SentenceTransformer
This is a sentence-transformers 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
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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]
Training Details
Training Dataset
Unnamed Dataset
- Size: 354,235 training samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label type string string int details - min: 11 tokens
- mean: 13.78 tokens
- max: 19 tokens
- min: 11 tokens
- mean: 14.8 tokens
- max: 23 tokens
- 0: ~74.10%
- 1: ~2.60%
- 2: ~23.30%
- Samples:
sentence1 sentence2 label 科目:コンクリート。名称:免震基礎天端グラウト注入。
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
科目:コンクリート。名称:免震基礎天端グラウト注入。
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:免震基礎天端グラウト注入。
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
- Loss:
sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 256per_device_eval_batch_size
: 256learning_rate
: 1e-05weight_decay
: 0.01num_train_epochs
: 4warmup_ratio
: 0.2fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 256per_device_eval_batch_size
: 256per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.01adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.2warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
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 |
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
@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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