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---
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:680
- loss:CoSENTLoss
widget:
- source_sentence: 中を見てみよう
sentences:
- 外を調べよう
- リリアンはどんな魔法が使えるの？
- 花がぬいぐるみに変えられている
- source_sentence: キャンドル要らない
sentences:
- なんで猫が話せる？
- 自分でやれば？
- 中を見てみよう
- source_sentence: 信用できない
sentences:
- どっちでもいいよ
- 誰？
- 誰かが呪文で花をぬいぐるみに変えた
- source_sentence: 例えば？
sentences:
- 誰かがが魔法をかけた
- ジャック
- なんでしなきゃいけないの？
- source_sentence: 魔法を使える人
sentences:
- かっこいいね
- 物の姿を変えられる人
- 町って？
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: 0.9044117647058824
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.5485918521881104
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.912751677852349
name: Cosine F1
- type: cosine_f1_threshold
value: 0.47659817337989807
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.918918918918919
name: Cosine Precision
- type: cosine_recall
value: 0.9066666666666666
name: Cosine Recall
- type: cosine_ap
value: 0.9088999169341241
name: Cosine Ap
- type: dot_accuracy
value: 0.9117647058823529
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 293.22845458984375
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9166666666666666
name: Dot F1
- type: dot_f1_threshold
value: 293.22845458984375
name: Dot F1 Threshold
- type: dot_precision
value: 0.9565217391304348
name: Dot Precision
- type: dot_recall
value: 0.88
name: Dot Recall
- type: dot_ap
value: 0.9171086358892895
name: Dot Ap
- type: manhattan_accuracy
value: 0.9117647058823529
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 524.0676879882812
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.918918918918919
name: Manhattan F1
- type: manhattan_f1_threshold
value: 524.0676879882812
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9315068493150684
name: Manhattan Precision
- type: manhattan_recall
value: 0.9066666666666666
name: Manhattan Recall
- type: manhattan_ap
value: 0.9111567321590129
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9117647058823529
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 23.82940673828125
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.918918918918919
name: Euclidean F1
- type: euclidean_f1_threshold
value: 23.82940673828125
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.9315068493150684
name: Euclidean Precision
- type: euclidean_recall
value: 0.9066666666666666
name: Euclidean Recall
- type: euclidean_ap
value: 0.9094221163568814
name: Euclidean Ap
- type: max_accuracy
value: 0.9117647058823529
name: Max Accuracy
- type: max_accuracy_threshold
value: 524.0676879882812
name: Max Accuracy Threshold
- type: max_f1
value: 0.918918918918919
name: Max F1
- type: max_f1_threshold
value: 524.0676879882812
name: Max F1 Threshold
- type: max_precision
value: 0.9565217391304348
name: Max Precision
- type: max_recall
value: 0.9066666666666666
name: Max Recall
- type: max_ap
value: 0.9171086358892895
name: Max Ap
---
# SentenceTransformer based on colorfulscoop/sbert-base-ja
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/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](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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': 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:
```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("sentence_transformers_model_id")
# Run inference
sentences = [
'魔法を使える人',
'物の姿を変えられる人',
'かっこいいね',
]
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]
```
<!--
### Direct Usage (Transformers)
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `custom-arc-semantics-data-jp`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9044 |
| cosine_accuracy_threshold | 0.5486 |
| cosine_f1 | 0.9128 |
| cosine_f1_threshold | 0.4766 |
| cosine_precision | 0.9189 |
| cosine_recall | 0.9067 |
| cosine_ap | 0.9089 |
| dot_accuracy | 0.9118 |
| dot_accuracy_threshold | 293.2285 |
| dot_f1 | 0.9167 |
| dot_f1_threshold | 293.2285 |
| dot_precision | 0.9565 |
| dot_recall | 0.88 |
| dot_ap | 0.9171 |
| manhattan_accuracy | 0.9118 |
| manhattan_accuracy_threshold | 524.0677 |
| manhattan_f1 | 0.9189 |
| manhattan_f1_threshold | 524.0677 |
| manhattan_precision | 0.9315 |
| manhattan_recall | 0.9067 |
| manhattan_ap | 0.9112 |
| euclidean_accuracy | 0.9118 |
| euclidean_accuracy_threshold | 23.8294 |
| euclidean_f1 | 0.9189 |
| euclidean_f1_threshold | 23.8294 |
| euclidean_precision | 0.9315 |
| euclidean_recall | 0.9067 |
| euclidean_ap | 0.9094 |
| max_accuracy | 0.9118 |
| max_accuracy_threshold | 524.0677 |
| max_f1 | 0.9189 |
| max_f1_threshold | 524.0677 |
| max_precision | 0.9565 |
| max_recall | 0.9067 |
| **max_ap** | **0.9171** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 680 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 680 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
* Samples:
| text1 | text2 | label |
|:----------------------------|:----------------------------|:---------------|
| <code>いらない</code> | <code>うんよろしく</code> | <code>0</code> |
| <code>足元よりも更に深くってどこ？</code> | <code>足元よりも更に深くってなに？</code> | <code>1</code> |
| <code>他にはないの？</code> | <code>どう思う？</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### csv
* Dataset: csv
* Size: 680 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 680 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
* Samples:
| text1 | text2 | label |
|:-------------------------|:-------------------------|:---------------|
| <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code> | <code>1</code> |
| <code>夕飯は何だったの？</code> | <code>チキンヌードル食べた？</code> | <code>0</code> |
| <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
| None | 0 | - | - | 0.8596 |
| 1.0 | 68 | 2.6802 | 1.7807 | 0.8872 |
| 2.0 | 136 | 1.4014 | 1.7683 | 0.8945 |
| 3.0 | 204 | 0.7937 | 1.9877 | 0.9039 |
| 4.0 | 272 | 0.5443 | 1.9106 | 0.9075 |
| 5.0 | 340 | 0.4225 | 1.9418 | 0.9109 |
| 6.0 | 408 | 0.3347 | 2.0123 | 0.9107 |
| 7.0 | 476 | 0.3425 | 2.0387 | 0.9094 |
| 8.0 | 544 | 0.2427 | 1.9878 | 0.9103 |
| 9.0 | 612 | 0.2412 | 2.0424 | 0.9178 |
| 10.0 | 680 | 0.1623 | 2.0273 | 0.9188 |
| 11.0 | 748 | 0.1909 | 2.0955 | 0.9220 |
| 12.0 | 816 | 0.1507 | 2.2124 | 0.9157 |
| 13.0 | 884 | 0.1406 | 2.2126 | 0.9171 |
### 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
```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",
}
```
#### CoSENTLoss
```bibtex
@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},
}
```
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