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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 256,
+    "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

@@ -1,66 +1,388 @@
 ---
-language: ja
-license: apache-2.0
 tags:
 - sentence-transformers
-- embeddings
-- japanese
-- semantic-search
-- highschool-project
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:5749
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: Nterprise Linux Services is expected to be available before then
+    end of this year.
+  sentences:
+  - Beta versions of Nterprise Linux Services are expected to be available on certain
+    HP ProLiant servers in July.
+  - Spain turning back the clock on siestas
+  - I don't like many flavored drinks.
+- source_sentence: Iran hopes nuclear talks will yield 'roadmap'
+  sentences:
+  - Iran Nuclear Talks in Geneva Spur High Hopes
+  - A black pet dog runs around in the garden of a house.
+  - The witness was a 27-year-old Kosovan parking attendant, who was paid by the News
+    of the World, the court heard.
+- source_sentence: Hamas Urges Hizbullah to Pull Fighters Out of Syria
+  sentences:
+  - '"This was a persistent problem which has not been solved, mechanically and physically,"
+    said board member Steven Wallace.'
+  - A small dog jumps over a yellow beam.
+  - Hamas calls on Hezbollah to pull forces out of Syria
+- source_sentence: Licensing revenue slid 21 percent, however, to $107.6 million.
+  sentences:
+  - Britain loses bid to deport radical cleric Abu Qatada
+  - A man sits on a bed very close to a small television.
+  - License sales, a key measure of demand, fell 21 percent to $107.6 million.
+- source_sentence: Comcast Class A shares were up 8 cents at $30.50 in morning trading
+    on the Nasdaq Stock Market.
+  sentences:
+  - The stock rose 48 cents to $30 yesterday in Nasdaq Stock Market trading.
+  - 'Malaysia: Chinese satellite found object in ocean'
+  - A boy in a robe sits in a chair.
+pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.4639747212598005
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4595105448711385
+      name: Spearman Cosine
 ---
 
-# MARK-Embedding
+# SentenceTransformer
 
-**MARK-Embedding** は、高校三年生によって開発された日本語文章向けの埋め込みモデルです。  
-SentenceTransformers 互換で、意味ベースの文章類似度計算や検索、クラスタリングなどに利用できます。
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
-## モデル概要
+## Model Details
 
-- **開発者**: 高校三年生 (2025)
-- **用途**: 日本語テキストの意味ベクトル化（埋め込み）
-- **アーキテクチャ**: [SentenceTransformers](https://www.sbert.net/) ベース
-- **想定タスク**
-  - 類似文章検索
-  - 重複検出
-  - 意味クラスタリング
-  - FAQ やチャットボット回答候補スコアリング
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 2048 tokens
+- **Output Dimensionality:** 256 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-## 使い方
+### Model Sources
 
-```python
-from sentence_transformers import SentenceTransformer, util
+- **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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
+  (1): Pooling({'word_embedding_dimension': 256, '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
 
-# モデルをロード
-model = SentenceTransformer("summerstars/MARK-Embedding")
+### 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
 
-# 文章をエンコード
-sentences = ["私はりんごが好きです", "私はバナナが好きです"]
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Comcast Class A shares were up 8 cents at $30.50 in morning trading on the Nasdaq Stock Market.',
+    'The stock rose 48 cents to $30 yesterday in Nasdaq Stock Market trading.',
+    'Malaysia: Chinese satellite found object in ocean',
+]
 embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 256]
 
-# 類似度を計算
-similarity = util.cos_sim(embeddings[0], embeddings[1])
-print("類似度:", similarity.item())
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.5752, 0.2980],
+#         [0.5752, 1.0000, 0.2161],
+#         [0.2980, 0.2161, 1.0000]])
 ```
 
-## 推奨用途
+<!--
+### 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
+
+#### Semantic Similarity
 
-- 日本語の類似度計算
-- 意味検索・レコメンデーション
-- クラスタリングやトピック分析
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.464      |
+| **spearman_cosine** | **0.4595** |
+
+<!--
+## 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: 5,749 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.73 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.55</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                  | sentence_1                                                                           | label                           |
+  |:----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Forecasters said warnings might go up for Cuba later Thursday.</code> | <code>Watches or warnings could be issued for eastern Cuba later on Thursday.</code> | <code>0.8</code>                |
+  | <code>Death toll in Lebanon bombings rises to 47</code>                     | <code>1 suspect arrested after Lebanon car bombings kill 45</code>                   | <code>0.5599999904632569</code> |
+  | <code>Three dogs running on a racetrack.</code>                             | <code>Three dogs round a bend at a racetrack.</code>                                 | <code>0.9600000381469727</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`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `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
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|
+| 1.0    | 360  | -             | 0.2967          |
+| 1.3889 | 500  | 0.11          | 0.3338          |
+| 2.0    | 720  | -             | 0.3665          |
+| 2.7778 | 1000 | 0.0857        | 0.4101          |
+| 3.0    | 1080 | -             | 0.4595          |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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",
+}
+```
 
-## 注意点
+<!--
+## Glossary
 
-- 本モデルは日本語テキスト向けに調整されていますが、英語など他言語では性能が保証されません。
-- 教育目的・研究目的での利用を推奨します。
-- 商用利用の場合はライセンス条件を確認してください。
+*Clearly define terms in order to be accessible across audiences.*
+-->
 
-## ライセンス
+<!--
+## Model Card Authors
 
-- 学習に使用したベースモデルのライセンスに準じます。
-- このリポジトリは [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) で公開しています。
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
 
-## 開発背景
+<!--
+## Model Card Contact
 
-このモデルは、高校三年生が自然言語処理と機械学習を学ぶ過程で作成したものです。  
-より多くの人が日本語の意味検索やAI開発を気軽に体験できるように公開しています。
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

calibration.json

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+{
+  "slope": 3.959556818008423,
+  "intercept": 0.20897746086120605
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/similarity_evaluation_results.csv

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+epoch,steps,cosine_pearson,cosine_spearman
+1.0,360,0.2903157409900097,0.2967070624119561
+2.0,720,0.36665449013888085,0.3665099682349319
+3.0,1080,0.4639747212598005,0.4595105448711385

model.safetensors

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 size 336250336

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 2048,
+    "do_lower_case": false
+}

tokenizer.json

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+size 33385262

tokenizer_config.json

@@ -51334,12 +51334,18 @@
     "image_token": "<image_soft_token>"
   },
   "image_token": "<image_soft_token>",
+  "max_length": 2048,
   "model_max_length": 2048,
+  "pad_to_multiple_of": null,
   "pad_token": "<pad>",
+  "pad_token_type_id": 0,
   "padding_side": "right",
   "sp_model_kwargs": null,
   "spaces_between_special_tokens": false,
+  "stride": 0,
   "tokenizer_class": "GemmaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "<unk>",
   "use_default_system_prompt": false
 }