Add new SentenceTransformer model with an openvino backend

1_Pooling/config.json

@@ -1,10 +1,10 @@
-{
-  "word_embedding_dimension": 768,
-  "pooling_mode_cls_token": true,
-  "pooling_mode_mean_tokens": false,
-  "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false,
-  "pooling_mode_weightedmean_tokens": false,
-  "pooling_mode_lasttoken": false,
-  "include_prompt": true
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

@@ -1,417 +1,417 @@
----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:182343
-- loss:CategoricalContrastiveLoss
-widget:
-- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
-  sentences:
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S18粗骨材20。備考：刊-ｺﾝ 2118EXP.J面ｺﾝ。
-  - 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。
-  - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
-- source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。
-  sentences:
-  - 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表    0059。
-  - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0040。
-  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝18N/mm2   Ｓ＝１８ 徳島１。備考：B1-111111 H2906BD     個別嵩上げｺﾝｸﾘｰﾄ。
-- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
-  sentences:
-  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場地上部ｺﾝ（5F）。
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC48 ﾌﾛｰ60粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 4860K免震装置下部ｺﾝ。
-  - 科目：コンクリート。名称：防水押えｺﾝｸﾘｰﾄ。
-- source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
-  sentences:
-  - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S18粗骨材地上部。備考：代価表    0060。
-  - 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表    0059。
-- source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
-  sentences:
-  - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
-  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112 H22.11     協議防水保護ｺﾝｸﾘｰﾄ。
-  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
----
-
-# SentenceTransformer
-
-This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 768 dimensions
-- **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': True, 'pooling_mode_mean_tokens': False, '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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8")
-# Run inference
-sentences = [
-    '科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。',
-    '科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。',
-    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112 H22.11     協議防水保護ｺﾝｸﾘｰﾄ。',
-]
-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.*
--->
-
-<!--
-## 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: 182,343 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                          | sentence2                                                                         | label                                                             |
-  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                            | int                                                               |
-  | details | <ul><li>min: 11 tokens</li><li>mean: 13.32 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.8 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~68.50%</li><li>1: ~4.50%</li><li>2: ~27.00%</li></ul> |
-* Samples:
-  | sentence1                                | sentence2                                                                                        | label          |
-  |:-----------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
-  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code>                                                               | <code>1</code> |
-  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場免震層下部ｺﾝ。</code> | <code>2</code> |
-  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場基礎部ﾏｽｺﾝ。</code> | <code>2</code> |
-* Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `learning_rate`: 1e-05
-- `weight_decay`: 0.01
-- `num_train_epochs`: 10
-- `warmup_ratio`: 0.2
-- `fp16`: True
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `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`: 1e-05
-- `weight_decay`: 0.01
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 10
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.2
-- `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}
-- `tp_size`: 0
-- `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`: None
-- `hub_always_push`: False
-- `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
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss |
-|:------:|:----:|:-------------:|
-| 0.0701 | 50   | 0.2825        |
-| 0.1403 | 100  | 0.1467        |
-| 0.2104 | 150  | 0.0947        |
-| 0.2805 | 200  | 0.0839        |
-| 0.3506 | 250  | 0.0769        |
-| 0.4208 | 300  | 0.0684        |
-| 0.4909 | 350  | 0.0625        |
-| 0.5610 | 400  | 0.0582        |
-| 0.6311 | 450  | 0.0579        |
-| 0.7013 | 500  | 0.0514        |
-| 0.7714 | 550  | 0.0514        |
-| 0.8415 | 600  | 0.0448        |
-| 0.9116 | 650  | 0.0436        |
-| 0.9818 | 700  | 0.0422        |
-| 1.0519 | 750  | 0.0371        |
-| 1.1220 | 800  | 0.0377        |
-| 1.1921 | 850  | 0.0353        |
-| 1.2623 | 900  | 0.0354        |
-| 1.3324 | 950  | 0.0325        |
-| 1.4025 | 1000 | 0.0328        |
-| 1.4727 | 1050 | 0.0302        |
-| 1.5428 | 1100 | 0.0259        |
-| 1.6129 | 1150 | 0.0267        |
-| 1.6830 | 1200 | 0.0274        |
-| 1.7532 | 1250 | 0.0262        |
-| 1.8233 | 1300 | 0.0234        |
-| 1.8934 | 1350 | 0.0244        |
-| 1.9635 | 1400 | 0.0238        |
-| 2.0337 | 1450 | 0.02          |
-| 2.1038 | 1500 | 0.0187        |
-| 2.1739 | 1550 | 0.0185        |
-| 2.2440 | 1600 | 0.0178        |
-| 2.3142 | 1650 | 0.016         |
-| 2.3843 | 1700 | 0.0169        |
-| 2.4544 | 1750 | 0.0171        |
-| 2.5245 | 1800 | 0.0146        |
-| 2.5947 | 1850 | 0.0145        |
-| 2.6648 | 1900 | 0.0146        |
-| 2.7349 | 1950 | 0.0139        |
-| 2.8050 | 2000 | 0.0119        |
-| 2.8752 | 2050 | 0.0131        |
-| 2.9453 | 2100 | 0.0124        |
-| 3.0154 | 2150 | 0.011         |
-| 3.0856 | 2200 | 0.0109        |
-| 3.1557 | 2250 | 0.0103        |
-| 3.2258 | 2300 | 0.0102        |
-| 3.2959 | 2350 | 0.0089        |
-| 3.3661 | 2400 | 0.0083        |
-| 3.4362 | 2450 | 0.0095        |
-| 3.5063 | 2500 | 0.0085        |
-| 3.5764 | 2550 | 0.009         |
-| 3.6466 | 2600 | 0.0083        |
-| 3.7167 | 2650 | 0.0093        |
-| 3.7868 | 2700 | 0.0084        |
-| 3.8569 | 2750 | 0.0084        |
-| 3.9271 | 2800 | 0.0088        |
-| 3.9972 | 2850 | 0.0086        |
-| 4.0673 | 2900 | 0.0057        |
-| 4.1374 | 2950 | 0.0078        |
-| 4.2076 | 3000 | 0.0062        |
-| 4.2777 | 3050 | 0.0066        |
-| 4.3478 | 3100 | 0.006         |
-| 4.4180 | 3150 | 0.0078        |
-| 4.4881 | 3200 | 0.0056        |
-| 4.5582 | 3250 | 0.0064        |
-| 4.6283 | 3300 | 0.0063        |
-| 4.6985 | 3350 | 0.0058        |
-| 4.7686 | 3400 | 0.005         |
-| 4.8387 | 3450 | 0.0057        |
-| 4.9088 | 3500 | 0.0059        |
-| 4.9790 | 3550 | 0.0063        |
-| 5.0491 | 3600 | 0.0046        |
-| 5.1192 | 3650 | 0.0041        |
-| 5.1893 | 3700 | 0.005         |
-| 5.2595 | 3750 | 0.0043        |
-| 5.3296 | 3800 | 0.0046        |
-| 5.3997 | 3850 | 0.0041        |
-| 5.4698 | 3900 | 0.006         |
-| 5.5400 | 3950 | 0.0052        |
-| 5.6101 | 4000 | 0.0043        |
-
-
-### Framework Versions
-- Python: 3.11.12
-- Sentence Transformers: 4.1.0
-- Transformers: 4.51.3
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.6.0
-- Datasets: 2.14.4
-- Tokenizers: 0.21.1
-
-## 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.*
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:182343
+- loss:CategoricalContrastiveLoss
+widget:
+- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
+  sentences:
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S18粗骨材20。備考：刊-ｺﾝ 2118EXP.J面ｺﾝ。
+  - 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。
+  - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
+- source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。
+  sentences:
+  - 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表    0059。
+  - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表    0040。
+  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝18N/mm2   Ｓ＝１８ 徳島１。備考：B1-111111 H2906BD     個別嵩上げｺﾝｸﾘｰﾄ。
+- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
+  sentences:
+  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場地上部ｺﾝ（5F）。
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC48 ﾌﾛｰ60粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 4860K免震装置下部ｺﾝ。
+  - 科目：コンクリート。名称：防水押えｺﾝｸﾘｰﾄ。
+- source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
+  sentences:
+  - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S18粗骨材地上部。備考：代価表    0060。
+  - 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表    0059。
+- source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
+  sentences:
+  - 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
+  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112 H22.11     協議防水保護ｺﾝｸﾘｰﾄ。
+  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **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': True, 'pooling_mode_mean_tokens': False, '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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8")
+# Run inference
+sentences = [
+    '科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。',
+    '科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。',
+    '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308   FC=18       S15粗骨材20。備考：B0-114112 H22.11     協議防水保護ｺﾝｸﾘｰﾄ。',
+]
+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.*
+-->
+
+<!--
+## 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: 182,343 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                         | label                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | int                                                               |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 13.32 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.8 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~68.50%</li><li>1: ~4.50%</li><li>2: ~27.00%</li></ul> |
+* Samples:
+  | sentence1                                | sentence2                                                                                        | label          |
+  |:-----------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
+  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code>                                                               | <code>1</code> |
+  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場免震層下部ｺﾝ。</code> | <code>2</code> |
+  | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1        市場基礎部ﾏｽｺﾝ。</code> | <code>2</code> |
+* Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.2
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 1e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.2
+- `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}
+- `tp_size`: 0
+- `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`: None
+- `hub_always_push`: False
+- `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
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0701 | 50   | 0.2825        |
+| 0.1403 | 100  | 0.1467        |
+| 0.2104 | 150  | 0.0947        |
+| 0.2805 | 200  | 0.0839        |
+| 0.3506 | 250  | 0.0769        |
+| 0.4208 | 300  | 0.0684        |
+| 0.4909 | 350  | 0.0625        |
+| 0.5610 | 400  | 0.0582        |
+| 0.6311 | 450  | 0.0579        |
+| 0.7013 | 500  | 0.0514        |
+| 0.7714 | 550  | 0.0514        |
+| 0.8415 | 600  | 0.0448        |
+| 0.9116 | 650  | 0.0436        |
+| 0.9818 | 700  | 0.0422        |
+| 1.0519 | 750  | 0.0371        |
+| 1.1220 | 800  | 0.0377        |
+| 1.1921 | 850  | 0.0353        |
+| 1.2623 | 900  | 0.0354        |
+| 1.3324 | 950  | 0.0325        |
+| 1.4025 | 1000 | 0.0328        |
+| 1.4727 | 1050 | 0.0302        |
+| 1.5428 | 1100 | 0.0259        |
+| 1.6129 | 1150 | 0.0267        |
+| 1.6830 | 1200 | 0.0274        |
+| 1.7532 | 1250 | 0.0262        |
+| 1.8233 | 1300 | 0.0234        |
+| 1.8934 | 1350 | 0.0244        |
+| 1.9635 | 1400 | 0.0238        |
+| 2.0337 | 1450 | 0.02          |
+| 2.1038 | 1500 | 0.0187        |
+| 2.1739 | 1550 | 0.0185        |
+| 2.2440 | 1600 | 0.0178        |
+| 2.3142 | 1650 | 0.016         |
+| 2.3843 | 1700 | 0.0169        |
+| 2.4544 | 1750 | 0.0171        |
+| 2.5245 | 1800 | 0.0146        |
+| 2.5947 | 1850 | 0.0145        |
+| 2.6648 | 1900 | 0.0146        |
+| 2.7349 | 1950 | 0.0139        |
+| 2.8050 | 2000 | 0.0119        |
+| 2.8752 | 2050 | 0.0131        |
+| 2.9453 | 2100 | 0.0124        |
+| 3.0154 | 2150 | 0.011         |
+| 3.0856 | 2200 | 0.0109        |
+| 3.1557 | 2250 | 0.0103        |
+| 3.2258 | 2300 | 0.0102        |
+| 3.2959 | 2350 | 0.0089        |
+| 3.3661 | 2400 | 0.0083        |
+| 3.4362 | 2450 | 0.0095        |
+| 3.5063 | 2500 | 0.0085        |
+| 3.5764 | 2550 | 0.009         |
+| 3.6466 | 2600 | 0.0083        |
+| 3.7167 | 2650 | 0.0093        |
+| 3.7868 | 2700 | 0.0084        |
+| 3.8569 | 2750 | 0.0084        |
+| 3.9271 | 2800 | 0.0088        |
+| 3.9972 | 2850 | 0.0086        |
+| 4.0673 | 2900 | 0.0057        |
+| 4.1374 | 2950 | 0.0078        |
+| 4.2076 | 3000 | 0.0062        |
+| 4.2777 | 3050 | 0.0066        |
+| 4.3478 | 3100 | 0.006         |
+| 4.4180 | 3150 | 0.0078        |
+| 4.4881 | 3200 | 0.0056        |
+| 4.5582 | 3250 | 0.0064        |
+| 4.6283 | 3300 | 0.0063        |
+| 4.6985 | 3350 | 0.0058        |
+| 4.7686 | 3400 | 0.005         |
+| 4.8387 | 3450 | 0.0057        |
+| 4.9088 | 3500 | 0.0059        |
+| 4.9790 | 3550 | 0.0063        |
+| 5.0491 | 3600 | 0.0046        |
+| 5.1192 | 3650 | 0.0041        |
+| 5.1893 | 3700 | 0.005         |
+| 5.2595 | 3750 | 0.0043        |
+| 5.3296 | 3800 | 0.0046        |
+| 5.3997 | 3850 | 0.0041        |
+| 5.4698 | 3900 | 0.006         |
+| 5.5400 | 3950 | 0.0052        |
+| 5.6101 | 4000 | 0.0043        |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 2.14.4
+- Tokenizers: 0.21.1
+
+## 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.*
 -->

config.json

@@ -1,24 +1,25 @@
-{
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.51.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 32768
-}
+{
+  "_name_or_path": "Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -1,10 +1,10 @@
-{
-  "__version__": {
-    "sentence_transformers": "4.1.0",
-    "transformers": "4.51.3",
-    "pytorch": "2.6.0+cu124"
-  },
-  "prompts": {},
-  "default_prompt_name": null,
-  "similarity_fn_name": "cosine"
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
 }

modules.json

@@ -1,14 +1,14 @@
-[
-  {
-    "idx": 0,
-    "name": "0",
-    "path": "",
-    "type": "sentence_transformers.models.Transformer"
-  },
-  {
-    "idx": 1,
-    "name": "1",
-    "path": "1_Pooling",
-    "type": "sentence_transformers.models.Pooling"
-  }
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
 ]

openvino/openvino_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:714c47a7a0601faeed7d530dc7a3f3b6d027fe77b8c868a929d0620ce9e52268
+size 442470560

sentence_bert_config.json

@@ -1,4 +1,4 @@
-{
-  "max_seq_length": 512,
-  "do_lower_case": false
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
 }

special_tokens_map.json

@@ -1,37 +1,37 @@
-{
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -1,64 +1,64 @@
-{
-  "added_tokens_decoder": {
-    "0": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
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-  "do_subword_tokenize": true,
-  "do_word_tokenize": true,
-  "extra_special_tokens": {},
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-  "mask_token": "[MASK]",
-  "mecab_kwargs": {
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-  "sudachi_kwargs": null,
-  "tokenizer_class": "BertJapaneseTokenizer",
-  "unk_token": "[UNK]",
-  "word_tokenizer_type": "mecab"
-}
+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "do_word_tokenize": true,
+  "extra_special_tokens": {},
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+  "mask_token": "[MASK]",
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+  },
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "subword_tokenizer_type": "wordpiece",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "mecab"
+}