This is a sentence-transformers model finetuned from sbintuitions/sarashina-embedding-v2-1b on the jsts dataset. It maps sentences & paragraphs to a 1792-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: sbintuitions/sarashina-embedding-v2-1b
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1792 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- jsts
Language: jpn

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': 8192, 'do_lower_case': False, 'architecture': 'LlamaModel'})
  (1): Pooling({'word_embedding_dimension': 1792, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': False})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kushalc1/sarashina-embedding-v2-1b-jsts-matryoshka")
# Run inference
sentences = [
    '樹木に囲まれた芝生の上に三頭のキリンが立っています。',
    '芝生の上に数頭のキリンが歩いています。',
    '茶色のテーブルの上にピザと飲み物が置かれています。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8499, 0.3438],
#         [0.8499, 1.0000, 0.3127],
#         [0.3438, 0.3127, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev-1792 and sts-test-1792
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 1792 3}

Metric	sts-dev-1792	sts-test-1792
pearson_cosine	0.8125	0.8124
spearman_cosine	0.7521	0.7521

Semantic Similarity

Datasets: sts-dev-1280 and sts-test-1280
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 1280 3}

Metric	sts-dev-1280	sts-test-1280
pearson_cosine	0.8099	0.8099
spearman_cosine	0.7492	0.7491

Semantic Similarity

Datasets: sts-dev-768 and sts-test-768
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	sts-dev-768	sts-test-768
pearson_cosine	0.8058	0.8057
spearman_cosine	0.7462	0.7461

Semantic Similarity

Datasets: sts-dev-256 and sts-test-256
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	sts-dev-256	sts-test-256
pearson_cosine	0.7946	0.7945
spearman_cosine	0.7381	0.738

Semantic Similarity

Datasets: sts-dev-64 and sts-test-64
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	sts-dev-64	sts-test-64
pearson_cosine	0.753	0.7531
spearman_cosine	0.708	0.708

Training Details

Training Dataset

jsts

Dataset: jsts at b3d3097
Size: 12,451 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 10.64 tokens
max: 35 tokens
min: 3 tokens
mean: 10.53 tokens
max: 30 tokens
min: 0.0
mean: 2.32
max: 5.0

Samples:

sentence1	sentence2	score
`川べりでサーフボードを持った人たちがいます。`	`トイレの壁に黒いタオルがかけられています。`	`0.0`
`二人の男性がジャンボジェット機を見ています。`	`2人の男性が、白い飛行機を眺めています。`	`3.799999952316284`
`男性が子供を抱き上げて立っています。`	`坊主頭の男性が子供を抱いて立っています。`	`4.0`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

jsts

Dataset: jsts at b3d3097
Size: 1,457 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 10.78 tokens
max: 34 tokens
min: 3 tokens
mean: 10.63 tokens
max: 37 tokens
min: 0.0
mean: 2.22
max: 5.0

Samples:

sentence1	sentence2	score
`レンガの建物の前を、乳母車を押した女性が歩いています。`	`厩舎で馬と女性とが寄り添っています。`	`0.0`
`山の上に顔の白い牛が2頭います。`	`曇り空の山肌で、牛が２匹草を食んでいます。`	`2.4000000953674316`
`バナナを持った人が道路を通行しています。`	`道の上をバナナを背負った男性が歩いています。`	`3.5999999046325684`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
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: 16
per_device_eval_batch_size: 16
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: 5e-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: 4
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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

Epoch	Step	Training Loss	Validation Loss	sts-dev-1792_spearman_cosine	sts-dev-1280_spearman_cosine	sts-dev-768_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-64_spearman_cosine	sts-test-1792_spearman_cosine	sts-test-1280_spearman_cosine	sts-test-768_spearman_cosine	sts-test-256_spearman_cosine	sts-test-64_spearman_cosine
0.1284	100	0.9104	1.0803	0.7732	0.7742	0.7700	0.7659	0.7287	-	-	-	-	-
0.2567	200	1.0621	1.1598	0.7208	0.7204	0.7198	0.7096	0.6787	-	-	-	-	-
0.3851	300	1.2289	1.4808	0.6225	0.6305	0.6234	0.6154	0.5922	-	-	-	-	-
0.5135	400	1.215	1.3408	0.6559	0.6527	0.6497	0.6473	0.6329	-	-	-	-	-
0.6418	500	1.2991	1.4541	0.6300	0.6324	0.6314	0.6276	0.5959	-	-	-	-	-
0.7702	600	1.2537	1.3891	0.6418	0.6416	0.6422	0.6389	0.6019	-	-	-	-	-
0.8986	700	1.2248	1.2778	0.6817	0.6855	0.6832	0.6800	0.6562	-	-	-	-	-
1.0270	800	1.1772	1.3947	0.6674	0.6666	0.6652	0.6618	0.6392	-	-	-	-	-
1.1553	900	1.119	1.2291	0.7086	0.7066	0.7018	0.7032	0.6758	-	-	-	-	-
1.2837	1000	1.0503	1.1655	0.7183	0.7163	0.7135	0.7130	0.6961	-	-	-	-	-
1.4121	1100	1.0729	1.1550	0.7333	0.7340	0.7299	0.7260	0.6955	-	-	-	-	-
1.5404	1200	1.0952	1.3186	0.6753	0.6790	0.6728	0.6738	0.6417	-	-	-	-	-
1.6688	1300	1.0284	1.1816	0.7146	0.7131	0.7071	0.7096	0.6786	-	-	-	-	-
1.7972	1400	1.0248	1.1495	0.7282	0.7270	0.7206	0.7196	0.6913	-	-	-	-	-
1.9255	1500	1.0138	1.1371	0.7264	0.7240	0.7175	0.7130	0.6738	-	-	-	-	-
2.0539	1600	0.9739	1.1577	0.7173	0.7143	0.7088	0.7142	0.6881	-	-	-	-	-
2.1823	1700	0.7908	1.1627	0.7358	0.7341	0.7300	0.7278	0.7045	-	-	-	-	-
2.3107	1800	0.8877	1.1396	0.7312	0.7303	0.7263	0.7219	0.6942	-	-	-	-	-
2.4390	1900	0.8403	1.1868	0.7395	0.7379	0.7346	0.7324	0.7167	-	-	-	-	-
2.5674	2000	0.8558	1.1481	0.7424	0.7411	0.7379	0.7373	0.7143	-	-	-	-	-
2.6958	2100	0.8304	1.1377	0.7391	0.7366	0.7314	0.7287	0.7075	-	-	-	-	-
2.8241	2200	0.8053	1.0874	0.7384	0.7367	0.7313	0.7284	0.7153	-	-	-	-	-
2.9525	2300	0.8071	1.0720	0.7504	0.7487	0.7436	0.7372	0.7205	-	-	-	-	-
3.0809	2400	0.6082	1.1407	0.7533	0.7521	0.7480	0.7403	0.7195	-	-	-	-	-
3.2092	2500	0.5183	1.1555	0.7562	0.7546	0.7505	0.7437	0.7124	-	-	-	-	-
3.3376	2600	0.5311	1.1923	0.7494	0.7476	0.7434	0.7382	0.7084	-	-	-	-	-
3.4660	2700	0.4914	1.1752	0.7446	0.7416	0.7376	0.7326	0.7034	-	-	-	-	-
3.5944	2800	0.5486	1.1924	0.7472	0.7439	0.7400	0.7313	0.6989	-	-	-	-	-
3.7227	2900	0.4702	1.1903	0.7526	0.7497	0.7466	0.7380	0.7075	-	-	-	-	-
3.8511	3000	0.4674	1.1749	0.7519	0.7487	0.7459	0.7378	0.7071	-	-	-	-	-
3.9795	3100	0.4696	1.1795	0.7521	0.7492	0.7462	0.7381	0.7080	-	-	-	-	-
-1	-1	-	-	-	-	-	-	-	0.7521	0.7491	0.7461	0.7380	0.7080

Framework Versions

Python: 3.12.6
Sentence Transformers: 5.2.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu129
Accelerate: 1.10.1
Datasets: 4.4.2
Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence1	sentence2	score
type	string	string	float
details	min: 5 tokens mean: 10.64 tokens max: 35 tokens	min: 3 tokens mean: 10.53 tokens max: 30 tokens	min: 0.0 mean: 2.32 max: 5.0