Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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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:5079
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: イリュージョンを使うの？
+  sentences:
+  - なんて言った？
+  - あなた
+  - 他にはある？
+- source_sentence: あなたは呪文が使えますか？
+  sentences:
+  - どっちを選ぶ？
+  - 屋根裏部屋の猫のぬいぐるみ
+  - 誰？
+- source_sentence: リリアンってものの形を変えられる？
+  sentences:
+  - どっちがいいと思う？
+  - 中に何か大事な物がある。
+  - 自分は今どこにいる？
+- source_sentence: どんな魔法なの？
+  sentences:
+  - だめじゃん
+  - こんばんは
+  - 井戸へ行った？
+- source_sentence: 屋根裏部屋に猫のぬいぐるいみがあった
+  sentences:
+  - なにが欲しい？
+  - もしもし
+  - センパーイ
+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: 0.9598425196850394
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.5491387844085693
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.8675324675324675
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.5491387844085693
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.8477157360406091
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8882978723404256
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.8892976796593005
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.9582677165354331
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 288.5785217285156
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.8608923884514437
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 277.5242919921875
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.8497409326424871
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.8723404255319149
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.9083604180944419
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.9598425196850394
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 440.51263427734375
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.8675324675324675
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 466.3338623046875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.8477157360406091
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8882978723404256
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.8891655873309381
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.9598425196850394
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 20.36484146118164
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.8675324675324675
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 20.96413230895996
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.8477157360406091
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8882978723404256
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.8891557631414319
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.9598425196850394
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 440.51263427734375
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.8675324675324675
+      name: Max F1
+    - type: max_f1_threshold
+      value: 466.3338623046875
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.8497409326424871
+      name: Max Precision
+    - type: max_recall
+      value: 0.8882978723404256
+      name: Max Recall
+    - type: max_ap
+      value: 0.9083604180944419
+      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). 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:** Unknown -->
+<!-- - **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 = [
+    '屋根裏部屋に猫のぬいぐるいみがあった',
+    'もしもし',
+    'なにが欲しい？',
+]
+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)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## 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              | 0.9598     |
+| cosine_accuracy_threshold    | 0.5491     |
+| cosine_f1                    | 0.8675     |
+| cosine_f1_threshold          | 0.5491     |
+| cosine_precision             | 0.8477     |
+| cosine_recall                | 0.8883     |
+| cosine_ap                    | 0.8893     |
+| dot_accuracy                 | 0.9583     |
+| dot_accuracy_threshold       | 288.5785   |
+| dot_f1                       | 0.8609     |
+| dot_f1_threshold             | 277.5243   |
+| dot_precision                | 0.8497     |
+| dot_recall                   | 0.8723     |
+| dot_ap                       | 0.9084     |
+| manhattan_accuracy           | 0.9598     |
+| manhattan_accuracy_threshold | 440.5126   |
+| manhattan_f1                 | 0.8675     |
+| manhattan_f1_threshold       | 466.3339   |
+| manhattan_precision          | 0.8477     |
+| manhattan_recall             | 0.8883     |
+| manhattan_ap                 | 0.8892     |
+| euclidean_accuracy           | 0.9598     |
+| euclidean_accuracy_threshold | 20.3648    |
+| euclidean_f1                 | 0.8675     |
+| euclidean_f1_threshold       | 20.9641    |
+| euclidean_precision          | 0.8477     |
+| euclidean_recall             | 0.8883     |
+| euclidean_ap                 | 0.8892     |
+| max_accuracy                 | 0.9598     |
+| max_accuracy_threshold       | 440.5126   |
+| max_f1                       | 0.8675     |
+| max_f1_threshold             | 466.3339   |
+| max_precision                | 0.8497     |
+| max_recall                   | 0.8883     |
+| **max_ap**                   | **0.9084** |
+
+<!--
+## 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.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 5,079 training samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                            | text2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | int                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.18 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.41 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~87.80%</li><li>1: ~12.20%</li></ul> |
+* Samples:
+  | text1                     | text2                    | label          |
+  |:--------------------------|:-------------------------|:---------------|
+  | <code>井戸へ訪れたことがある？</code> | <code>どんな食べ物が好き？</code>  | <code>0</code> |
+  | <code>猫が好きな人</code>       | <code>君は何でここにいるの？</code> | <code>0</code> |
+  | <code>町って？</code>         | <code>ぬいぐるみ</code>       | <code>0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,270 evaluation samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                            | text2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | int                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.14 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.53 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~85.30%</li><li>1: ~14.70%</li></ul> |
+* Samples:
+  | text1                          | text2               | label          |
+  |:-------------------------------|:--------------------|:---------------|
+  | <code>他には選べないの？</code>         | <code>だめじゃん</code>  | <code>0</code> |
+  | <code>村長は誰？</code>             | <code>屋根裏って？</code> | <code>0</code> |
+  | <code>誰かが呪文で花をぬいぐるみに変えた</code> | <code>両方はだめ？</code> | <code>0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.4
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.4
+- `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 |
+|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
+| 1.0   | 635  | 0.0749        | 0.0361 | 0.9084                              |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.1.1
+- 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
+```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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[PAD]": 32000
+}

checkpoint-635/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

checkpoint-635/README.md

@@ -0,0 +1,563 @@
+---
+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:5079
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: イリュージョンを使うの？
+  sentences:
+  - なんて言った？
+  - あなた
+  - 他にはある？
+- source_sentence: あなたは呪文が使えますか？
+  sentences:
+  - どっちを選ぶ？
+  - 屋根裏部屋の猫のぬいぐるみ
+  - 誰？
+- source_sentence: リリアンってものの形を変えられる？
+  sentences:
+  - どっちがいいと思う？
+  - 中に何か大事な物がある。
+  - 自分は今どこにいる？
+- source_sentence: どんな魔法なの？
+  sentences:
+  - だめじゃん
+  - こんばんは
+  - 井戸へ行った？
+- source_sentence: 屋根裏部屋に猫のぬいぐるいみがあった
+  sentences:
+  - なにが欲しい？
+  - もしもし
+  - センパーイ
+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: 0.9598425196850394
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.5491387844085693
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.8675324675324675
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.5491387844085693
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.8477157360406091
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8882978723404256
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.8892976796593005
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.9582677165354331
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 288.5785217285156
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.8608923884514437
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 277.5242919921875
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.8497409326424871
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.8723404255319149
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.9083604180944419
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.9598425196850394
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 440.51263427734375
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.8675324675324675
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 466.3338623046875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.8477157360406091
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8882978723404256
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.8891655873309381
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.9598425196850394
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 20.36484146118164
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.8675324675324675
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 20.96413230895996
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.8477157360406091
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8882978723404256
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.8891557631414319
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.9598425196850394
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 440.51263427734375
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.8675324675324675
+      name: Max F1
+    - type: max_f1_threshold
+      value: 466.3338623046875
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.8497409326424871
+      name: Max Precision
+    - type: max_recall
+      value: 0.8882978723404256
+      name: Max Recall
+    - type: max_ap
+      value: 0.9083604180944419
+      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). 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:** Unknown -->
+<!-- - **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 = [
+    '屋根裏部屋に猫のぬいぐるいみがあった',
+    'もしもし',
+    'なにが欲しい？',
+]
+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)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## 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              | 0.9598     |
+| cosine_accuracy_threshold    | 0.5491     |
+| cosine_f1                    | 0.8675     |
+| cosine_f1_threshold          | 0.5491     |
+| cosine_precision             | 0.8477     |
+| cosine_recall                | 0.8883     |
+| cosine_ap                    | 0.8893     |
+| dot_accuracy                 | 0.9583     |
+| dot_accuracy_threshold       | 288.5785   |
+| dot_f1                       | 0.8609     |
+| dot_f1_threshold             | 277.5243   |
+| dot_precision                | 0.8497     |
+| dot_recall                   | 0.8723     |
+| dot_ap                       | 0.9084     |
+| manhattan_accuracy           | 0.9598     |
+| manhattan_accuracy_threshold | 440.5126   |
+| manhattan_f1                 | 0.8675     |
+| manhattan_f1_threshold       | 466.3339   |
+| manhattan_precision          | 0.8477     |
+| manhattan_recall             | 0.8883     |
+| manhattan_ap                 | 0.8892     |
+| euclidean_accuracy           | 0.9598     |
+| euclidean_accuracy_threshold | 20.3648    |
+| euclidean_f1                 | 0.8675     |
+| euclidean_f1_threshold       | 20.9641    |
+| euclidean_precision          | 0.8477     |
+| euclidean_recall             | 0.8883     |
+| euclidean_ap                 | 0.8892     |
+| max_accuracy                 | 0.9598     |
+| max_accuracy_threshold       | 440.5126   |
+| max_f1                       | 0.8675     |
+| max_f1_threshold             | 466.3339   |
+| max_precision                | 0.8497     |
+| max_recall                   | 0.8883     |
+| **max_ap**                   | **0.9084** |
+
+<!--
+## 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.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 5,079 training samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                            | text2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | int                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.18 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.41 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~87.80%</li><li>1: ~12.20%</li></ul> |
+* Samples:
+  | text1                     | text2                    | label          |
+  |:--------------------------|:-------------------------|:---------------|
+  | <code>井戸へ訪れたことがある？</code> | <code>どんな食べ物が好き？</code>  | <code>0</code> |
+  | <code>猫が好きな人</code>       | <code>君は何でここにいるの？</code> | <code>0</code> |
+  | <code>町って？</code>         | <code>ぬいぐるみ</code>       | <code>0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,270 evaluation samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                            | text2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | int                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.14 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.53 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~85.30%</li><li>1: ~14.70%</li></ul> |
+* Samples:
+  | text1                          | text2               | label          |
+  |:-------------------------------|:--------------------|:---------------|
+  | <code>他には選べないの？</code>         | <code>だめじゃん</code>  | <code>0</code> |
+  | <code>村長は誰？</code>             | <code>屋根裏って？</code> | <code>0</code> |
+  | <code>誰かが呪文で花をぬいぐるみに変えた</code> | <code>両方はだめ？</code> | <code>0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.4
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.4
+- `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 |
+|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
+| 1.0   | 635  | 0.0749        | 0.0361 | 0.9084                              |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.1.1
+- 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
+```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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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