metadata
base_model: colorfulscoop/sbert-base-ja
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:281
- loss:CoSENTLoss
widget:
- source_sentence: オッケー
sentences:
- どっち?
- 青いオーブがどこにあるか知ってる?
- ハーイ
- source_sentence: 昨日作ったのはビーフシチュー?
sentences:
- 魔法の残り香
- 家の外へ行こう
- 赤い染みが皿にあった
- source_sentence: 外を見てみよう
sentences:
- 物の見た目を変えられる魔法を使える人を知っている?
- これって?
- いつ手に入れたやつ?
- source_sentence: やっぱり、キャンドルがいい
sentences:
- 物体を変える
- きらい
- タイマツがいいな
- source_sentence: 棚からトマトがなくなってたから
sentences:
- こんにちは
- お鍋から辛い匂いがしたから
- スカーフは布袋の中?
model-index:
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: custom arc semantics data jp
type: custom-arc-semantics-data-jp
metrics:
- type: cosine_accuracy
value: 0.9824561403508771
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.9462370276451111
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0
name: Cosine F1
- type: cosine_f1_threshold
value: 0
name: Cosine F1 Threshold
- type: cosine_precision
value: 0
name: Cosine Precision
- type: cosine_recall
value: 0
name: Cosine Recall
- type: cosine_ap
value: -0.0
name: Cosine Ap
- type: dot_accuracy
value: 0.9824561403508771
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 546.0494384765625
name: Dot Accuracy Threshold
- type: dot_f1
value: 0
name: Dot F1
- type: dot_f1_threshold
value: 0
name: Dot F1 Threshold
- type: dot_precision
value: 0
name: Dot Precision
- type: dot_recall
value: 0
name: Dot Recall
- type: dot_ap
value: -0.0
name: Dot Ap
- type: manhattan_accuracy
value: 0.9824561403508771
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 119.43295288085938
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0
name: Manhattan F1
- type: manhattan_f1_threshold
value: 0
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0
name: Manhattan Precision
- type: manhattan_recall
value: 0
name: Manhattan Recall
- type: manhattan_ap
value: -0.0
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9824561403508771
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 5.491857528686523
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0
name: Euclidean Precision
- type: euclidean_recall
value: 0
name: Euclidean Recall
- type: euclidean_ap
value: -0.0
name: Euclidean Ap
- type: max_accuracy
value: 0.9824561403508771
name: Max Accuracy
- type: max_accuracy_threshold
value: 546.0494384765625
name: Max Accuracy Threshold
- type: max_f1
value: 0
name: Max F1
- type: max_f1_threshold
value: 0
name: Max F1 Threshold
- type: max_precision
value: 0
name: Max Precision
- type: max_recall
value: 0
name: Max Recall
- type: max_ap
value: -0.0
name: Max Ap
SentenceTransformer based on colorfulscoop/sbert-base-ja
This is a sentence-transformers model finetuned from colorfulscoop/sbert-base-ja on the csv dataset. 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
- Training Dataset:
- csv
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("sentence_transformers_model_id")
# Run inference
sentences = [
'棚からトマトがなくなってたから',
'お鍋から辛い匂いがしたから',
'スカーフは布袋の中?',
]
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-jp
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9825 |
cosine_accuracy_threshold | 0.9462 |
cosine_f1 | 0 |
cosine_f1_threshold | 0 |
cosine_precision | 0 |
cosine_recall | 0 |
cosine_ap | -0.0 |
dot_accuracy | 0.9825 |
dot_accuracy_threshold | 546.0494 |
dot_f1 | 0 |
dot_f1_threshold | 0 |
dot_precision | 0 |
dot_recall | 0 |
dot_ap | -0.0 |
manhattan_accuracy | 0.9825 |
manhattan_accuracy_threshold | 119.433 |
manhattan_f1 | 0 |
manhattan_f1_threshold | 0 |
manhattan_precision | 0 |
manhattan_recall | 0 |
manhattan_ap | -0.0 |
euclidean_accuracy | 0.9825 |
euclidean_accuracy_threshold | 5.4919 |
euclidean_f1 | 0 |
euclidean_f1_threshold | 0 |
euclidean_precision | 0 |
euclidean_recall | 0 |
euclidean_ap | -0.0 |
max_accuracy | 0.9825 |
max_accuracy_threshold | 546.0494 |
max_f1 | 0 |
max_f1_threshold | 0 |
max_precision | 0 |
max_recall | 0 |
max_ap | -0.0 |
Training Details
Training Dataset
csv
- Dataset: csv
- Size: 281 training samples
- Columns:
text1
,text2
, andlabel
- Approximate statistics based on the first 281 samples:
text1 text2 label type string string int details - min: 4 tokens
- mean: 8.56 tokens
- max: 15 tokens
- min: 4 tokens
- mean: 7.49 tokens
- max: 14 tokens
- 0: 100.00%
- Samples:
text1 text2 label キャンドルがいいな
タイマツ
0
物の形を変えられる人
物の形を変えられる人
0
イベントについて教えて
リリアンって誰?
0
- Loss:
CoSENTLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_cos_sim" }
Evaluation Dataset
csv
- Dataset: csv
- Size: 281 evaluation samples
- Columns:
text1
,text2
, andlabel
- Approximate statistics based on the first 281 samples:
text1 text2 label type string string int details - min: 4 tokens
- mean: 8.86 tokens
- max: 14 tokens
- min: 4 tokens
- mean: 8.02 tokens
- max: 14 tokens
- 0: 100.00%
- Samples:
text1 text2 label だれかが魔法で花をぬいぐるみに変えた
これって?
0
他の選択肢をちょうだい
どこを探したい?
0
昨日作ったのはビーフシチュー?
赤い染みが皿にあった
0
- Loss:
CoSENTLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochlearning_rate
: 2e-05num_train_epochs
: 5warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_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
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_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
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
---|---|---|---|---|
0 | 0 | - | - | -0.0 |
1.0 | 28 | 0.0 | 0.0 | -0.0 |
2.0 | 56 | 0.0 | 0.0 | -0.0 |
3.0 | 84 | 0.0 | 0.0 | -0.0 |
4.0 | 112 | 0.0 | 0.0 | -0.0 |
5.0 | 140 | 0.0 | 0.0 | -0.0 |
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- 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},
}