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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
Model card Files
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Community
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cl-nagoya-sup-simcse-ja-nss-v1_0_7_8
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---

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](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-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]

```

<!--
### 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: 182,343 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: 13.32 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.8 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~68.50%</li><li>1: ~4.50%</li><li>2: ~27.00%</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        市場基礎部ﾏｽｺﾝ。</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}

- `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 |
|:------:|:----:|:-------------:|
| 0.0701 | 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
```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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