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cl-nagoya-sup-simcse-ja-nss-v_1_0_7_5

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:197418
loss:CategoricalContrastiveLoss
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cl-nagoya-sup-simcse-ja-nss-v_1_0_7_5
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:197418
- loss:CategoricalContrastiveLoss
widget:
- 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：多目的ﾎｰﾙ浮き床ｺﾝｸﾘｰﾄ。
  - 科目：コンクリート。名称：ｼﾝﾀﾞｰｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0038。
  - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
- source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。
  sentences:
  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC30+ΔS（構造体補正）S18　粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 3018K免震層上部ｺﾝ。
  - 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。
  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
- source_sentence: 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。
  sentences:
  - 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ打設手間。
  - 科目：タイル。名称：床ﾀｲﾙK。
  - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0039。
- 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：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15粗骨材20高性能AE減水剤･防水剤入。備考：刊-ｺﾝ
    3315KB基礎部ｺﾝ。
  - 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。摘要：Fc24 S18粗骨材20。備考：代価表    0123。
  - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。
- source_sentence: 科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。
  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：タイル。名称：階段段鼻ﾉﾝｽﾘｯﾌﾟ役物ﾀｲﾙ。
  - 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_5")
# Run inference
sentences = [
    '科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。',
    '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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 197,418 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: 13.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 31.5 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~61.50%</li><li>1: ~5.60%</li><li>2: ~32.90%</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        市場捨てｺﾝ。</code>                                           | <code>0</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：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体           ﾎﾟﾝﾌﾟ打設100m3/回以上       S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 No.1        市場地上部ｺﾝ（1F）。</code> | <code>0</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`: 20
- `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`: 20
- `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

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0648 | 50   | 0.2993        |
| 0.1295 | 100  | 0.1925        |
| 0.1943 | 150  | 0.1197        |
| 0.2591 | 200  | 0.1054        |
| 0.3238 | 250  | 0.0849        |
| 0.3886 | 300  | 0.0854        |
| 0.4534 | 350  | 0.0716        |
| 0.5181 | 400  | 0.0659        |
| 0.5829 | 450  | 0.0641        |
| 0.6477 | 500  | 0.0641        |
| 0.7124 | 550  | 0.0619        |
| 0.7772 | 600  | 0.0589        |
| 0.8420 | 650  | 0.0564        |
| 0.9067 | 700  | 0.0506        |
| 0.9715 | 750  | 0.0513        |
| 1.0363 | 800  | 0.0473        |
| 1.1010 | 850  | 0.0451        |
| 1.1658 | 900  | 0.044         |
| 1.2306 | 950  | 0.0418        |
| 1.2953 | 1000 | 0.042         |
| 1.3601 | 1050 | 0.0337        |
| 1.4249 | 1100 | 0.0337        |
| 1.4896 | 1150 | 0.0354        |
| 1.5544 | 1200 | 0.0353        |
| 1.6192 | 1250 | 0.0353        |
| 1.6839 | 1300 | 0.0323        |
| 1.7487 | 1350 | 0.0297        |
| 1.8135 | 1400 | 0.0331        |
| 1.8782 | 1450 | 0.0303        |
| 1.9430 | 1500 | 0.0286        |
| 2.0078 | 1550 | 0.0265        |
| 2.0725 | 1600 | 0.0257        |
| 2.1373 | 1650 | 0.0195        |
| 2.2021 | 1700 | 0.0225        |
| 2.2668 | 1750 | 0.0206        |
| 2.3316 | 1800 | 0.0231        |
| 2.3964 | 1850 | 0.0225        |
| 2.4611 | 1900 | 0.0203        |
| 2.5259 | 1950 | 0.0207        |
| 2.5907 | 2000 | 0.02          |
| 2.6554 | 2050 | 0.0181        |
| 2.7202 | 2100 | 0.0202        |
| 2.7850 | 2150 | 0.0187        |
| 2.8497 | 2200 | 0.0192        |
| 2.9145 | 2250 | 0.0168        |
| 2.9793 | 2300 | 0.0162        |
| 3.0440 | 2350 | 0.0159        |
| 3.1088 | 2400 | 0.0145        |
| 3.1736 | 2450 | 0.0134        |
| 3.2383 | 2500 | 0.0138        |
| 3.3031 | 2550 | 0.0125        |
| 3.3679 | 2600 | 0.0132        |
| 3.4326 | 2650 | 0.0122        |
| 3.4974 | 2700 | 0.0133        |
| 3.5622 | 2750 | 0.0127        |
| 3.6269 | 2800 | 0.0125        |
| 3.6917 | 2850 | 0.0107        |
| 3.7565 | 2900 | 0.0114        |
| 3.8212 | 2950 | 0.0104        |
| 3.8860 | 3000 | 0.0107        |
| 3.9508 | 3050 | 0.0112        |
| 4.0155 | 3100 | 0.0084        |
| 4.0803 | 3150 | 0.0086        |
| 4.1451 | 3200 | 0.0077        |
| 4.2098 | 3250 | 0.0098        |
| 4.2746 | 3300 | 0.0068        |
| 4.3394 | 3350 | 0.0082        |
| 4.4041 | 3400 | 0.0064        |
| 4.4689 | 3450 | 0.0083        |
| 4.5337 | 3500 | 0.0065        |
| 4.5984 | 3550 | 0.0067        |
| 4.6632 | 3600 | 0.0074        |
| 4.7280 | 3650 | 0.0078        |
| 4.7927 | 3700 | 0.0072        |
| 4.8575 | 3750 | 0.0077        |
| 4.9223 | 3800 | 0.007         |
| 4.9870 | 3850 | 0.0067        |
| 5.0518 | 3900 | 0.0057        |
| 5.1166 | 3950 | 0.0054        |
| 5.1813 | 4000 | 0.0046        |


### 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
```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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