Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "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
+}

2_Dense/config.json

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+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5929d74a21edc1c70e4f5319e230d9aa63eaca0be65cf4c7ee49c7092c0b9f51
+size 2362528

README.md

@@ -0,0 +1,1129 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1000000
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/LaBSE
+widget:
+- source_sentence: Акӑ ӗнтӗ Чакак кимӗ ҫине сикрӗ, Коля пӗр-икӗ хут шнуртан туртрӗ
+    те, мотор кӗрлесе те кайрӗ, унтан кимӗ утрав еннелле вӗҫтерчӗ.
+  sentences:
+  - Вот Сорока вскочил в лодку, Коля дернул за шнур, раз, другой, мотор затрещал,
+    и лодка понеслась к острову.
+  - Победа римского флота в гавани Эвносте.
+  - Повесть Бориса Горбатова о подвиге и героизме советских людей во время Великой
+    Отечественной войны.
+- source_sentence: Ун патне пысӑках мар хырӑмлӑ, шурӑ сӑнлӑ, хӗрлӗ питлӗ, лутра ҫын
+    килсе кӗчӗ.
+  sentences:
+  - Антонов, Семён Михеевич
+  - Явился низенький человек, с умеренным брюшком, с белым лицом, румяными щеками
+  - Чёрно-белые фильмы СССР
+- source_sentence: '3. Анчах Гаваон ҫыннисем, Иисус Иерихонпа Гай хулисене епле пӗтерсе
+    тӑкни ҫинчен илтсессӗн, 4. акӑ мӗнле чеелӗх тупнӑ: ашакӗсем ҫине ҫул валли кивӗ
+    михӗсемпе ҫӑкӑр янтӑласа хунӑ, ҫӗтӗлсе пӗтнӗ, саплӑклӑ тир хутаҫпа эрех илнӗ;
+    5. ури сырри те вӗсен кивӗ, саплӑклӑ пулнӑ, ҫийӗнчи тумтирӗсем те ҫӗтӗк пулнӑ;
+    ҫул ҫине илнӗ ҫӑкӑрӗ те пӗтӗмпех типсе-кӑвакарса кайнӑскер, [тӗпренсе] пӗтнӗскер
+    пулнӑ.'
+  sentences:
+  - '3. Но жители Гаваона, услышав, что Иисус сделал с Иерихоном и Гаем, 4. употребили
+    хитрость: пошли, запаслись хлебом на дорогу и положили ветхие мешки на ослов своих
+    и ветхие, изорванные и заплатанные мехи вина; 5. и обувь на ногах их была ветхая
+    с заплатами, и одежда на них ветхая; и весь дорожный хлеб их был сухой и заплесневелый
+    [и раскрошенный].'
+  - «Черти бы их дули!..» — в отчаянии вскричал Щукарь и кинулся к цыганскому табору,
+    но, выскочив на пригорок, обнаружил, что ни шатров, ни кибиток возле речки уже
+    нет.
+  - 9. И сделаю над тобою то, чего Я никогда не делал и чему подобного впредь не буду
+    делать, за все твои мерзости.
+- source_sentence: Эпӗ кӗпер айӗпе укҫасӑрах, ахалех вӗҫсе тухрӑм.
+  sentences:
+  - У меня в экипаже был механик — что называется, «палец в рот не клади».
+  - А я под мост даром слетал.
+  - Я пользовался этим и прогуливал школу, чтобы проводить время в компании более
+    старших ребят.
+- source_sentence: Генри Джастис Форд
+  sentences:
+  - — Вижу, по одному делу? — спросила она, взглянув на Сашу и его приятелей.
+  - Я вышел из ванны свеж и бодр, как будто собирался на бал.
+  - Форд, Генри Джастис
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b -->
+- **Maximum Sequence Length:** 256 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': 256, '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})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): Normalize()
+)
+```
+
+## 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 = [
+    'Генри Джастис Форд',
+    'Форд, Генри Джастис',
+    'Я вышел из ванны свеж и бодр, как будто собирался на бал.',
+]
+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: 1,000,000 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: 3 tokens</li><li>mean: 21.82 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 21.16 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                         | sentence_1                                                    | label            |
+  |:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|:-----------------|
+  | <code>Темех мар.</code>                                                            | <code>Дело десятое.</code>                                    | <code>1.0</code> |
+  | <code>Уругвайӑн тĕн ĕҫченĕсем</code>                                               | <code>Религиозные деятели Уругвая</code>                      | <code>1.0</code> |
+  | <code>Эп аванах ас тӑватӑп, пилӗк ҫул каялла пахчана эпир лайӑх тасатнӑччӗ.</code> | <code>А пять лет тому назад я знал, что сад был чищен.</code> | <code>1.0</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 12
+- `num_train_epochs`: 1
+- `fp16`: True
+- `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`: 12
+- `per_device_eval_batch_size`: 12
+- `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`: 1
+- `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`: 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`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0012 | 100   | -             |
+| 0.0024 | 200   | -             |
+| 0.0036 | 300   | -             |
+| 0.0048 | 400   | -             |
+| 0.0060 | 500   | 0.5331        |
+| 0.0072 | 600   | -             |
+| 0.0084 | 700   | -             |
+| 0.0096 | 800   | -             |
+| 0.0108 | 900   | -             |
+| 0.0120 | 1000  | 0.3694        |
+| 0.0132 | 1100  | -             |
+| 0.0144 | 1200  | -             |
+| 0.0156 | 1300  | -             |
+| 0.0168 | 1400  | -             |
+| 0.0180 | 1500  | 0.3141        |
+| 0.0192 | 1600  | -             |
+| 0.0204 | 1700  | -             |
+| 0.0216 | 1800  | -             |
+| 0.0228 | 1900  | -             |
+| 0.0240 | 2000  | 0.2836        |
+| 0.0252 | 2100  | -             |
+| 0.0264 | 2200  | -             |
+| 0.0276 | 2300  | -             |
+| 0.0288 | 2400  | -             |
+| 0.0300 | 2500  | 0.2823        |
+| 0.0312 | 2600  | -             |
+| 0.0324 | 2700  | -             |
+| 0.0336 | 2800  | -             |
+| 0.0348 | 2900  | -             |
+| 0.0360 | 3000  | 0.265         |
+| 0.0372 | 3100  | -             |
+| 0.0384 | 3200  | -             |
+| 0.0396 | 3300  | -             |
+| 0.0408 | 3400  | -             |
+| 0.0420 | 3500  | 0.2599        |
+| 0.0432 | 3600  | -             |
+| 0.0444 | 3700  | -             |
+| 0.0456 | 3800  | -             |
+| 0.0468 | 3900  | -             |
+| 0.0480 | 4000  | 0.234         |
+| 0.0492 | 4100  | -             |
+| 0.0504 | 4200  | -             |
+| 0.0516 | 4300  | -             |
+| 0.0528 | 4400  | -             |
+| 0.0540 | 4500  | 0.1966        |
+| 0.0552 | 4600  | -             |
+| 0.0564 | 4700  | -             |
+| 0.0576 | 4800  | -             |
+| 0.0588 | 4900  | -             |
+| 0.0600 | 5000  | 0.2204        |
+| 0.0612 | 5100  | -             |
+| 0.0624 | 5200  | -             |
+| 0.0636 | 5300  | -             |
+| 0.0648 | 5400  | -             |
+| 0.0660 | 5500  | 0.2272        |
+| 0.0672 | 5600  | -             |
+| 0.0684 | 5700  | -             |
+| 0.0696 | 5800  | -             |
+| 0.0708 | 5900  | -             |
+| 0.0720 | 6000  | 0.2256        |
+| 0.0732 | 6100  | -             |
+| 0.0744 | 6200  | -             |
+| 0.0756 | 6300  | -             |
+| 0.0768 | 6400  | -             |
+| 0.0780 | 6500  | 0.2071        |
+| 0.0792 | 6600  | -             |
+| 0.0804 | 6700  | -             |
+| 0.0816 | 6800  | -             |
+| 0.0828 | 6900  | -             |
+| 0.0840 | 7000  | 0.2113        |
+| 0.0852 | 7100  | -             |
+| 0.0864 | 7200  | -             |
+| 0.0876 | 7300  | -             |
+| 0.0888 | 7400  | -             |
+| 0.0900 | 7500  | 0.2222        |
+| 0.0912 | 7600  | -             |
+| 0.0924 | 7700  | -             |
+| 0.0936 | 7800  | -             |
+| 0.0948 | 7900  | -             |
+| 0.0960 | 8000  | 0.2186        |
+| 0.0972 | 8100  | -             |
+| 0.0984 | 8200  | -             |
+| 0.0996 | 8300  | -             |
+| 0.1008 | 8400  | -             |
+| 0.1020 | 8500  | 0.2137        |
+| 0.1032 | 8600  | -             |
+| 0.1044 | 8700  | -             |
+| 0.1056 | 8800  | -             |
+| 0.1068 | 8900  | -             |
+| 0.1080 | 9000  | 0.1928        |
+| 0.1092 | 9100  | -             |
+| 0.1104 | 9200  | -             |
+| 0.1116 | 9300  | -             |
+| 0.1128 | 9400  | -             |
+| 0.1140 | 9500  | 0.2117        |
+| 0.1152 | 9600  | -             |
+| 0.1164 | 9700  | -             |
+| 0.1176 | 9800  | -             |
+| 0.1188 | 9900  | -             |
+| 0.1200 | 10000 | 0.1987        |
+| 0.1212 | 10100 | -             |
+| 0.1224 | 10200 | -             |
+| 0.1236 | 10300 | -             |
+| 0.1248 | 10400 | -             |
+| 0.1260 | 10500 | 0.2011        |
+| 0.1272 | 10600 | -             |
+| 0.1284 | 10700 | -             |
+| 0.1296 | 10800 | -             |
+| 0.1308 | 10900 | -             |
+| 0.1320 | 11000 | 0.1775        |
+| 0.1332 | 11100 | -             |
+| 0.1344 | 11200 | -             |
+| 0.1356 | 11300 | -             |
+| 0.1368 | 11400 | -             |
+| 0.1380 | 11500 | 0.2048        |
+| 0.1392 | 11600 | -             |
+| 0.1404 | 11700 | -             |
+| 0.1416 | 11800 | -             |
+| 0.1428 | 11900 | -             |
+| 0.1440 | 12000 | 0.2064        |
+| 0.1452 | 12100 | -             |
+| 0.1464 | 12200 | -             |
+| 0.1476 | 12300 | -             |
+| 0.1488 | 12400 | -             |
+| 0.1500 | 12500 | 0.1883        |
+| 0.1512 | 12600 | -             |
+| 0.1524 | 12700 | -             |
+| 0.1536 | 12800 | -             |
+| 0.1548 | 12900 | -             |
+| 0.1560 | 13000 | 0.2084        |
+| 0.1572 | 13100 | -             |
+| 0.1584 | 13200 | -             |
+| 0.1596 | 13300 | -             |
+| 0.1608 | 13400 | -             |
+| 0.1620 | 13500 | 0.2077        |
+| 0.1632 | 13600 | -             |
+| 0.1644 | 13700 | -             |
+| 0.1656 | 13800 | -             |
+| 0.1668 | 13900 | -             |
+| 0.1680 | 14000 | 0.1866        |
+| 0.1692 | 14100 | -             |
+| 0.1704 | 14200 | -             |
+| 0.1716 | 14300 | -             |
+| 0.1728 | 14400 | -             |
+| 0.1740 | 14500 | 0.1859        |
+| 0.1752 | 14600 | -             |
+| 0.1764 | 14700 | -             |
+| 0.1776 | 14800 | -             |
+| 0.1788 | 14900 | -             |
+| 0.1800 | 15000 | 0.1735        |
+| 0.1812 | 15100 | -             |
+| 0.1824 | 15200 | -             |
+| 0.1836 | 15300 | -             |
+| 0.1848 | 15400 | -             |
+| 0.1860 | 15500 | 0.171         |
+| 0.1872 | 15600 | -             |
+| 0.1884 | 15700 | -             |
+| 0.1896 | 15800 | -             |
+| 0.1908 | 15900 | -             |
+| 0.1920 | 16000 | 0.1465        |
+| 0.1932 | 16100 | -             |
+| 0.1944 | 16200 | -             |
+| 0.1956 | 16300 | -             |
+| 0.1968 | 16400 | -             |
+| 0.1980 | 16500 | 0.1921        |
+| 0.1992 | 16600 | -             |
+| 0.2004 | 16700 | -             |
+| 0.2016 | 16800 | -             |
+| 0.2028 | 16900 | -             |
+| 0.2040 | 17000 | 0.1669        |
+| 0.2052 | 17100 | -             |
+| 0.2064 | 17200 | -             |
+| 0.2076 | 17300 | -             |
+| 0.2088 | 17400 | -             |
+| 0.2100 | 17500 | 0.1656        |
+| 0.2112 | 17600 | -             |
+| 0.2124 | 17700 | -             |
+| 0.2136 | 17800 | -             |
+| 0.2148 | 17900 | -             |
+| 0.2160 | 18000 | 0.1952        |
+| 0.2172 | 18100 | -             |
+| 0.2184 | 18200 | -             |
+| 0.2196 | 18300 | -             |
+| 0.2208 | 18400 | -             |
+| 0.2220 | 18500 | 0.1658        |
+| 0.2232 | 18600 | -             |
+| 0.2244 | 18700 | -             |
+| 0.2256 | 18800 | -             |
+| 0.2268 | 18900 | -             |
+| 0.2280 | 19000 | 0.1774        |
+| 0.2292 | 19100 | -             |
+| 0.2304 | 19200 | -             |
+| 0.2316 | 19300 | -             |
+| 0.2328 | 19400 | -             |
+| 0.2340 | 19500 | 0.1802        |
+| 0.2352 | 19600 | -             |
+| 0.2364 | 19700 | -             |
+| 0.2376 | 19800 | -             |
+| 0.2388 | 19900 | -             |
+| 0.2400 | 20000 | 0.1724        |
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+| 0.2424 | 20200 | -             |
+| 0.2436 | 20300 | -             |
+| 0.2448 | 20400 | -             |
+| 0.2460 | 20500 | 0.1653        |
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+| 0.2484 | 20700 | -             |
+| 0.2496 | 20800 | -             |
+| 0.2508 | 20900 | -             |
+| 0.2520 | 21000 | 0.1484        |
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+| 0.2544 | 21200 | -             |
+| 0.2556 | 21300 | -             |
+| 0.2568 | 21400 | -             |
+| 0.2580 | 21500 | 0.1544        |
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+| 0.2604 | 21700 | -             |
+| 0.2616 | 21800 | -             |
+| 0.2628 | 21900 | -             |
+| 0.2640 | 22000 | 0.174         |
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+| 0.2676 | 22300 | -             |
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+| 0.2700 | 22500 | 0.1488        |
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+| 0.2736 | 22800 | -             |
+| 0.2748 | 22900 | -             |
+| 0.2760 | 23000 | 0.1696        |
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+| 0.2796 | 23300 | -             |
+| 0.2808 | 23400 | -             |
+| 0.2820 | 23500 | 0.1468        |
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+| 0.2856 | 23800 | -             |
+| 0.2868 | 23900 | -             |
+| 0.2880 | 24000 | 0.1738        |
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+| 0.2904 | 24200 | -             |
+| 0.2916 | 24300 | -             |
+| 0.2928 | 24400 | -             |
+| 0.2940 | 24500 | 0.1667        |
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+| 0.2964 | 24700 | -             |
+| 0.2976 | 24800 | -             |
+| 0.2988 | 24900 | -             |
+| 0.3000 | 25000 | 0.1562        |
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+| 0.3036 | 25300 | -             |
+| 0.3048 | 25400 | -             |
+| 0.3060 | 25500 | 0.1628        |
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+| 0.3084 | 25700 | -             |
+| 0.3096 | 25800 | -             |
+| 0.3108 | 25900 | -             |
+| 0.3120 | 26000 | 0.1392        |
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+| 0.3144 | 26200 | -             |
+| 0.3156 | 26300 | -             |
+| 0.3168 | 26400 | -             |
+| 0.3180 | 26500 | 0.1507        |
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+| 0.3204 | 26700 | -             |
+| 0.3216 | 26800 | -             |
+| 0.3228 | 26900 | -             |
+| 0.3240 | 27000 | 0.1646        |
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+| 0.3264 | 27200 | -             |
+| 0.3276 | 27300 | -             |
+| 0.3288 | 27400 | -             |
+| 0.3300 | 27500 | 0.1433        |
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+| 0.3324 | 27700 | -             |
+| 0.3336 | 27800 | -             |
+| 0.3348 | 27900 | -             |
+| 0.3360 | 28000 | 0.1689        |
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+| 0.3384 | 28200 | -             |
+| 0.3396 | 28300 | -             |
+| 0.3408 | 28400 | -             |
+| 0.3420 | 28500 | 0.1432        |
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+| 0.3480 | 29000 | 0.1534        |
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+| 0.3516 | 29300 | -             |
+| 0.3528 | 29400 | -             |
+| 0.3540 | 29500 | 0.1487        |
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+| 0.3564 | 29700 | -             |
+| 0.3576 | 29800 | -             |
+| 0.3588 | 29900 | -             |
+| 0.3600 | 30000 | 0.1439        |
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+| 0.3624 | 30200 | -             |
+| 0.3636 | 30300 | -             |
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+| 0.3660 | 30500 | 0.1397        |
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+| 0.3684 | 30700 | -             |
+| 0.3696 | 30800 | -             |
+| 0.3708 | 30900 | -             |
+| 0.3720 | 31000 | 0.1542        |
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+| 0.3756 | 31300 | -             |
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+| 0.3780 | 31500 | 0.1448        |
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+| 0.3804 | 31700 | -             |
+| 0.3816 | 31800 | -             |
+| 0.3828 | 31900 | -             |
+| 0.3840 | 32000 | 0.1608        |
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+| 0.3876 | 32300 | -             |
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+| 0.3900 | 32500 | 0.1486        |
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+| 0.3936 | 32800 | -             |
+| 0.3948 | 32900 | -             |
+| 0.3960 | 33000 | 0.1274        |
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+| 0.3984 | 33200 | -             |
+| 0.3996 | 33300 | -             |
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+| 0.4020 | 33500 | 0.1451        |
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+| 0.4080 | 34000 | 0.1316        |
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+| 0.4104 | 34200 | -             |
+| 0.4116 | 34300 | -             |
+| 0.4128 | 34400 | -             |
+| 0.4140 | 34500 | 0.1306        |
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+| 0.4176 | 34800 | -             |
+| 0.4188 | 34900 | -             |
+| 0.4200 | 35000 | 0.1382        |
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+| 0.4236 | 35300 | -             |
+| 0.4248 | 35400 | -             |
+| 0.4260 | 35500 | 0.1322        |
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+| 0.4284 | 35700 | -             |
+| 0.4296 | 35800 | -             |
+| 0.4308 | 35900 | -             |
+| 0.4320 | 36000 | 0.1617        |
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+| 0.4344 | 36200 | -             |
+| 0.4356 | 36300 | -             |
+| 0.4368 | 36400 | -             |
+| 0.4380 | 36500 | 0.14          |
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+| 0.4416 | 36800 | -             |
+| 0.4428 | 36900 | -             |
+| 0.4440 | 37000 | 0.1321        |
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+| 0.4464 | 37200 | -             |
+| 0.4476 | 37300 | -             |
+| 0.4488 | 37400 | -             |
+| 0.4500 | 37500 | 0.1464        |
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+| 0.4536 | 37800 | -             |
+| 0.4548 | 37900 | -             |
+| 0.4560 | 38000 | 0.1236        |
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+| 0.4584 | 38200 | -             |
+| 0.4596 | 38300 | -             |
+| 0.4608 | 38400 | -             |
+| 0.4620 | 38500 | 0.147         |
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+| 0.4656 | 38800 | -             |
+| 0.4668 | 38900 | -             |
+| 0.4680 | 39000 | 0.1376        |
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+| 0.4740 | 39500 | 0.1342        |
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+| 0.4788 | 39900 | -             |
+| 0.4800 | 40000 | 0.123         |
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+| 0.4860 | 40500 | 0.1312        |
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+| 0.4896 | 40800 | -             |
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+| 0.4920 | 41000 | 0.1325        |
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+| 0.4980 | 41500 | 0.1203        |
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+| 0.5016 | 41800 | -             |
+| 0.5028 | 41900 | -             |
+| 0.5040 | 42000 | 0.1258        |
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+| 0.5076 | 42300 | -             |
+| 0.5088 | 42400 | -             |
+| 0.5100 | 42500 | 0.141         |
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+| 0.5148 | 42900 | -             |
+| 0.5160 | 43000 | 0.1473        |
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+| 0.5196 | 43300 | -             |
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+| 0.5220 | 43500 | 0.1247        |
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+| 0.5256 | 43800 | -             |
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+| 0.5280 | 44000 | 0.1259        |
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+| 0.5304 | 44200 | -             |
+| 0.5316 | 44300 | -             |
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+| 0.5340 | 44500 | 0.1372        |
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+| 0.5376 | 44800 | -             |
+| 0.5388 | 44900 | -             |
+| 0.5400 | 45000 | 0.1413        |
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+| 0.5436 | 45300 | -             |
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+| 0.5460 | 45500 | 0.1157        |
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+| 0.5496 | 45800 | -             |
+| 0.5508 | 45900 | -             |
+| 0.5520 | 46000 | 0.127         |
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+| 0.5544 | 46200 | -             |
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+| 0.5580 | 46500 | 0.1202        |
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+| 0.5616 | 46800 | -             |
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+| 0.5640 | 47000 | 0.1199        |
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+| 0.5664 | 47200 | -             |
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+| 0.5688 | 47400 | -             |
+| 0.5700 | 47500 | 0.1309        |
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+| 0.5760 | 48000 | 0.1276        |
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+| 0.5820 | 48500 | 0.1278        |
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+| 0.5880 | 49000 | 0.1175        |
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+| 0.5904 | 49200 | -             |
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+| 0.6000 | 50000 | 0.1109        |
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+| 0.6108 | 50900 | -             |
+| 0.6120 | 51000 | 0.1296        |
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+| 0.6204 | 51700 | -             |
+| 0.6216 | 51800 | -             |
+| 0.6228 | 51900 | -             |
+| 0.6240 | 52000 | 0.1155        |
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+| 0.6300 | 52500 | 0.1245        |
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+| 0.6348 | 52900 | -             |
+| 0.6360 | 53000 | 0.1238        |
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+| 0.6420 | 53500 | 0.12          |
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+| 0.6480 | 54000 | 0.1116        |
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+| 0.6540 | 54500 | 0.1305        |
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+| 0.6600 | 55000 | 0.1355        |
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+| 0.6660 | 55500 | 0.1139        |
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+| 0.6816 | 56800 | -             |
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+| 0.6900 | 57500 | 0.1071        |
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+| 0.6948 | 57900 | -             |
+| 0.6960 | 58000 | 0.112         |
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+| 0.6984 | 58200 | -             |
+| 0.6996 | 58300 | -             |
+| 0.7008 | 58400 | -             |
+| 0.7020 | 58500 | 0.1038        |
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+| 0.7044 | 58700 | -             |
+| 0.7056 | 58800 | -             |
+| 0.7068 | 58900 | -             |
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+| 0.7116 | 59300 | -             |
+| 0.7128 | 59400 | -             |
+| 0.7140 | 59500 | 0.1001        |
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+| 0.7176 | 59800 | -             |
+| 0.7188 | 59900 | -             |
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+| 0.7224 | 60200 | -             |
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+| 0.7308 | 60900 | -             |
+| 0.7320 | 61000 | 0.1117        |
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+| 0.7344 | 61200 | -             |
+| 0.7356 | 61300 | -             |
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+| 0.7416 | 61800 | -             |
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+| 0.7440 | 62000 | 0.0972        |
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+| 0.7488 | 62400 | -             |
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+| 0.7536 | 62800 | -             |
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+| 0.8604 | 71700 | -             |
+| 0.8616 | 71800 | -             |
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+| 0.8736 | 72800 | -             |
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+| 0.8796 | 73300 | -             |
+| 0.8808 | 73400 | -             |
+| 0.8820 | 73500 | 0.1138        |
+| 0.8832 | 73600 | -             |
+| 0.8844 | 73700 | -             |
+| 0.8856 | 73800 | -             |
+| 0.8868 | 73900 | -             |
+| 0.8880 | 74000 | 0.1071        |
+| 0.8892 | 74100 | -             |
+| 0.8904 | 74200 | -             |
+| 0.8916 | 74300 | -             |
+| 0.8928 | 74400 | -             |
+| 0.8940 | 74500 | 0.1091        |
+| 0.8952 | 74600 | -             |
+| 0.8964 | 74700 | -             |
+| 0.8976 | 74800 | -             |
+| 0.8988 | 74900 | -             |
+| 0.9000 | 75000 | 0.1143        |
+| 0.9012 | 75100 | -             |
+| 0.9024 | 75200 | -             |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.10
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 3.6.0
+- 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "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,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

config_sentence_transformers.json

@@ -0,0 +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"
+}

model.safetensors

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+oid sha256:215a4e2edc994b0f8f664933498def03d92e377f6f4468791a7f18e951cc1fb1
+size 1883730160

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
+size 13632172

tokenizer_config.json

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+      "normalized": false,
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+  "do_basic_tokenize": true,
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+  "extra_special_tokens": {},
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}