Add model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,437 @@
----
-license: mit
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2560000
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: ما هي أفضل الفنادق في ايبوهبالقرب من Ipoh Parade Shopping Centre؟
+  sentences:
+  - Bei ORION gibt es eine Sale-Rubrik, in der alle reduzierten Artikel zu finden
+    sind. Wenn du also auf der Suche nach einem Schnäppchen bist, weißt du, an welcher
+    Stelle auf der Webseite du fündig wirst. Der Sale umfasst viele verschiedene Produke.
+    Von Toys bis hin zu Dessous und Drogerieartikel - es spielt keine Rolle, wonach
+    du suchst. Aufgrund der Produktvielfalt ist die Chance, dass du im Sale den passenden
+    Gegenstand findest, groß.
+  - عادة ما يكون لأصحاب النفوذ الجزئي ما بين 10000 و 100000 متابع.
+  - المسافرون الموثّقون إلى مدينة ايبوه الذين أقاموا قرب Ipoh Parade Shopping Centre
+    أعطوا أعلى التقييمات لـفندق فايل ، Zone Hotel (Ipoh) وGolden Roof Hotel Ampang
+    Ipoh.
+- source_sentence: Habe ich Vorteile, wenn ich früh in das Projekt einsteige?
+  sentences:
+  - نعم لدينا خصومات مُتعددة على جميع أعمال السواتر والمظلات والجلسات ففي فصل الشتاء
+    والصيف هناك خصومات مُتعددة في الأعياد والمناسبات
+  - Der Vorteil einer frühen Mitgliedschaft besteht in der Möglichkeit der Mitgestaltung
+    des Projektes. Alle später Hinzukommenden müssen die bis dahin getroffenen Entscheidungen
+    akzeptieren. Zudem entscheidet unter anderem auch das Eintrittsdatum in die eG
+    und das Engagement während des Projektverlaufes über die spätere Reihenfolge der
+    Vergabe der Wohnungen.
+  - Средняя оценка Registered от клиентов – 4 на основе 227 оценок и отзывов. Заходите
+    на сайт и прочитайте реальные отзывы о Registered.
+- source_sentence: В какое время доступны ваши технические услуги?
+  sentences:
+  - Наша команда технической поддержки обеспечивает круглосуточное обслуживание в
+    случае чрезвычайных ситуаций. Вы можете связаться с нами в любой день недели,
+    в любое время суток и получить поддержку для ваших холодильных систем. Услуги
+    по плановому техническому обслуживанию и ремонту предоставляются в обычное рабочее
+    время, а услуги предоставляются в экстренных случаях, в том числе в ночное время
+    и в выходные дни.
+  - این سوال کاملا به علاقه و مهارت شما بستگی دارد. اگر به درس شیمی علاقه زیادی دارید
+    این رشته بهترین انتخاب برای تحصیل در دانشگاه برای شما محسوب می‌شود.
+  - 'eo光は10Gを提供している光回線です。
+
+    提供エリアは、通常プランと変わらず関西地方と福井県です。しかし、一部の利用できないエリアもあるので契約前に確認しましょう。
+
+    関連記事
+
+    eo光の10Gプランの評判口コミ'
+- source_sentence: Supertotobet redtiger oyun çeşitleri hangileri?
+  sentences:
+  - Supertotobet redtiger oyunları arasında gold star, golden tsar, golden lotus,
+    blood suckers ve redtiger slot gibi çeşitli oyunlar vardır. Bu oyun seçeneklerini
+    kullanabilmek için oyunlar hakkında bilgi sahibi olmalısınız.
+  - Время выполнения проекта зависит от его сложности и размера. Обычно, время выполнения
+    проекта составляет несколько месяцев.
+  - 'Wer kein Homeoffice während des Coronavirus anbieten kann, ist dazu verpflichtet,
+    Schutzmaßnahmen zu ergreifen.
+
+    Die Arbeitsschutzbehörden der Länder sind befugt, Corona-Schutzmaßnahmen in Betrieben
+    zu kontrollieren und Fehlverhalten zu bestrafen. Bei Verstößen sind Bußgelder
+    in Höhe von bis zu 30.000 Euro möglich. Wiederholen sich schwere Verstöße, droht
+    den Verantwortlichen sogar bis zu einem Jahr Freiheitsstrafe.
+
+    Arbeitgeber, die die Vorschriften missachten, könnten zudem dafür haften, wenn
+    Mitarbeiter durch eine Corona-Infektion gesundheitliche Schäden erleiden.'
+- source_sentence: Muss der Deckel der TipBox beim Autoklavieren geöffnet werden?
+  sentences:
+  - ВВП (валовый внутренний продукт) - это общая стоимость всех товаров и услуг, произведенных
+    в стране за определенный период времени. Он является ключевым экономическим показателем,
+    который отражает общий уровень экономической активности и роста. Инвесторы следят
+    за ВВП, чтобы оценить состояние и перспективы экономики, потенциал для роста и
+    возможности для инвестиций
+  - برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به دلیل صدمه
+    ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد.
+  - Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im geschlossenen
+    Zustand autoklaviert werden.
+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: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Muss der Deckel der TipBox beim Autoklavieren geöffnet werden?',
+    'Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im geschlossenen Zustand autoklaviert werden.',
+    'برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به دلیل صدمه ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد.',
+]
+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: 2,560,000 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.02 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 66.82 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Hat myTime ein großes Produktsortiment?</code>                                                       | <code>Das Sortiment von myTime umfasst mehr als 13.000 Lebensmittel. Du findest alle Produkte, die du auch im Supermarkt findest, darunter Obst und Gemüse, trockene Lebensmittel wie Pasta und Reis, Backwaren, Snacks und Tiefkühlkost. Auch Getränke wie Kaffee, Alkohol und Soda findest du im Online-Supermarkt.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  | <code>Gibt es eine Tigerspin App?</code>                                                                   | <code>Tigerspin verzichtet auf eine mobile App. Wenn Sie ein paar Runden spielen möchten, öffnen Sie einfach die Webseite des Casinos und starten die Spiele im Browser.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  | <code>Bietet ihr auch maschinelle Übersetzungen an? Wenn ja, wann eignet sich diese und wann nicht?</code> | <code>Maschinelle Übersetzungen sind ein span­nen­des Thema, auch aktuell bei techtrans. Unter maschineller Übersetzung (MÜ) ver­steht man die auto­mati­sierte Über­tra­gung eines Aus­gangs­textes in die Ziel­sprache mittels einer so­ge­nannten Über­set­zungs­engine. Eine solche Engine kann nach re­gel­ba­sier­ten, statis­tischen oder neu­ro­nalen Prin­zipien auf­ge­baut sein.<br>Ob­wohl es maschinelle Über­set­zungs­engines schon seit einigen Jahr­zehn­ten gibt, ist erst mit der Ein­führung der neu­ro­nalen Engines (NMT) ca. ab dem Jahre 2015 die Output-Qualität ge­stie­gen. Nam­hafte Engine Provider sind zum Bei­spiel Google, DeepL, Microsoft, Amazon AWS und SDL. So ist es kaum ver­wunder­lich, dass diese Tech­no­logie zu­neh­mend Ein­zug sowohl in unseren All­tag als auch in den Über­set­zungs­pro­zess findet.</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
+
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: no
+- `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`: 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}
+- `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
+- `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
+- `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
+| Epoch | Step  | Training Loss |
+|:-----:|:-----:|:-------------:|
+| 0.025 | 500   | 0.1999        |
+| 0.05  | 1000  | 0.0279        |
+| 0.075 | 1500  | 0.0234        |
+| 0.1   | 2000  | 0.0203        |
+| 0.125 | 2500  | 0.0179        |
+| 0.15  | 3000  | 0.0171        |
+| 0.175 | 3500  | 0.0153        |
+| 0.2   | 4000  | 0.015         |
+| 0.225 | 4500  | 0.0143        |
+| 0.25  | 5000  | 0.014         |
+| 0.275 | 5500  | 0.0128        |
+| 0.3   | 6000  | 0.013         |
+| 0.325 | 6500  | 0.0129        |
+| 0.35  | 7000  | 0.0124        |
+| 0.375 | 7500  | 0.012         |
+| 0.4   | 8000  | 0.0121        |
+| 0.425 | 8500  | 0.0115        |
+| 0.45  | 9000  | 0.0113        |
+| 0.475 | 9500  | 0.0106        |
+| 0.5   | 10000 | 0.0107        |
+| 0.525 | 10500 | 0.011         |
+| 0.55  | 11000 | 0.0108        |
+| 0.575 | 11500 | 0.0103        |
+| 0.6   | 12000 | 0.0097        |
+| 0.625 | 12500 | 0.01          |
+| 0.65  | 13000 | 0.0104        |
+| 0.675 | 13500 | 0.0096        |
+| 0.7   | 14000 | 0.0096        |
+| 0.725 | 14500 | 0.0097        |
+| 0.75  | 15000 | 0.0097        |
+| 0.775 | 15500 | 0.0089        |
+| 0.8   | 16000 | 0.0089        |
+| 0.825 | 16500 | 0.0091        |
+| 0.85  | 17000 | 0.0085        |
+| 0.875 | 17500 | 0.0084        |
+| 0.9   | 18000 | 0.0089        |
+| 0.925 | 18500 | 0.0087        |
+| 0.95  | 19000 | 0.0087        |
+| 0.975 | 19500 | 0.0088        |
+| 1.0   | 20000 | 0.0089        |
+
+
+### Framework Versions
+- Python: 3.11.5
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.2.1
+- Datasets: 2.21.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### 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

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "./output/train_bi-encoder-margin_mse_en-FacebookAI-xlm-roberta-base-batch_size_64-2025-01-30_18-47-30/checkpoint-235740",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1696eeef2daa2f542901467abdab2ce775c8c163c267c48f9e366ff83081018b
+size 1112197096

modules.json

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

optimizer.pt

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