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

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 <!-- at revision 1f3408afaa7b617e3445d891310a9c26dd0c68a5 -->
• —Maximum Sequence Length: 8192 tokens
• —Output Dimensionality: 1792 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —jsts
• —Language: jpn <!-- - License: Unknown -->

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, 0.8499, 0.3438],
#         [0.8499, 1.0000, 0.3127],
#         [0.3438, 0.3127, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Semantic Similarity

• —Datasets: sts-dev-1792 and sts-test-1792
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code> with these parameters:

  {
      "truncate_dim": 1792
  }

Semantic Similarity

• —Datasets: sts-dev-1280 and sts-test-1280
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code> with these parameters:

  {
      "truncate_dim": 1280
  }

Semantic Similarity

• —Datasets: sts-dev-768 and sts-test-768
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code> with these parameters:

  {
      "truncate_dim": 768
  }

Semantic Similarity

• —Datasets: sts-dev-256 and sts-test-256
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code> with these parameters:

  {
      "truncate_dim": 256
  }

Semantic Similarity

• —Datasets: sts-dev-64 and sts-test-64
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code> with these parameters:

  {
      "truncate_dim": 64
  }

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Training Details

Training Dataset

jsts

• —Dataset: jsts at b3d3097
• —Size: 12,451 training samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 10.64 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.53 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.32</li><li>max: 5.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:------------------------------------|:-----------------------------------|:-------------------------------| | <code>川べりでサーフボードを持った人たちがいます。</code> | <code>トイレの壁に黒いタオルがかけられています。</code> | <code>0.0</code> | | <code>二人の男性がジャンボジェット機を見ています。</code> | <code>2人の男性が、白い飛行機を眺めています。</code> | <code>3.799999952316284</code> | | <code>男性が子供を抱き上げて立っています。</code> | <code>坊主頭の男性が子供を抱いて立っています。</code> | <code>4.0</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Evaluation Dataset

jsts

• —Dataset: jsts at b3d3097
• —Size: 1,457 evaluation samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 10.78 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.63 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.22</li><li>max: 5.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:-----------------------------------------|:------------------------------------|:--------------------------------| | <code>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。</code> | <code>厩舎で馬と女性とが寄り添っています。</code> | <code>0.0</code> | | <code>山の上に顔の白い牛が2頭います。</code> | <code>曇り空の山肌で、牛が２匹草を食んでいます。</code> | <code>2.4000000953674316</code> | | <code>バナナを持った人が道路を通行しています。</code> | <code>道の上をバナナを背負った男性が歩いています。</code> | <code>3.5999999046325684</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

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

<details><summary>Click to expand</summary>

• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: {}

</details>

Training Logs

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",
}

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}
}

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