README.md

---
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:ContrastiveLoss
widget:
- source_sentence: 猫のぬいぐるみ
  sentences:
  - 猫の人形
  - 昨日作ったのはチキンヌードル？
  - どんな魔法なの？
- source_sentence: キにスカーフが引っかかってる
  sentences:
  - 猫好き
  - どうしてキャンドルなの？
  - あの木の上のやつ、スカーフ？
- source_sentence: お気に入りの食べ物は？
  sentences:
  - 好きだよ
  - 好きな食べ物は？
  - ゆうべはなにをたべたの？
- source_sentence: あなたは魔法使いですか？
  sentences:
  - 昨晩井戸を使った？
  - 井戸へ行ったことある？
  - 魔法を使える人
- source_sentence: どう思う？
  sentences:
  - 井戸へ行ったことある？
  - なんて言った？
  - 井戸へ訪れた？
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: 0.75
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.94242924451828
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8191489361702128
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.94242924451828
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.7129629629629629
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9625
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7937482383802095
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.7352941176470589
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 606.192138671875
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.8042328042328042
      name: Dot F1
    - type: dot_f1_threshold
      value: 591.55224609375
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.6972477064220184
      name: Dot Precision
    - type: dot_recall
      value: 0.95
      name: Dot Recall
    - type: dot_ap
      value: 0.7795070974315252
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.75
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 187.74122619628906
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.8191489361702128
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 187.74122619628906
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.7129629629629629
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9625
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7929777714049108
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.75
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 8.505434036254883
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8191489361702128
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 8.505434036254883
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.7129629629629629
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9625
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7939427298225319
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.75
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 606.192138671875
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8191489361702128
      name: Max F1
    - type: max_f1_threshold
      value: 591.55224609375
      name: Max F1 Threshold
    - type: max_precision
      value: 0.7129629629629629
      name: Max Precision
    - type: max_recall
      value: 0.9625
      name: Max Recall
    - type: max_ap
      value: 0.7939427298225319
      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 = [
    'どう思う？',
    'なんて言った？',
    '井戸へ行ったことある？',
]
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]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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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              | 0.75       |
| cosine_accuracy_threshold    | 0.9424     |
| cosine_f1                    | 0.8191     |
| cosine_f1_threshold          | 0.9424     |
| cosine_precision             | 0.713      |
| cosine_recall                | 0.9625     |
| cosine_ap                    | 0.7937     |
| dot_accuracy                 | 0.7353     |
| dot_accuracy_threshold       | 606.1921   |
| dot_f1                       | 0.8042     |
| dot_f1_threshold             | 591.5522   |
| dot_precision                | 0.6972     |
| dot_recall                   | 0.95       |
| dot_ap                       | 0.7795     |
| manhattan_accuracy           | 0.75       |
| manhattan_accuracy_threshold | 187.7412   |
| manhattan_f1                 | 0.8191     |
| manhattan_f1_threshold       | 187.7412   |
| manhattan_precision          | 0.713      |
| manhattan_recall             | 0.9625     |
| manhattan_ap                 | 0.793      |
| euclidean_accuracy           | 0.75       |
| euclidean_accuracy_threshold | 8.5054     |
| euclidean_f1                 | 0.8191     |
| euclidean_f1_threshold       | 8.5054     |
| euclidean_precision          | 0.713      |
| euclidean_recall             | 0.9625     |
| euclidean_ap                 | 0.7939     |
| max_accuracy                 | 0.75       |
| max_accuracy_threshold       | 606.1921   |
| max_f1                       | 0.8191     |
| max_f1_threshold             | 591.5522   |
| max_precision                | 0.713      |
| max_recall                   | 0.9625     |
| **max_ap**                   | **0.7939** |

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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: 8.26 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.99 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~41.36%</li><li>1: ~58.64%</li></ul> |
* Samples:
  | text1                  | text2                | label          |
  |:-----------------------|:---------------------|:---------------|
  | <code>外を調べよう</code>    | <code>うん探そう</code>   | <code>0</code> |
  | <code>キャンドルが欲しい</code> | <code>キャンドル頂戴</code> | <code>1</code> |
  | <code>欲しくない</code>     | <code>家の中へ行こう</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.1,
      "size_average": true
  }
  ```

### 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: 8.46 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.04 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~41.18%</li><li>1: ~58.82%</li></ul> |
* Samples:
  | text1                      | text2                     | label          |
  |:---------------------------|:--------------------------|:---------------|
  | <code>みんなどんな魔法を使うの？</code> | <code>村人たちの魔法を教えて？</code> | <code>1</code> |
  | <code>質問なんだっけ？</code>      | <code>いやだ</code>          | <code>0</code> |
  | <code>村人はどんな呪文を使うの？</code> | <code>スパイク</code>         | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.1,
      "size_average": true
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `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`: 5
- `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    | -             | -      | 0.7819                              |
| 1.0   | 68   | 0.0059        | 0.0011 | 0.7630                              |
| 2.0   | 136  | 0.0008        | 0.0010 | 0.7782                              |
| 3.0   | 204  | 0.0007        | 0.0009 | 0.7862                              |
| 4.0   | 272  | 0.0007        | 0.0009 | 0.7916                              |
| 5.0   | 340  | 0.0007        | 0.0009 | 0.7939                              |


### 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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