README.md

metadata

base_model: colorfulscoop/sbert-base-ja
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:356
  - loss:CoSENTLoss
widget:
  - source_sentence: これって？
    sentences:
      - 黄色
      - ワゴンを調べよう
      - これはなに？
  - source_sentence: リリアンってものの形を変えられる？
    sentences:
      - ただのゲームって？
      - 木にスカーフがひっかかってる?
      - それは何？
  - source_sentence: これが花
    sentences:
      - イリュージョン
      - 皿についた赤い染み
      - ぬいぐるみが花
  - source_sentence: 魔法使い
    sentences:
      - いつ手に入れたやつ？
      - 魔法をかけられる人
      - 何か思い出せることは？
  - source_sentence: リリアンってものの形を変えられる？
    sentences:
      - 例えば？
      - 井戸を調べよう
      - リリアンってものの姿を変える魔法を使える？
model-index:
  - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: custom arc semantics data
          type: custom-arc-semantics-data
        metrics:
          - type: cosine_accuracy
            value: 0.9550561797752809
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.5568578243255615
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9655172413793103
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.5568578243255615
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9824561403508771
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9491525423728814
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9932329299017532
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9438202247191011
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 281.24676513671875
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.957983193277311
            name: Dot F1
          - type: dot_f1_threshold
            value: 240.45741271972656
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.95
            name: Dot Precision
          - type: dot_recall
            value: 0.9661016949152542
            name: Dot Recall
          - type: dot_ap
            value: 0.992060744461618
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9550561797752809
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 468.22576904296875
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9655172413793103
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 486.80523681640625
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9824561403508771
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9491525423728814
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9937064750898389
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9550561797752809
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 21.117210388183594
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9655172413793103
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 21.95305633544922
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9824561403508771
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9491525423728814
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9933690931735095
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9550561797752809
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 468.22576904296875
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9655172413793103
            name: Max F1
          - type: max_f1_threshold
            value: 486.80523681640625
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9824561403508771
            name: Max Precision
          - type: max_recall
            value: 0.9661016949152542
            name: Max Recall
          - type: max_ap
            value: 0.9937064750898389
            name: Max Ap

SentenceTransformer based on colorfulscoop/sbert-base-ja

This is a sentence-transformers model finetuned from colorfulscoop/sbert-base-ja. 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: colorfulscoop/sbert-base-ja
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• 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': False, 'pooling_mode_mean_tokens': True, '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("LeoChiuu/sbert-base-ja-arc-temp")
# Run inference
sentences = [
    'リリアンってものの形を変えられる？',
    'リリアンってものの姿を変える魔法を使える？',
    '井戸を調べよう',
]
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]

Evaluation

Metrics

Binary Classification

• Dataset: custom-arc-semantics-data
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9551
cosine_accuracy_threshold	0.5569
cosine_f1	0.9655
cosine_f1_threshold	0.5569
cosine_precision	0.9825
cosine_recall	0.9492
cosine_ap	0.9932
dot_accuracy	0.9438
dot_accuracy_threshold	281.2468
dot_f1	0.958
dot_f1_threshold	240.4574
dot_precision	0.95
dot_recall	0.9661
dot_ap	0.9921
manhattan_accuracy	0.9551
manhattan_accuracy_threshold	468.2258
manhattan_f1	0.9655
manhattan_f1_threshold	486.8052
manhattan_precision	0.9825
manhattan_recall	0.9492
manhattan_ap	0.9937
euclidean_accuracy	0.9551
euclidean_accuracy_threshold	21.1172
euclidean_f1	0.9655
euclidean_f1_threshold	21.9531
euclidean_precision	0.9825
euclidean_recall	0.9492
euclidean_ap	0.9934
max_accuracy	0.9551
max_accuracy_threshold	468.2258
max_f1	0.9655
max_f1_threshold	486.8052
max_precision	0.9825
max_recall	0.9661
max_ap	0.9937

Training Details

Training Dataset

Unnamed Dataset

• Size: 356 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 4 tokens mean: 8.31 tokens max: 15 tokens	min: 4 tokens mean: 8.32 tokens max: 14 tokens	0: ~36.24% 1: ~63.76%

• Samples:

  text1	text2	label
  ジャックはどんな魔法を使うの？	見た目を変える魔法	0
  魔法使い	魔法をかけられる人	1
  ぬいぐるみが花	花がぬいぐるみに変えられている	1

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 89 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 4 tokens mean: 8.22 tokens max: 15 tokens	min: 4 tokens mean: 8.13 tokens max: 14 tokens	0: ~33.71% 1: ~66.29%

• Samples:

  text1	text2	label
  トーチ	なにも要らない	0
  家の外	家の外へ行こう	1
  お皿に赤い染みがついていたから	棚からトマトがなくなってたから	0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• learning_rate: 2e-05
• num_train_epochs: 13
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 13
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: 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
• eval_use_gather_object: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	custom-arc-semantics-data_max_ap
None	0	-	-	0.9511
1.0	45	1.9903	1.1863	0.9765
2.0	90	0.8198	1.0991	0.9873
3.0	135	0.0806	0.9033	0.9914
4.0	180	0.0024	0.7569	0.9930
5.0	225	0.0002	0.7598	0.9937
6.0	270	0.0001	0.7418	0.9937
7.0	315	0.0001	0.7322	0.9937
8.0	360	0.0001	0.7269	0.9937
9.0	405	0.0001	0.7277	0.9937
10.0	450	0.0001	0.7289	0.9937
11.0	495	0.0	0.7301	0.9937
12.0	540	0.0001	0.7299	0.9937
13.0	585	0.0001	0.7296	0.9937

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.0.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 2.20.0
• Tokenizers: 0.19.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}