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language:
  - jpn
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:12451
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: sbintuitions/sarashina-embedding-v2-1b
widget:
  - source_sentence: 草原で２頭のシマウマが草を食べています。
    sentences:
      - 芝の上に５体象のオブジェが置いてあります。
      - テーブルトップが大理石になってる台所です。
      - 草地にシマウマが二頭並んで草を食べています。
  - source_sentence: 三匹のシマウマが草原の上で草を食べています。
    sentences:
      - どこかの山間の草原にて放し飼いにされた馬たちが餌を食べています。
      - ノートパソコンのキーボードの上にネックレスが置いてあります。
      - テーブルに様々な食品が置いてあります。
  - source_sentence: 小さな子供がバッティングの練習をしています。
    sentences:
      - 小さな男の子がティーバッティングをしています。
      - 整備されていない道路を自動車が走っている
      - 水面に赤いくちばしの黒い鳥が一羽います。
  - source_sentence: 水辺に熊のぬいぐるみが置かれています。
    sentences:
      - 男性と女性が、歯を磨いています。
      - ぬいぐるみが水面を眺めるように置かれています。
      - 机の上にキーボードとマウスがあります。
  - source_sentence: 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。
    sentences:
      - 木立のある飛行場にプロペラ機があります。
      - 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。
datasets:
  - mteb/JSTS
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on sbintuitions/sarashina-embedding-v2-1b
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev 1792
          type: sts-dev-1792
        metrics:
          - type: pearson_cosine
            value: 0.8087868579610134
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7434852310420895
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev 1280
          type: sts-dev-1280
        metrics:
          - type: pearson_cosine
            value: 0.8078298935695407
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7442183123552939
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev 768
          type: sts-dev-768
        metrics:
          - type: pearson_cosine
            value: 0.8049498106276536
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7423127841298944
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev 256
          type: sts-dev-256
        metrics:
          - type: pearson_cosine
            value: 0.8022036966421968
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7410650407423576
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev 64
          type: sts-dev-64
        metrics:
          - type: pearson_cosine
            value: 0.7972172928220316
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7388786050712278
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 1792
          type: sts-test-1792
        metrics:
          - type: pearson_cosine
            value: 0.8087781664749797
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7435051743546024
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.8087781664749797
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7435051743546024
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 1280
          type: sts-test-1280
        metrics:
          - type: pearson_cosine
            value: 0.8078219018746986
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7442250390777712
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.8078219018746986
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7442250390777712
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 768
          type: sts-test-768
        metrics:
          - type: pearson_cosine
            value: 0.8049404729865752
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7423149875969083
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.8049404729865752
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7423149875969083
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 256
          type: sts-test-256
        metrics:
          - type: pearson_cosine
            value: 0.8022025594051618
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7410686789846747
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.8022025594051618
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7410686789846747
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 64
          type: sts-test-64
        metrics:
          - type: pearson_cosine
            value: 0.7972183575514205
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7388646166416691
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.7972183575514205
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7388646166416691
            name: Spearman Cosine

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.

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:
```
{
    "truncate_dim": 1792
}
```

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:
```
{
    "truncate_dim": 1280
}
```

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:
```
{
    "truncate_dim": 768
}
```

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:
```
{
    "truncate_dim": 256
}
```

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:
```
{
    "truncate_dim": 64
}
```

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

	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:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1792,
        1280,
        768,
        256,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

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

	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:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1792,
        1280,
        768,
        256,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

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

@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

@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

@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}
}