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MARK-Embedding

Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
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SentenceTransformer

これは、訓練済みのsentence-transformersモデルです。このモデルは、文と段落を256次元の密なベクトル空間にマッピングし、意味的テキスト類似性、意味検索、言い換えマイニング、テキスト分類、クラスタリングなどに使用できます。

モデル詳細

image/png

モデルの説明

  • モデルタイプ: Sentence Transformer
  • 最大シーケンス長: 2048トークン
  • 出力次元数: 256次元
  • 類似度関数: コサイン類似度

モデルのソース

完全なモデルアーキテクチャ 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'word_embedding_dimension': 256, '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})
)

使用方法

直接使用 (Sentence Transformers)

まず、Sentence Transformersライブラリをインストールします:

pip install -U sentence-transformers

次に、このモデルをロードして推論を実行できます。

from sentence_transformers import SentenceTransformer

# 🤗 Hubからダウンロード
model = SentenceTransformer("sentence_transformers_model_id")
# 推論を実行
sentences = [
    'Comcast Class A shares were up 8 cents at $30.50 in morning trading on the Nasdaq Stock Market.',
    'The stock rose 48 cents to $30 yesterday in Nasdaq Stock Market trading.',
    'Malaysia: Chinese satellite found object in ocean',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

# 埋め込みベクトルの類似度スコアを取得
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5752, 0.2980],
#         [0.5752, 1.0000, 0.2161],
#         [0.2980, 0.2161, 1.0000]])

評価

メトリクス

意味的類似性

メトリクス 値
pearson_cosine 0.464
spearman_cosine 0.4595

訓練詳細

訓練データセット

名称未設定のデータセット

  • サイズ: 5,749 訓練サンプル
  • カラム: sentence_0, sentence_1, label
  • 最初の1000サンプルに基づくおおよその統計:
    sentence_0 sentence_1 label
    型 string string float
    詳細
    • 最小: 6 トークン
    • 平均: 14.76 トークン
    • 最大: 55 トークン
    • 最小: 6 トークン
    • 平均: 14.73 トークン
    • 最大: 57 トークン
    • 最小: 0.0
    • 平均: 0.55
    • 最大: 1.0
  • サンプル:
    sentence_0 sentence_1 label
    Forecasters said warnings might go up for Cuba later Thursday. Watches or warnings could be issued for eastern Cuba later on Thursday. 0.8
    Death toll in Lebanon bombings rises to 47 1 suspect arrested after Lebanon car bombings kill 45 0.5599999904632569
    Three dogs running on a racetrack. Three dogs round a bend at a racetrack. 0.9600000381469727
  • 損失関数: CosineSimilarityLoss 以下のパラメータを使用:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

訓練ハイパーパラメータ

デフォルト以外のハイパーパラメータ

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • multi_dataset_batch_sampler: round_robin

すべてのハイパーパラメータ

クリックして展開
  • 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
  • num_train_epochs: 3
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • 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: 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: 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: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

訓練ログ

エポック ステップ 訓練損失 spearman_cosine
1.0 360 - 0.2967
1.3889 500 0.11 0.3338
2.0 720 - 0.3665
2.7778 1000 0.0857 0.4101
3.0 1080 - 0.4595

フレームワークのバージョン

  • Python: 3.12.11
  • Sentence Transformers: 5.1.0
  • Transformers: 4.56.1
  • PyTorch: 2.8.0+cu126
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.22.0

引用

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",
}
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Evaluation results

  • ピアソン相関係数 (コサイン類似度)
    self-reported
    0.464
  • スピアマン相関係数 (コサイン類似度)
    self-reported
    0.460