metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:193915
  - loss:CategoricalContrastiveLoss
widget:
  - source_sentence: 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
    sentences:
      - 科目：コンクリート。名称：設備基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表    0036。
      - 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0045。
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：防水保護ｺﾝ     人力打設工作物の基礎等     S15～S18-      
        -。備考：代価表    0013。
  - source_sentence: 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
    sentences:
      - >-
        科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=21      S15粗骨材20膨張剤
        ﾃﾞﾝｶﾊﾟﾜｰCSA20㎏/m2。備考：代価表    0061屋内土間ｺﾝｸﾘｰﾄ。
      - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表    0065。
      - 科目：タイル。名称：段床ﾀｲﾙ張り。
  - source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
    sentences:
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：捨てｺﾝｸﾘｰﾄ     ﾎﾟﾝﾌﾟ打設30m3/回程度       
        S15～S18-       圧送費､基本料別途。備考：B0-434215 No.1        市場。
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体           ﾎﾟﾝﾌﾟ打設50m3/回未満       
        S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 3101新営地上部ｺﾝｸﾘｰﾄ。
      - >-
        科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=21      S15粗骨材20。備考：B0-114112
        No.1         参資基礎部ｺﾝｸﾘｰﾄ。
  - source_sentence: 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ。
    sentences:
      - >-
        科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ。摘要：FC36 S15粗骨材20 高性能AE減水剤躯体防水材
        ﾍﾞｽﾄﾝA同等品以上。備考：代価表    0105。
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体           ﾎﾟﾝﾌﾟ打設50m3/回未満       
        S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 3101新営高流動ｺﾝｸﾘｰﾄ（免震下部基礎）。
      - >-
        科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。摘要：基礎部         ﾎﾟﾝﾌﾟ打設100m3/回以上      
        S15～S18-        圧送費､基本料別途。備考：代価表    0115。
  - source_sentence: 科目：タイル。名称：階段蹴上ﾀｲﾙ。
    sentences:
      - >-
        科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝24N/mm2   Ｓ＝１５ 徳島１。備考：B1-111111
        H2906BD     個別基礎部躯体ｺﾝｸﾘｰﾄ。
      - 科目：コンクリート。名称：免震EXP_J用充填ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表    0064。
      - 科目：タイル。名称：屋外階段A段床床ﾀｲﾙA。
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

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': 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-v_1_0_7_13")
# Run inference
sentences = [
    '科目：タイル。名称：階段蹴上ﾀｲﾙ。',
    '科目：タイル。名称：屋外階段A段床床ﾀｲﾙA。',
    '科目：コンクリート。名称：免震EXP_J用充填ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表    0064。',
]
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: 193,915 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: 13.74 tokens
max: 19 tokens

min: 11 tokens
mean: 34.76 tokens
max: 72 tokens

0: ~57.20%
1: ~5.00%
2: ~37.80%

	sentence1	sentence2	label
type	string	string	int
details	min: 11 tokens mean: 13.74 tokens max: 19 tokens	min: 11 tokens mean: 34.76 tokens max: 72 tokens	0: ~57.20% 1: ~5.00% 2: ~37.80%

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：30m3以上 50m3/回未満基本料金別途加算。備考：B0-434226 No.1 市場免震装置下部ｺﾝ。`	`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基本料金。`	`0`

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: 1
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: 1
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}
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
0.0660	50	0.0048
0.1319	100	0.0049
0.1979	150	0.0042
0.2639	200	0.0062
0.3298	250	0.0056
0.3958	300	0.005
0.4617	350	0.0048
0.5277	400	0.0049
0.5937	450	0.0043
0.6596	500	0.0051
0.7256	550	0.0046
0.7916	600	0.0045
0.8575	650	0.0043
0.9235	700	0.0049
0.9894	750	0.0038

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.52.2
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
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",
}