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-v_1_0_3")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：HWC荷物棚。',
    '科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。',
    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=36       S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ   2。',
]
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: 11,961 training samples
Columns: sentence and label

Approximate statistics based on the first 1000 samples:

	sentence	label
type	string	int
details	min: 11 tokens mean: 18.2 tokens max: 54 tokens	0: ~0.30% 1: ~0.30% 2: ~0.30% 3: ~0.30% 4: ~0.30% 5: ~1.10% 6: ~0.30% 7: ~0.30% 8: ~0.30% 9: ~0.30% 10: ~0.30% 11: ~0.30% 12: ~0.30% 13: ~0.30% 14: ~0.30% 15: ~0.30% 16: ~0.40% 17: ~0.30% 18: ~0.30% 19: ~0.30% 20: ~0.90% 21: ~0.30% 22: ~0.40% 23: ~0.30% 24: ~1.10% 25: ~0.30% 26: ~0.30% 27: ~0.30% 28: ~0.30% 29: ~0.30% 30: ~0.30% 31: ~0.30% 32: ~0.30% 33: ~0.30% 34: ~0.30% 35: ~0.30% 36: ~0.30% 37: ~0.30% 38: ~0.30% 39: ~0.30% 40: ~0.30% 41: ~0.30% 42: ~0.40% 43: ~0.30% 44: ~0.30% 45: ~0.30% 46: ~0.60% 47: ~0.70% 48: ~0.30% 49: ~0.30% 50: ~0.30% 51: ~0.30% 52: ~0.30% 53: ~0.30% 54: ~0.30% 55: ~0.30% 56: ~0.30% 57: ~0.30% 58: ~0.30% 59: ~0.30% 60: ~0.30% 61: ~0.50% 62: ~0.30% 63: ~0.30% 64: ~0.30% 65: ~0.30% 66: ~0.30% 67: ~0.30% 68: ~0.30% 69: ~0.30% 70: ~0.30% 71: ~0.30% 72: ~0.30% 73: ~0.30% 74: ~0.30% 75: ~0.30% 76: ~0.30% 77: ~0.80% 78: ~0.60% 79: ~0.30% 80: ~0.30% 81: ~0.30% 82: ~0.30% 83: ~0.30% 84: ~0.30% 85: ~0.30% 86: ~0.50% 87: ~0.30% 88: ~0.30% 89: ~0.30% 90: ~0.30% 91: ~0.80% 92: ~0.60% 93: ~0.50% 94: ~0.30% 95: ~0.30% 96: ~16.50% 97: ~0.30% 98: ~0.30% 99: ~0.30% 100: ~0.30% 101: ~0.30% 102: ~0.30% 103: ~0.30% 104: ~0.30% 105: ~0.50% 106: ~0.30% 107: ~0.30% 108: ~0.30% 109: ~0.30% 110: ~0.30% 111: ~0.30% 112: ~0.30% 113: ~0.30% 114: ~0.70% 115: ~0.30% 116: ~0.30% 117: ~0.30% 118: ~0.40% 119: ~2.10% 120: ~2.10% 121: ~0.30% 122: ~0.30% 123: ~0.50% 124: ~0.50% 125: ~0.50% 126: ~0.40% 127: ~0.30% 128: ~0.30% 129: ~0.30% 130: ~0.80% 131: ~0.30% 132: ~0.30% 133: ~0.30% 134: ~0.30% 135: ~0.30% 136: ~0.30% 137: ~0.30% 138: ~0.30% 139: ~0.30% 140: ~0.30% 141: ~0.30% 142: ~0.30% 143: ~0.50% 144: ~0.30% 145: ~0.40% 146: ~0.30% 147: ~0.30% 148: ~0.30% 149: ~0.30% 150: ~0.30% 151: ~0.30% 152: ~0.30% 153: ~0.30% 154: ~0.30% 155: ~0.30% 156: ~0.30% 157: ~0.40% 158: ~0.30% 159: ~0.30% 160: ~0.30% 161: ~0.30% 162: ~0.30% 163: ~0.30% 164: ~0.70% 165: ~0.30% 166: ~0.30% 167: ~0.30% 168: ~1.30% 169: ~0.30% 170: ~0.40% 171: ~0.30% 172: ~0.30% 173: ~0.30% 174: ~1.50% 175: ~0.30% 176: ~0.30% 177: ~0.30% 178: ~0.30% 179: ~0.30% 180: ~0.30% 181: ~0.30% 182: ~1.60% 183: ~0.30% 184: ~0.30% 185: ~7.20% 186: ~0.30% 187: ~1.00% 188: ~0.30% 189: ~0.30% 190: ~0.30% 191: ~1.80% 192: ~0.30% 193: ~0.50% 194: ~0.70% 195: ~0.30%

Samples:

sentence	label
`科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。`	`0`
`科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。`	`0`
`科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。`	`0`

Loss: sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 512
per_device_eval_batch_size: 512
learning_rate: 1e-05
weight_decay: 0.01
num_train_epochs: 200
warmup_ratio: 0.15
fp16: True
batch_sampler: group_by_label

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 512
per_device_eval_batch_size: 512
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: 200
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.15
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: group_by_label
multi_dataset_batch_sampler: proportional

Training Logs