Add new SentenceTransformer model

e00386e verified 4 months ago

16.3 kB

	---
	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]
	```

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	You can finetune this model on your own dataset.

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

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	## 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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