Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+ "word_embedding_dimension": 1024,
+ "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

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+---
+base_model: ai-forever/ru-en-RoSBERTa
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:19988
+- loss:MultipleNegativesRankingLoss
+- loss:BatchHardSoftMarginTripletLoss
+widget:
+- source_sentence: Продам учебники для 1ого класса для русского сектора (по программе
+ Евро2000). 100 лари
+ sentences:
+ - Заказывали картку не подошло по размеру продам за 20 лар размеры длина спины 38.5
+ обхват в груди 60
+ - Купите малышку🙏🏻🙏🏻🙏🏻
+ - 'MacBook air m2 15 Inch 8/512 (фактически два раза быстрее конфигурации 8/256) Макбук
+ в состояние нового, ни царапинки 5 циклов заряда Оригинальный комплект Комфортный
+ осмотр Проверки приветствуются! Цена: 3790 лари'
+- source_sentence: ⚠️⚠️⚠️⚠️⚠️⚠️⚠️ ОБМЕН НА ЗЕМЕЛЬНЫЙ УЧАСТОК ЯЛТА ИЛИ РЯДОМ⚠️⚠️⚠️⚠️⚠️⚠️ 🔥🔥🔥🔥🔥🔥🔥🔥🔥ЭЛИТНЫЙ
+ ПРИГОРОД ГОРОДА РОСТОВ НА ДОНУ🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥
+ sentences:
+ - Куплю да чемодана больших . Размер 75 -50-30
+ - Продаю новый перфоратор за 130 лари
+ - Набор для обучения чтению. Всё новое, 40 лари
+- source_sentence: Отдам в хорошие руки ненужные игрушки, всей семьей решили очистить
+ дом.
+ sentences:
+ - Кто продает строительные леса в Москве?
+ - Ищу работу как разработчик на PHP, можно удаленно, зарплата от 100к.
+ - Срочно ищу игрушки для детей, кто что продает?
+- source_sentence: Продам набор столовых приборов из серебра, 12 персон, в идеальном
+ состоянии, покупался за 50 тысяч, отдам за 30 тысяч.
+ sentences:
+ - Ищу набор столовых приборов, желательно из серебра, в хорошем состоянии, по доступной
+ цене.
+ - Ищу крысу декоративную, Москва.
+ - Нужны строители на строительство дома, оплата хорошая, звоните по номеру в профиле.
+- source_sentence: Продаю попугайчика, очень веселый, 5 лет, цена 3000 рублей.
+ sentences:
+ - Куплю комнатное растение сирень
+ - Нужен попугай, люблю этих птичек, вдруг кто-то продает.
+ - Ищу недорогой автомобиль для поездок по деревне, бюджет до 200 тысяч.
+---
+
+# SentenceTransformer based on ai-forever/ru-en-RoSBERTa
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ai-forever/ru-en-RoSBERTa](https://huggingface.co/ai-forever/ru-en-RoSBERTa) on the match-pairs and clusters datasets. It maps sentences & paragraphs to a 1024-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:** [ai-forever/ru-en-RoSBERTa](https://huggingface.co/ai-forever/ru-en-RoSBERTa) <!-- at revision 89fb1651989adbb1cfcfdedafd7d102951ad0555 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+ - match-pairs
+ - clusters
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: RobertaModel 
+ (1): Pooling({'word_embedding_dimension': 1024, '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): 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("poc-embeddings/ru-en-RoSBERTa-trade-magnet")
+# Run inference
+sentences = [
+ 'Продаю попугайчика, очень веселый, 5 лет, цена 3000 рублей.',
+ 'Нужен попугай, люблю этих птичек, вдруг кто-то продает.',
+ 'Ищу недорогой автомобиль для поездок по деревне, бюджет до 200 тысяч.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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 Datasets
+
+#### match-pairs
+
+* Dataset: match-pairs
+* Size: 536 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 536 samples:
+ | | anchor | positive |
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | <ul><li>min: 9 tokens</li><li>mean: 22.15 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.15 tokens</li><li>max: 40 tokens</li></ul> |
+* Samples:
+ | anchor | positive |
+ |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|
+ | <code>Ищу работу HR-менеджера, опыт 4 года, знание трудового законодательства.</code> | <code>Требуется HR-менеджер с опытом работы и знанием трудового законодательства.</code> |
+ | <code>Акция на косметику, 3 по цене 2, только до конца недели!</code> | <code>Кто видел скидки на косметику в последних рекламках?</code> |
+ | <code>Продам ковер ручной работы из шерсти, из Ирана, размер 2х3 метра, состояние отличное, покупался за 150 тысяч, отдам за 100 тысяч.</code> | <code>Ищу ковер из натуральных материалов, размер 2х3 метра, в хорошем состоянии, по адекватной цене.</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"
+ }
+ ```
+
+#### clusters
+
+* Dataset: clusters
+* Size: 19,452 training samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+ | | sentence | label |
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+ | type | string | int |
+ | details | <ul><li>min: 5 tokens</li><li>mean: 49.97 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~2.30%</li><li>1: ~0.70%</li><li>2: ~0.60%</li><li>3: ~1.40%</li><li>4: ~0.20%</li><li>5: ~0.50%</li><li>6: ~1.60%</li><li>7: ~7.30%</li><li>8: ~0.50%</li><li>9: ~0.90%</li><li>10: ~0.40%</li><li>11: ~13.40%</li><li>12: ~0.70%</li><li>13: ~1.10%</li><li>14: ~1.60%</li><li>15: ~3.80%</li><li>16: ~2.70%</li><li>17: ~1.70%</li><li>18: ~3.40%</li><li>19: ~0.70%</li><li>20: ~1.20%</li><li>21: ~1.00%</li><li>22: ~2.70%</li><li>23: ~3.80%</li><li>24: ~4.20%</li><li>25: ~1.10%</li><li>26: ~4.00%</li><li>27: ~0.70%</li><li>28: ~1.90%</li><li>29: ~0.60%</li><li>30: ~0.90%</li><li>31: ~5.70%</li><li>32: ~1.40%</li><li>33: ~1.60%</li><li>34: ~0.80%</li><li>35: ~3.50%</li><li>36: ~0.50%</li><li>37: ~0.10%</li><li>38: ~0.70%</li><li>39: ~0.40%</li><li>40: ~0.40%</li><li>41: ~0.50%</li><li>42: ~0.10%</li><li>43: ~1.00%</li><li>44: ~1.70%</li><li>45: ~0.40%</li><li>46: ~1.10%</li><li>47: ~0.70%</li><li>48: ~0.70%</li><li>49: ~1.10%</li><li>50: ~0.50%</li><li>51: ~0.20%</li><li>52: ~0.50%</li><li>53: ~0.80%</li><li>54: ~0.70%</li><li>55: ~0.80%</li><li>56: ~0.20%</li><li>57: ~0.70%</li><li>58: ~0.20%</li><li>59: ~0.40%</li><li>60: ~0.30%</li><li>61: ~0.40%</li><li>63: ~0.80%</li><li>64: ~0.20%</li><li>65: ~0.90%</li><li>66: ~0.20%</li><li>67: ~0.20%</li><li>68: ~0.20%</li><li>69: ~0.10%</li><li>70: ~0.20%</li><li>71: ~0.20%</li><li>73: ~0.50%</li><li>74: ~0.10%</li><li>75: ~0.20%</li><li>76: ~0.20%</li><li>78: ~0.30%</li></ul> |
+* Samples:
+ | sentence | label |
+ |:---------------------------------------------------------------------------------------------------------------------------|:----------------|
+ | <code>Продам кроссовки New Balance 574 Новые. Размер: 9 US, 42.5 EU Цена: 250 лари Больше моделей в шапке профиля.</code> | <code>31</code> |
+ | <code>Куплю Новый MagicQ MQ250M</code> | <code>27</code> |
+ | <code>КУПЛЮ iPhone 6s, 7, 8 возможно с дефектом‼️</code> | <code>15</code> |
+* Loss: [<code>BatchHardSoftMarginTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchhardsoftmargintripletloss)
+
+### Evaluation Datasets
+
+#### match-pairs
+
+* Dataset: match-pairs
+* Size: 536 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 536 samples:
+ | | anchor | positive |
+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | type | string | string |
+ | details | <ul><li>min: 11 tokens</li><li>mean: 21.78 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.61 tokens</li><li>max: 39 tokens</li></ul> |
+* Samples:
+ | anchor | positive |
+ |:------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+ | <code>Отдам бульдозер Komatsu, почти новый, Ростов-на-Дону, 4 млн рублей.</code> | <code>Кто продает бульдозер Komatsu в Ростове-на-Дону?</code> |
+ | <code>Нужен PHP-разработчик, удаленка, ЗП до 150к.</code> | <code>Ищу работу как разработчик на PHP, можно удаленно, зарплата от 100к.</code> |
+ | <code>Ищу программиста Python, нужен опытный человек, чтобы сделать сайт для компании, пишите в личку, обсудим детали.</code> | <code>Программист python, опыт работы 2 года.</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"
+ }
+ ```
+
+#### clusters
+
+* Dataset: clusters
+* Size: 19,452 evaluation samples
+* Columns: <code>sentence</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+ | | sentence | label |
+ |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+ | type | string | int |
+ | details | <ul><li>min: 5 tokens</li><li>mean: 48.11 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~1.80%</li><li>1: ~0.60%</li><li>2: ~0.50%</li><li>3: ~0.40%</li><li>4: ~0.60%</li><li>5: ~0.60%</li><li>6: ~1.80%</li><li>7: ~5.40%</li><li>8: ~0.30%</li><li>9: ~1.00%</li><li>10: ~0.40%</li><li>11: ~13.70%</li><li>12: ~0.70%</li><li>13: ~1.30%</li><li>14: ~1.60%</li><li>15: ~3.70%</li><li>16: ~2.60%</li><li>17: ~1.90%</li><li>18: ~3.60%</li><li>19: ~0.30%</li><li>20: ~0.80%</li><li>21: ~1.20%</li><li>22: ~2.70%</li><li>23: ~3.20%</li><li>24: ~5.30%</li><li>25: ~0.40%</li><li>26: ~4.10%</li><li>27: ~0.80%</li><li>28: ~2.00%</li><li>29: ~0.80%</li><li>30: ~0.70%</li><li>31: ~7.40%</li><li>32: ~1.20%</li><li>33: ~1.30%</li><li>34: ~0.80%</li><li>35: ~2.80%</li><li>36: ~0.50%</li><li>37: ~0.60%</li><li>38: ~0.30%</li><li>39: ~0.10%</li><li>40: ~0.80%</li><li>41: ~1.20%</li><li>42: ~0.40%</li><li>43: ~0.80%</li><li>44: ~2.10%</li><li>45: ~0.60%</li><li>46: ~0.50%</li><li>47: ~0.70%</li><li>48: ~0.60%</li><li>49: ~0.40%</li><li>50: ~0.90%</li><li>51: ~0.20%</li><li>52: ~0.60%</li><li>53: ~1.00%</li><li>54: ~1.10%</li><li>55: ~0.80%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.50%</li><li>60: ~0.30%</li><li>61: ~0.10%</li><li>62: ~0.30%</li><li>63: ~0.70%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.60%</li><li>67: ~0.50%</li><li>68: ~0.10%</li><li>69: ~0.30%</li><li>70: ~0.20%</li><li>71: ~0.40%</li><li>72: ~0.10%</li><li>73: ~0.20%</li><li>74: ~0.10%</li><li>75: ~0.10%</li><li>76: ~0.40%</li><li>77: ~0.10%</li><li>78: ~0.30%</li></ul> |
+* Samples:
+ | sentence | label |
+ |:----------------------------------------------------------------------------------------------------|:----------------|
+ | <code>Куплю клетчатую сумку с замком, либо подобную, пишите в лс</code> | <code>1</code> |
+ | <code>asus r 752 l - 1tb HDD, 12gb ddr3, nvidia GeForce 940, intel core i7 5500u - 550 лари.</code> | <code>14</code> |
+ | <code>срочно Продам геймпад Defender X7 с держателем для телефона Состояние - новый 1300р.</code> | <code>15</code> |
+* Loss: [<code>BatchHardSoftMarginTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchhardsoftmargintripletloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.022
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.17
+- `fp16`: True
+- `dataloader_num_workers`: 8
+
+#### 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 0.022
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.17
+- `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`: 8
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | match-pairs loss | clusters loss |
+|:------:|:----:|:-------------:|:----------------:|:-------------:|
+| 0.3546 | 50 | 0.6678 | 1.0062 | 0.6543 |
+| 0.7092 | 100 | 0.7114 | 0.7569 | 0.6323 |
+| 1.0638 | 150 | 0.6571 | 0.7267 | 0.6181 |
+| 1.4184 | 200 | 0.6263 | 0.9529 | 0.6057 |
+| 1.7730 | 250 | 0.6396 | 0.9458 | 0.5934 |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- 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",
+}
+```
+
+#### 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}
+}
+```
+
+#### BatchHardSoftMarginTripletLoss
+```bibtex
+@misc{hermans2017defense,
+ title={In Defense of the Triplet Loss for Person Re-Identification},
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+ year={2017},
+ eprint={1703.07737},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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

@@ -0,0 +1,28 @@
+{
+ "_name_or_path": "/content/drive/MyDrive/Colab Notebooks/trade-magnet/ai-forever/ru-en-RoSBERTa/checkpoint-282",
+ "architectures": [
+ "RobertaModel"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "bos_token_id": 1,
+ "classifier_dropout": null,
+ "eos_token_id": 2,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 514,
+ "model_type": "roberta",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 1,
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.44.2",
+ "type_vocab_size": 1,
+ "use_cache": true,
+ "vocab_size": 98505
+}

config_sentence_transformers.json

@@ -0,0 +1,15 @@
+{
+ "prompts": {
+ "classification": "classification: ",
+ "search_query": "search_query: ",
+ "search_document": "search_document: ",
+ "clustering": "clustering: "
+ },
+ "default_prompt_name": null,
+ "similarity_fn_name": null,
+ "__version__": {
+ "sentence_transformers": "3.2.0",
+ "transformers": "4.44.2",
+ "pytorch": "2.4.1+cu121"
+ }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ddf5658c9df608ea468071c9bf4b11cd4b7e3c2dff63be3fc154d4467cd39fe
+size 1619075072

modules.json

@@ -0,0 +1,20 @@
+[
+ {
+ "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_Normalize",
+ "type": "sentence_transformers.models.Normalize"
+ }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+ "max_seq_length": 128,
+ "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+ "bos_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "cls_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "mask_token": {
+ "content": "<mask>",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<pad>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "sep_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+ "add_prefix_space": false,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "<pad>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "3": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "4": {
+ "content": "<mask>",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "bos_token": "<s>",
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "<s>",
+ "eos_token": "</s>",
+ "errors": "replace",
+ "mask_token": "<mask>",
+ "max_length": 512,
+ "model_max_length": 128,
+ "pad_to_multiple_of": null,
+ "pad_token": "<pad>",
+ "pad_token_type_id": 0,
+ "padding_side": "right",
+ "sep_token": "</s>",
+ "stride": 0,
+ "tokenizer_class": "RobertaTokenizer",
+ "trim_offsets": true,
+ "truncation_side": "right",
+ "truncation_strategy": "longest_first",
+ "unk_token": "<unk>"
+}