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kushalc1/
sarashina-embedding-v2-1b-jsts-matryoshka

Name: sarashina-embedding-v2-1b-jsts-matryoshka
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SentenceTransformer based on sbintuitions/sarashina-embedding-v2-1b

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, 0.9453, 0.4754],
#         [0.9453, 1.0000, 0.5004],
#         [0.4754, 0.5004, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev-1792, sts-test-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	0.8088	0.8088
spearman_cosine	0.7435	0.7435

Semantic Similarity

Datasets: sts-dev-1280, sts-test-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	0.8078	0.8078
spearman_cosine	0.7442	0.7442

Semantic Similarity

Datasets: sts-dev-768, sts-test-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	0.8049	0.8049
spearman_cosine	0.7423	0.7423

Semantic Similarity

Datasets: sts-dev-256, sts-test-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	0.8022	0.8022
spearman_cosine	0.7411	0.7411

Semantic Similarity

Datasets: sts-dev-64, sts-test-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	0.7972	0.7972
spearman_cosine	0.7389	0.7389

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: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1792, 5 1280, 6 768, 7 256, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1792, 5 1280, 6 768, 7 256, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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	4.911	6.2373	0.7573	0.7572	0.7517	0.7350	0.7114	-	-	-	-	-
0.2567	200	5.8664	7.9813	0.6739	0.6692	0.6636	0.6500	0.6146	-	-	-	-	-
0.3851	300	7.259	7.9829	0.6831	0.6815	0.6797	0.6698	0.6529	-	-	-	-	-
0.5135	400	7.1234	7.5810	0.6878	0.6887	0.6881	0.6819	0.6679	-	-	-	-	-
0.6418	500	7.233	6.9384	0.6628	0.6694	0.6658	0.6662	0.6583	-	-	-	-	-
0.7702	600	7.0228	7.0102	0.6352	0.6364	0.6346	0.6310	0.6246	-	-	-	-	-
0.8986	700	6.539	6.7671	0.6411	0.6415	0.6403	0.6394	0.6346	-	-	-	-	-
1.0270	800	6.2863	7.5846	0.6120	0.6342	0.6314	0.6266	0.6189	-	-	-	-	-
1.1553	900	5.7608	6.7480	0.6773	0.6790	0.6758	0.6748	0.6691	-	-	-	-	-
1.2837	1000	5.672	6.6481	0.6846	0.6836	0.6817	0.6834	0.6794	-	-	-	-	-
1.4121	1100	5.7371	6.6843	0.6945	0.6953	0.6966	0.6939	0.6891	-	-	-	-	-
1.5404	1200	5.8827	6.6863	0.6903	0.6940	0.6922	0.6883	0.6834	-	-	-	-	-
1.6688	1300	5.6242	6.6517	0.6856	0.6857	0.6847	0.6809	0.6762	-	-	-	-	-
1.7972	1400	5.5211	6.1428	0.7134	0.7123	0.7117	0.7065	0.7022	-	-	-	-	-
1.9255	1500	5.4882	6.0439	0.7227	0.7227	0.7214	0.7192	0.7134	-	-	-	-	-
2.0539	1600	5.4436	6.0361	0.7199	0.7203	0.7191	0.7201	0.7143	-	-	-	-	-
2.1823	1700	4.366	6.1447	0.7286	0.7290	0.7274	0.7283	0.7231	-	-	-	-	-
2.3107	1800	4.6607	6.1692	0.7365	0.7356	0.7344	0.7303	0.7263	-	-	-	-	-
2.4390	1900	4.3651	6.2109	0.7178	0.7169	0.7149	0.7134	0.7125	-	-	-	-	-
2.5674	2000	4.4692	6.1421	0.7237	0.7233	0.7214	0.7192	0.7150	-	-	-	-	-
2.6958	2100	4.434	5.9462	0.7275	0.7267	0.7260	0.7253	0.7203	-	-	-	-	-
2.8241	2200	4.2634	6.0055	0.7218	0.7216	0.7205	0.7196	0.7177	-	-	-	-	-
2.9525	2300	4.2524	5.8834	0.7297	0.7308	0.7302	0.7282	0.7245	-	-	-	-	-
3.0809	2400	3.5146	6.2635	0.7430	0.7425	0.7416	0.7402	0.7357	-	-	-	-	-
3.2092	2500	3.0137	6.1396	0.7455	0.7441	0.7430	0.7410	0.7377	-	-	-	-	-
3.3376	2600	2.9956	6.2779	0.7426	0.7427	0.7407	0.7402	0.7371	-	-	-	-	-
3.4660	2700	3.0125	6.2415	0.7459	0.7457	0.7435	0.7425	0.7378	-	-	-	-	-
3.5944	2800	3.2683	6.2214	0.7407	0.7407	0.7385	0.7378	0.7342	-	-	-	-	-
3.7227	2900	2.7818	6.2854	0.7444	0.7442	0.7422	0.7411	0.7390	-	-	-	-	-
3.8511	3000	2.7216	6.2760	0.7425	0.7429	0.7411	0.7401	0.7378	-	-	-	-	-
3.9795	3100	2.8901	6.2306	0.7435	0.7442	0.7423	0.7411	0.7389	-	-	-	-	-
-1	-1	-	-	-	-	-	-	-	0.7435	0.7442	0.7423	0.7411	0.7389

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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