2.0035	41500	0.001	-	-	-
2.0276	42000	0.001	0.0004	0.7708	0.8479
2.0518	42500	0.001	-	-	-
2.0759	43000	0.001	-	-	-
2.1000	43500	0.001	-	-	-
2.1242	44000	0.001	0.0004	0.7703	0.8505
2.1483	44500	0.001	-	-	-
2.1724	45000	0.0009	-	-	-
2.1966	45500	0.0009	-	-	-
2.2207	46000	0.0009	0.0004	0.7752	0.8525
2.2449	46500	0.0009	-	-	-
2.2690	47000	0.0009	-	-	-
2.2931	47500	0.0009	-	-	-
2.3173	48000	0.0009	0.0004	0.7734	0.8518
2.3414	48500	0.0009	-	-	-
2.3655	49000	0.0009	-	-	-
2.3897	49500	0.0009	-	-	-
2.4138	50000	0.0009	0.0004	0.7725	0.8512
2.4380	50500	0.0009	-	-	-
2.4621	51000	0.0009	-	-	-
2.4862	51500	0.0009	-	-	-
2.5104	52000	0.0009	0.0004	0.7709	0.8535
2.5345	52500	0.0009	-	-	-
2.5587	53000	0.0009	-	-	-
2.5828	53500	0.0009	-	-	-
2.6069	54000	0.0009	0.0004	0.7751	0.8519
2.6311	54500	0.0009	-	-	-
2.6552	55000	0.0009	-	-	-
2.6793	55500	0.0009	-	-	-
2.7035	56000	0.0009	0.0004	0.7770	0.8500
2.7276	56500	0.0009	-	-	-
2.7518	57000	0.0009	-	-	-
2.7759	57500	0.0009	-	-	-
2.8000	58000	0.0009	0.0004	0.7756	0.8514
2.8242	58500	0.0009	-	-	-
2.8483	59000	0.0009	-	-	-
2.8725	59500	0.0009	-	-	-
2.8966	60000	0.0009	0.0004	0.7791	0.8541
2.9207	60500	0.0009	-	-	-
2.9449	61000	0.0009	-	-	-
2.9690	61500	0.0009	-	-	-
2.9931	62000	0.0009	0.0004	0.7759	0.8539
3.0173	62500	0.0009	-	-	-
3.0414	63000	0.0009	-	-	-
3.0656	63500	0.0009	-	-	-
3.0897	64000	0.0009	0.0004	0.7770	0.8526
3.1138	64500	0.0009	-	-	-
3.1380	65000	0.0009	-	-	-
3.1621	65500	0.0009	-	-	-
3.1863	66000	0.0009	0.0004	0.7762	0.8531
3.2104	66500	0.0009	-	-	-
3.2345	67000	0.0009	-	-	-
3.2587	67500	0.0009	-	-	-
3.2828	68000	0.0009	0.0004	0.7771	0.8515
3.3069	68500	0.0009	-	-	-
3.3311	69000	0.0009	-	-	-
3.3552	69500	0.0009	-	-	-
3.3794	70000	0.0009	0.0004	0.7757	0.8530
3.4035	70500	0.0009	-	-	-
3.4276	71000	0.0009	-	-	-
3.4518	71500	0.0009	-	-	-
3.4759	72000	0.0009	0.0004	0.7776	0.8532
3.5000	72500	0.0009	-	-	-
3.5242	73000	0.0009	-	-	-
3.5483	73500	0.0009	-	-	-
3.5725	74000	0.0009	0.0004	0.7776	0.8542
3.5966	74500	0.0009	-	-	-
3.6207	75000	0.0009	-	-	-
3.6449	75500	0.0009	-	-	-
3.6690	76000	0.0009	0.0004	0.7803	0.8539
3.6932	76500	0.0009	-	-	-
3.7173	77000	0.0009	-	-	-
3.7414	77500	0.0009	-	-	-
3.7656	78000	0.0009	0.0004	0.7778	0.8537
3.7897	78500	0.0009	-	-	-
3.8138	79000	0.0009	-	-	-
3.8380	79500	0.0009	-	-	-
3.8621	80000	0.0009	0.0004	0.7800	0.8539
3.8863	80500	0.0009	-	-	-
3.9104	81000	0.0009	-	-	-
3.9345	81500	0.0009	-	-	-
3.9587	82000	0.0009	0.0004	0.7797	0.8542
3.9828	82500	0.0009	-	-	-
4.0070	83000	0.0009	-	-	-
4.0311	83500	0.0009	-	-	-
4.0552	84000	0.0009	0.0004	0.7808	0.8547
4.0794	84500	0.0009	-	-	-
4.1035	85000	0.0009	-	-	-
4.1276	85500	0.0009	-	-	-
4.1518	86000	0.0009	0.0004	0.7778	0.8545
4.1759	86500	0.0009	-	-	-
4.2001	87000	0.0009	-	-	-
4.2242	87500	0.0009	-	-	-
4.2483	88000	0.0009	0.0004	0.7815	0.8555
4.2725	88500	0.0009	-	-	-
4.2966	89000	0.0009	-	-	-
4.3207	89500	0.0009	-	-	-
4.3449	90000	0.0009	0.0004	0.7797	0.8534
4.3690	90500	0.0009	-	-	-
4.3932	91000	0.0009	-	-	-
4.4173	91500	0.0009	-	-	-
4.4414	92000	0.0009	0.0004	0.7823	0.8547
4.4656	92500	0.0009	-	-	-
4.4897	93000	0.0009	-	-	-
4.5139	93500	0.0009	-	-	-
4.5380	94000	0.0009	0.0004	0.7783	0.8535
4.5621	94500	0.0009	-	-	-
4.5863	95000	0.0009	-	-	-
4.6104	95500	0.0009	-	-	-
4.6345	96000	0.0009	0.0004	0.7811	0.8550
4.6587	96500	0.0009	-	-	-
4.6828	97000	0.0009	-	-	-
4.7070	97500	0.0009	-	-	-
4.7311	98000	0.0009	0.0004	0.7801	0.8540
4.7552	98500	0.0009	-	-	-
4.7794	99000	0.0009	-	-	-
4.8035	99500	0.0009	-	-	-
4.8277	100000	0.0009	0.0004	0.7811	0.8544
4.8518	100500	0.0009	-	-	-
4.8759	101000	0.0009	-	-	-
4.9001	101500	0.0009	-	-	-
4.9242	102000	0.0009	0.0004	0.7805	0.8548
4.9483	102500	0.0009	-	-	-
4.9725	103000	0.0009	-	-	-
4.9966	103500	0.0009	-	-	-
5.0208	104000	0.0009	0.0004	0.7797	0.8534
5.0449	104500	0.0009	-	-	-
5.0690	105000	0.0009	-	-	-
5.0932	105500	0.0009	-	-	-
5.1173	106000	0.0009	0.0004	0.7821	0.8555
5.1415	106500	0.0009	-	-	-
5.1656	107000	0.0009	-	-	-
5.1897	107500	0.0009	-	-	-
5.2139	108000	0.0009	0.0004	0.7816	0.8558
5.2380	108500	0.0009	-	-	-
5.2621	109000	0.0009	-	-	-
5.2863	109500	0.0009	-	-	-
5.3104	110000	0.0009	0.0004	0.7804	0.8556
5.3346	110500	0.0009	-	-	-
5.3587	111000	0.0009	-	-	-
5.3828	111500	0.0009	-	-	-
5.4070	112000	0.0009	0.0004	0.7813	0.8548
5.4311	112500	0.0009	-	-	-
5.4552	113000	0.0009	-	-	-
5.4794	113500	0.0009	-	-	-
5.5035	114000	0.0009	0.0004	0.7823	0.8548
5.5277	114500	0.0009	-	-	-
5.5518	115000	0.0009	-	-	-
5.5759	115500	0.0009	-	-	-
5.6001	116000	0.0009	0.0004	0.7809	0.8551
5.6242	116500	0.0009	-	-	-
5.6484	117000	0.0009	-	-	-
5.6725	117500	0.0009	-	-	-
5.6966	118000	0.0009	0.0004	0.7833	0.8557
5.7208	118500	0.0009	-	-	-
5.7449	119000	0.0009	-	-	-
5.7690	119500	0.0009	-	-	-
5.7932	120000	0.0009	0.0004	0.7842	0.8551
5.8173	120500	0.0009	-	-	-
5.8415	121000	0.0009	-	-	-
5.8656	121500	0.0009	-	-	-
5.8897	122000	0.0009	0.0004	0.7817	0.8563
5.9139	122500	0.0009	-	-	-
5.9380	123000	0.0009	-	-	-
5.9622	123500	0.0009	-	-	-
5.9863	124000	0.0009	0.0004	0.7812	0.8559
6.0104	124500	0.0009	-	-	-
6.0346	125000	0.0009	-	-	-
6.0587	125500	0.0009	-	-	-
6.0828	126000	0.0009	0.0004	0.7821	0.8558
6.1070	126500	0.0009	-	-	-
6.1311	127000	0.0009	-	-	-
6.1553	127500	0.0009	-	-	-
6.1794	128000	0.0009	0.0004	0.7829	0.8548
6.2035	128500	0.0009	-	-	-
6.2277	129000	0.0009	-	-	-
6.2518	129500	0.0009	-	-	-
6.2759	130000	0.0009	0.0004	0.7805	0.8549
6.3001	130500	0.0009	-	-	-
6.3242	131000	0.0009	-	-	-
6.3484	131500	0.0009	-	-	-
6.3725	132000	0.0009	0.0004	0.7807	0.8563
6.3966	132500	0.0009	-	-	-
6.4208	133000	0.0009	-	-	-
6.4449	133500	0.0009	-	-	-
6.4691	134000	0.0009	0.0004	0.7829	0.8555
6.4932	134500	0.0009	-	-	-
6.5173	135000	0.0009	-	-	-
6.5415	135500	0.0009	-	-	-
6.5656	136000	0.0009	0.0004	0.7819	0.8550
6.5897	136500	0.0009	-	-	-
6.6139	137000	0.0009	-	-	-
6.6380	137500	0.0009	-	-	-
6.6622	138000	0.0009	0.0004	0.7800	0.8548
6.6863	138500	0.0009	-	-	-
6.7104	139000	0.0009	-	-	-
6.7346	139500	0.0009	-	-	-
6.7587	140000	0.0009	0.0004	0.7817	0.8555
6.7829	140500	0.0009	-	-	-
6.8070	141000	0.0009	-	-	-
6.8311	141500	0.0009	-	-	-
6.8553	142000	0.0009	0.0004	0.7812	0.8556
6.8794	142500	0.0009	-	-	-
6.9035	143000	0.0009	-	-	-
6.9277	143500	0.0009	-	-	-
6.9518	144000	0.0009	0.0004	0.7830	0.8559
6.9760	144500	0.0009	-	-	-
7.0001	145000	0.0009	-	-	-
7.0242	145500	0.0009	-	-	-
7.0484	146000	0.0009	0.0004	0.7809	0.8561
7.0725	146500	0.0009	-	-	-
7.0966	147000	0.0009	-	-	-
7.1208	147500	0.0009	-	-	-
7.1449	148000	0.0009	0.0004	0.7798	0.8560
7.1691	148500	0.0009	-	-	-
7.1932	149000	0.0009	-	-	-
7.2173	149500	0.0009	-	-	-
7.2415	150000	0.0009	0.0004	0.7815	0.8559
7.2656	150500	0.0009	-	-	-
7.2898	151000	0.0009	-	-	-
7.3139	151500	0.0009	-	-	-
7.3380	152000	0.0009	0.0004	0.7828	0.8562
7.3622	152500	0.0009	-	-	-
7.3863	153000	0.0009	-	-	-
7.4104	153500	0.0009	-	-	-
7.4346	154000	0.0009	0.0004	0.7837	0.8565
7.4587	154500	0.0009	-	-	-
7.4829	155000	0.0009	-	-	-
7.5070	155500	0.0009	-	-	-
7.5311	156000	0.0009	0.0004	0.7819	0.8565
7.5553	156500	0.0009	-	-	-
7.5794	157000	0.0009	-	-	-
7.6036	157500	0.0009	-	-	-
7.6277	158000	0.0009	0.0004	0.7818	0.8557
7.6518	158500	0.0009	-	-	-
7.6760	159000	0.0009	-	-	-
7.7001	159500	0.0009	-	-	-
7.7242	160000	0.0009	0.0004	0.7811	0.8557
7.7484	160500	0.0009	-	-	-
7.7725	161000	0.0009	-	-	-
7.7967	161500	0.0009	-	-	-
7.8208	162000	0.0009	0.0004	0.7821	0.8566
7.8449	162500	0.0009	-	-	-
7.8691	163000	0.0009	-	-	-
7.8932	163500	0.0009	-	-	-
7.9174	164000	0.0009	0.0004	0.7815	0.8564
7.9415	164500	0.0009	-	-	-
7.9656	165000	0.0009	-	-	-
7.9898	165500	0.0009	-	-	-

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 3.3.1
• Transformers: 4.51.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.1

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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}