Add new SentenceTransformer model

e157093 verified 6 months ago

15.8 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:354235
	- loss:CategoricalContrastiveLoss
	widget:
	- source_sentence: 科目：コンクリート。名称：免震上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。
	- 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
	- 科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。
	- 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：タイル。名称：ﾋﾟﾛﾃｨ床床ﾀｲﾙ張り。
	- 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。
	- 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。
	- source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。
	sentences:
	- 科目：コンクリート。名称：保護ｺﾝｸﾘｰﾄ。
	- 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。
	- 科目：タイル。名称：EV#･#床磁器質ﾀｲﾙA（材のみ）。
	- source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。
	sentences:
	- 科目：コンクリート。名称：体育館防振床浮き床ｺﾝｸﾘｰﾄ。
	- 科目：タイル。名称：EXP_J上床磁器質ﾀｲﾙA。
	- 科目：タイル。名称：床ﾀｲﾙB。
	- source_sentence: 科目：タイル。名称：ﾀｲﾙ出隅ｺｰﾅｰ。
	sentences:
	- 科目：タイル。名称：地流し床ﾀｲﾙ。
	- 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。
	- 科目：タイル。名称：段床ﾀｲﾙ。
	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_8_4")
	# Run inference
	sentences = [
	'科目：タイル。名称：ﾀｲﾙ出隅ｺｰﾅｰ。',
	'科目：タイル。名称：段床ﾀｲﾙ。',
	'科目：タイル。名称：地流し床ﾀｲﾙ。',
	]
	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]
	```

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	### Downstream Usage (Sentence Transformers)

	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: 354,235 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: 13.78 tokens</li><li>max: 19 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>0: ~74.10%</li><li>1: ~2.60%</li><li>2: ~23.30%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:-----------------------------------------\|:-------------------------------------------------\|:---------------\|
	\| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>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。</code> \| <code>0</code> \|
	\| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。</code> \| <code>0</code> \|
	\| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>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。</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`: 4
	- `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`: 4
	- `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
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.0361 \| 50 \| 0.0492 \|
	\| 0.0723 \| 100 \| 0.0438 \|
	\| 0.1084 \| 150 \| 0.0385 \|
	\| 0.1445 \| 200 \| 0.0376 \|
	\| 0.1806 \| 250 \| 0.0388 \|
	\| 0.2168 \| 300 \| 0.0391 \|
	\| 0.2529 \| 350 \| 0.0337 \|
	\| 0.2890 \| 400 \| 0.0354 \|
	\| 0.3251 \| 450 \| 0.0322 \|
	\| 0.3613 \| 500 \| 0.0345 \|
	\| 0.3974 \| 550 \| 0.0278 \|
	\| 0.4335 \| 600 \| 0.0282 \|
	\| 0.4697 \| 650 \| 0.0261 \|
	\| 0.5058 \| 700 \| 0.0284 \|
	\| 0.5419 \| 750 \| 0.0264 \|
	\| 0.5780 \| 800 \| 0.0251 \|
	\| 0.6142 \| 850 \| 0.02 \|
	\| 0.6503 \| 900 \| 0.0253 \|
	\| 0.6864 \| 950 \| 0.0197 \|
	\| 0.7225 \| 1000 \| 0.0221 \|
	\| 0.7587 \| 1050 \| 0.0204 \|
	\| 0.7948 \| 1100 \| 0.0188 \|
	\| 0.8309 \| 1150 \| 0.0198 \|
	\| 0.8671 \| 1200 \| 0.0203 \|
	\| 0.9032 \| 1250 \| 0.0177 \|
	\| 0.9393 \| 1300 \| 0.0162 \|
	\| 0.9754 \| 1350 \| 0.0148 \|
	\| 1.0116 \| 1400 \| 0.014 \|
	\| 1.0477 \| 1450 \| 0.011 \|
	\| 1.0838 \| 1500 \| 0.0123 \|
	\| 1.1199 \| 1550 \| 0.0126 \|
	\| 1.1561 \| 1600 \| 0.01 \|
	\| 1.1922 \| 1650 \| 0.0124 \|
	\| 1.2283 \| 1700 \| 0.0107 \|
	\| 1.2645 \| 1750 \| 0.0107 \|
	\| 1.3006 \| 1800 \| 0.0118 \|
	\| 1.3367 \| 1850 \| 0.0103 \|
	\| 1.3728 \| 1900 \| 0.0102 \|
	\| 1.4090 \| 1950 \| 0.0104 \|
	\| 1.4451 \| 2000 \| 0.01 \|
	\| 1.4812 \| 2050 \| 0.0101 \|
	\| 1.5173 \| 2100 \| 0.0098 \|
	\| 1.5535 \| 2150 \| 0.0097 \|
	\| 1.5896 \| 2200 \| 0.0093 \|
	\| 1.6257 \| 2250 \| 0.0088 \|
	\| 1.6618 \| 2300 \| 0.0095 \|
	\| 1.6980 \| 2350 \| 0.0103 \|
	\| 1.7341 \| 2400 \| 0.0077 \|
	\| 1.7702 \| 2450 \| 0.0085 \|
	\| 1.8064 \| 2500 \| 0.0082 \|
	\| 1.8425 \| 2550 \| 0.0074 \|
	\| 1.8786 \| 2600 \| 0.0081 \|
	\| 1.9147 \| 2650 \| 0.0067 \|
	\| 1.9509 \| 2700 \| 0.0082 \|
	\| 1.9870 \| 2750 \| 0.0076 \|
	\| 2.0231 \| 2800 \| 0.0067 \|
	\| 2.0592 \| 2850 \| 0.0056 \|
	\| 2.0954 \| 2900 \| 0.0065 \|
	\| 2.1315 \| 2950 \| 0.0057 \|
	\| 2.1676 \| 3000 \| 0.0059 \|
	\| 2.2038 \| 3050 \| 0.0047 \|
	\| 2.2399 \| 3100 \| 0.0051 \|
	\| 2.2760 \| 3150 \| 0.0049 \|
	\| 2.3121 \| 3200 \| 0.0051 \|
	\| 2.3483 \| 3250 \| 0.0049 \|
	\| 2.3844 \| 3300 \| 0.0045 \|
	\| 2.4205 \| 3350 \| 0.0047 \|
	\| 2.4566 \| 3400 \| 0.0052 \|
	\| 2.4928 \| 3450 \| 0.004 \|
	\| 2.5289 \| 3500 \| 0.0057 \|
	\| 2.5650 \| 3550 \| 0.0046 \|
	\| 2.6012 \| 3600 \| 0.0052 \|
	\| 2.6373 \| 3650 \| 0.0049 \|
	\| 2.6734 \| 3700 \| 0.0046 \|
	\| 2.7095 \| 3750 \| 0.0056 \|
	\| 2.7457 \| 3800 \| 0.0054 \|
	\| 2.7818 \| 3850 \| 0.0037 \|
	\| 2.8179 \| 3900 \| 0.0044 \|
	\| 2.8540 \| 3950 \| 0.0037 \|
	\| 2.8902 \| 4000 \| 0.0049 \|
	\| 2.9263 \| 4050 \| 0.0044 \|
	\| 2.9624 \| 4100 \| 0.0046 \|
	\| 2.9986 \| 4150 \| 0.0041 \|
	\| 3.0347 \| 4200 \| 0.0044 \|
	\| 3.0708 \| 4250 \| 0.0035 \|
	\| 3.1069 \| 4300 \| 0.0029 \|
	\| 3.1431 \| 4350 \| 0.0035 \|
	\| 3.1792 \| 4400 \| 0.0031 \|
	\| 3.2153 \| 4450 \| 0.0038 \|
	\| 3.2514 \| 4500 \| 0.0039 \|
	\| 3.2876 \| 4550 \| 0.0034 \|
	\| 3.3237 \| 4600 \| 0.0043 \|
	\| 3.3598 \| 4650 \| 0.0042 \|
	\| 3.3960 \| 4700 \| 0.004 \|
	\| 3.4321 \| 4750 \| 0.0028 \|
	\| 3.4682 \| 4800 \| 0.0035 \|
	\| 3.5043 \| 4850 \| 0.0033 \|
	\| 3.5405 \| 4900 \| 0.0039 \|
	\| 3.5766 \| 4950 \| 0.0045 \|
	\| 3.6127 \| 5000 \| 0.0032 \|
	\| 3.6488 \| 5050 \| 0.0036 \|
	\| 3.6850 \| 5100 \| 0.0032 \|
	\| 3.7211 \| 5150 \| 0.0031 \|
	\| 3.7572 \| 5200 \| 0.0043 \|
	\| 3.7934 \| 5250 \| 0.0032 \|
	\| 3.8295 \| 5300 \| 0.0034 \|
	\| 3.8656 \| 5350 \| 0.0029 \|
	\| 3.9017 \| 5400 \| 0.0037 \|
	\| 3.9379 \| 5450 \| 0.0028 \|
	\| 3.9740 \| 5500 \| 0.0028 \|

	</details>

	### Framework Versions
	- Python: 3.11.13
	- Sentence Transformers: 4.1.0
	- Transformers: 4.52.4
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.8.1
	- 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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