---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:366717
- loss:CategoricalContrastiveLoss
widget:
- source_sentence: 科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。
sentences:
- 科目：コンクリート。名称：#F/#FLｺﾝｸﾘｰﾄ打設手間。
- 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：EXP_J上床磁器質ﾀｲﾙA。
- source_sentence: 科目：タイル。名称：段床ﾀｲﾙ。
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：地流し床ﾀｲﾙ。
- 科目：タイル。名称：#階WWC洗面台壁ﾓｻﾞｲｸﾀｲﾙ-#。
- source_sentence: 科目：タイル。名称：屋外階段踊場ﾀｲﾙ。
sentences:
- 科目：タイル。名称：手洗い水周りﾀｲﾙ（A）。
- 科目：タイル。名称：ﾀｲﾙ出隅ｺｰﾅｰ。
- 科目：タイル。名称：#階WWC洗面台壁ﾓｻﾞｲｸﾀｲﾙ-#。
- source_sentence: 科目：タイル。名称：ﾃﾞｯｷ床ﾀｲﾙ。
sentences:
- 科目：タイル。名称：昇降口床ﾀｲﾙ張り。
- 科目：タイル。名称：床磁器質ﾀｲﾙA。
- 科目：タイル。名称：ﾋﾟﾛﾃｨ柱壁ﾀｲﾙA。
- source_sentence: 科目：タイル。名称：床ﾀｲﾙ。
sentences:
- 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
- 科目：タイル。名称：段鼻ﾀｲﾙ。
- 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
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
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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_9_1")
# Run inference
sentences = [
'科目：タイル。名称：床ﾀｲﾙ。',
'科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。',
'科目：タイル。名称：段鼻ﾀｲﾙ。',
]
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: 366,717 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: 13.8 tokens
- max: 19 tokens
| - min: 11 tokens
- mean: 14.78 tokens
- max: 23 tokens
| - 0: ~66.70%
- 1: ~3.50%
- 2: ~29.80%
|
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------|:-------------------------------------------------|:---------------|
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部（外周基礎梁）ｺﾝｸﾘｰﾄ打設手間。 | 0 |
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。 | 0 |
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。 | 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
- `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`: 3
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional