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cl-nagoya-sup-simcse-ja-nss-v1_0_7_8

Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:182343
loss:CategoricalContrastiveLoss
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metadata
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:182343
  - loss:CategoricalContrastiveLoss
widget:
  - source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
    sentences:
      - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S18粗骨材20。備考：刊-ｺﾝ 2118EXP.J面ｺﾝ。
      - 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。
      - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
  - source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。
    sentences:
      - >-
        科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表   
        0059。
      - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0040。
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝18N/mm2   Ｓ＝１８ 徳島１。備考：B1-111111
        H2906BD     個別嵩上げｺﾝｸﾘｰﾄ。
  - source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
    sentences:
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1       
        市場地上部ｺﾝ（5F）。
      - >-
        科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC48 ﾌﾛｰ60粗骨材20高性能AE減水剤。備考：刊-ｺﾝ
        4860K免震装置下部ｺﾝ。
      - 科目：コンクリート。名称：防水押えｺﾝｸﾘｰﾄ。
  - source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
    sentences:
      - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
      - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S18粗骨材地上部。備考：代価表    0060。
      - >-
        科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表   
        0059。
  - source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
    sentences:
      - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
      - >-
        科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112
        H22.11     協議防水保護ｺﾝｸﾘｰﾄ。
      - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-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
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, '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': False, 'include_prompt': True})
)

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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8")
# Run inference
sentences = [
    '科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。',
    '科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。',
    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112 H22.11     協議防水保護ｺﾝｸﾘｰﾄ。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 182,343 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string int
    details
    • min: 11 tokens
    • mean: 13.32 tokens
    • max: 19 tokens
    • min: 11 tokens
    • mean: 34.8 tokens
    • max: 72 tokens
    • 0: ~68.50%
    • 1: ~4.50%
    • 2: ~27.00%
  • Samples:
    sentence1 sentence2 label
    科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。 1
    科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場免震層下部ｺﾝ。 2
    科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場基礎部ﾏｽｺﾝ。 2
  • Loss: sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 256
  • per_device_eval_batch_size: 256
  • learning_rate: 1e-05
  • weight_decay: 0.01
  • num_train_epochs: 10
  • warmup_ratio: 0.2
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 256
  • per_device_eval_batch_size: 256
  • 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: 1e-05
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 10
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.2
  • 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}
  • tp_size: 0
  • 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}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • 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
  • 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
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss
0.0701 50 0.2825
0.1403 100 0.1467
0.2104 150 0.0947
0.2805 200 0.0839
0.3506 250 0.0769
0.4208 300 0.0684
0.4909 350 0.0625
0.5610 400 0.0582
0.6311 450 0.0579
0.7013 500 0.0514
0.7714 550 0.0514
0.8415 600 0.0448
0.9116 650 0.0436
0.9818 700 0.0422
1.0519 750 0.0371
1.1220 800 0.0377
1.1921 850 0.0353
1.2623 900 0.0354
1.3324 950 0.0325
1.4025 1000 0.0328
1.4727 1050 0.0302
1.5428 1100 0.0259
1.6129 1150 0.0267
1.6830 1200 0.0274
1.7532 1250 0.0262
1.8233 1300 0.0234
1.8934 1350 0.0244
1.9635 1400 0.0238
2.0337 1450 0.02
2.1038 1500 0.0187
2.1739 1550 0.0185
2.2440 1600 0.0178
2.3142 1650 0.016
2.3843 1700 0.0169
2.4544 1750 0.0171
2.5245 1800 0.0146
2.5947 1850 0.0145
2.6648 1900 0.0146
2.7349 1950 0.0139
2.8050 2000 0.0119
2.8752 2050 0.0131
2.9453 2100 0.0124
3.0154 2150 0.011
3.0856 2200 0.0109
3.1557 2250 0.0103
3.2258 2300 0.0102
3.2959 2350 0.0089
3.3661 2400 0.0083
3.4362 2450 0.0095
3.5063 2500 0.0085
3.5764 2550 0.009
3.6466 2600 0.0083
3.7167 2650 0.0093
3.7868 2700 0.0084
3.8569 2750 0.0084
3.9271 2800 0.0088
3.9972 2850 0.0086
4.0673 2900 0.0057
4.1374 2950 0.0078
4.2076 3000 0.0062
4.2777 3050 0.0066
4.3478 3100 0.006
4.4180 3150 0.0078
4.4881 3200 0.0056
4.5582 3250 0.0064
4.6283 3300 0.0063
4.6985 3350 0.0058
4.7686 3400 0.005
4.8387 3450 0.0057
4.9088 3500 0.0059
4.9790 3550 0.0063
5.0491 3600 0.0046
5.1192 3650 0.0041
5.1893 3700 0.005
5.2595 3750 0.0043
5.3296 3800 0.0046
5.3997 3850 0.0041
5.4698 3900 0.006
5.5400 3950 0.0052
5.6101 4000 0.0043

Framework Versions

  • Python: 3.11.12
  • Sentence Transformers: 4.1.0
  • Transformers: 4.51.3
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.6.0
  • Datasets: 2.14.4
  • Tokenizers: 0.21.1

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",
}