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cl-nagoya-sup-simcse-ja-nss-v_1_0_7_10

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:210384
loss:CategoricalContrastiveLoss
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:210384
- loss:CategoricalContrastiveLoss
widget:
- 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：ｺﾝｸﾘｰﾄ打設手間･ﾎﾟﾝﾌﾟ圧送。
- 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 0059。
- 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟ18高性能AE減水剤。備考：代価表 0032。
- 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：多目的ﾎｰﾙ機械式移動座席基礎ｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S15粗骨材20。備考：刊-ｺﾝ 2115嵩上げｺﾝ。
- 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 0064。
- source_sentence: 科目：コンクリート。名称：防振床浮き床ｺﾝｸﾘｰﾄ。
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：免震上部ｺﾝｸﾘｰﾄ。摘要：FC30 S15高性能AE減水剤。備考：代価表 0106。
- 科目：コンクリート。名称：多目的ﾎｰﾙ間柱基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0041。
- source_sentence: 科目：コンクリート。名称：EXP_J充填ｺﾝｸﾘｰﾄ。
sentences:
- 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：基礎部ｽﾛｰﾌﾟｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0048。
- 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。
- 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：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。
- 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。
- 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 0054。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) 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
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_7_10")
# Run inference
sentences = [
'科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。',
'科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。',
'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 0054。',
]
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]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Bias, Risks and Limitations
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 210,384 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 11 tokens</li><li>mean: 13.73 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 35.89 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~71.40%</li><li>1: ~2.90%</li><li>2: ~25.70%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code> | <code>1</code> |
| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>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。</code> | <code>2</code> |
| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>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 市場湧水ﾏｯﾄ保護ｺﾝ。</code> | <code>2</code> |
* Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
### 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
<details><summary>Click to expand</summary>
- `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}
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0608 | 50 | 0.3009 |
| 0.1217 | 100 | 0.1359 |
| 0.1825 | 150 | 0.095 |
| 0.2433 | 200 | 0.0808 |
| 0.3041 | 250 | 0.0724 |
| 0.3650 | 300 | 0.0757 |
| 0.4258 | 350 | 0.0608 |
| 0.4866 | 400 | 0.0607 |
| 0.5474 | 450 | 0.0549 |
| 0.6083 | 500 | 0.051 |
| 0.6691 | 550 | 0.0517 |
| 0.7299 | 600 | 0.0432 |
| 0.7908 | 650 | 0.0436 |
| 0.8516 | 700 | 0.0418 |
| 0.9124 | 750 | 0.04 |
| 0.9732 | 800 | 0.0391 |
| 1.0341 | 850 | 0.038 |
| 1.0949 | 900 | 0.0352 |
| 1.1557 | 950 | 0.0329 |
| 1.2165 | 1000 | 0.029 |
| 1.2774 | 1050 | 0.0283 |
| 1.3382 | 1100 | 0.03 |
| 1.3990 | 1150 | 0.029 |
| 1.4599 | 1200 | 0.0274 |
| 1.5207 | 1250 | 0.0261 |
| 1.5815 | 1300 | 0.0248 |
| 1.6423 | 1350 | 0.0267 |
| 1.7032 | 1400 | 0.0234 |
| 1.7640 | 1450 | 0.0218 |
| 1.8248 | 1500 | 0.0217 |
| 1.8856 | 1550 | 0.0195 |
| 1.9465 | 1600 | 0.022 |
| 2.0073 | 1650 | 0.0195 |
| 2.0681 | 1700 | 0.0165 |
| 2.1290 | 1750 | 0.0155 |
| 2.1898 | 1800 | 0.0156 |
| 2.2506 | 1850 | 0.0148 |
| 2.3114 | 1900 | 0.0135 |
| 2.3723 | 1950 | 0.0122 |
| 2.4331 | 2000 | 0.0145 |
| 2.4939 | 2050 | 0.0138 |
| 2.5547 | 2100 | 0.0133 |
| 2.6156 | 2150 | 0.0137 |
| 2.6764 | 2200 | 0.0118 |
| 2.7372 | 2250 | 0.0132 |
| 2.7981 | 2300 | 0.0132 |
| 2.8589 | 2350 | 0.0129 |
| 2.9197 | 2400 | 0.0109 |
| 2.9805 | 2450 | 0.0115 |
| 3.0414 | 2500 | 0.0083 |
| 3.1022 | 2550 | 0.0082 |
| 3.1630 | 2600 | 0.0096 |
| 3.2238 | 2650 | 0.0081 |
| 3.2847 | 2700 | 0.0081 |
| 3.3455 | 2750 | 0.0083 |
| 3.4063 | 2800 | 0.01 |
| 3.4672 | 2850 | 0.0077 |
| 3.5280 | 2900 | 0.0081 |
| 3.5888 | 2950 | 0.0088 |
| 3.6496 | 3000 | 0.0088 |
| 3.7105 | 3050 | 0.0079 |
| 3.7713 | 3100 | 0.0075 |
| 3.8321 | 3150 | 0.0079 |
| 3.8929 | 3200 | 0.0066 |
| 3.9538 | 3250 | 0.0081 |
| 4.0146 | 3300 | 0.0062 |
| 4.0754 | 3350 | 0.0058 |
| 4.1363 | 3400 | 0.0055 |
| 4.1971 | 3450 | 0.0061 |
| 4.2579 | 3500 | 0.006 |
| 4.3187 | 3550 | 0.0057 |
| 4.3796 | 3600 | 0.0057 |
| 4.4404 | 3650 | 0.0061 |
| 4.5012 | 3700 | 0.0056 |
| 4.5620 | 3750 | 0.005 |
| 4.6229 | 3800 | 0.005 |
| 4.6837 | 3850 | 0.0054 |
| 4.7445 | 3900 | 0.0045 |
| 4.8054 | 3950 | 0.0062 |
| 4.8662 | 4000 | 0.0052 |
### 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
```bibtex
@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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