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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:131157 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: عواقب ممنوعیت یادداشت های 500 روپیه و 1000 روپیه در مورد اقتصاد |
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هند چیست؟ |
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sentences: |
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- آیا باید در فیزیک و علوم کامپیوتر دو برابر کنم؟ |
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- چگونه اقتصاد هند پس از ممنوعیت 500 1000 یادداشت تحت تأثیر قرار گرفت؟ |
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- آیا آلمان در اجازه پناهندگان سوری به کشور خود اشتباه کرد؟ |
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- source_sentence: بهترین شماره پشتیبانی فنی QuickBooks در نیویورک ، ایالات متحده |
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کدام است؟ |
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sentences: |
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- فناوری هایی که اکثر مردم از آنها نمی دانند چیست؟ |
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- بهترین شماره پشتیبانی QuickBooks در آرکانزاس چیست؟ |
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- چرا در مقایسه با طرف نزدیک ، دهانه های زیادی در قسمت دور ماه وجود دارد؟ |
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- source_sentence: اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA |
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در میشیگان چیست؟ |
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sentences: |
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- پیروزی ترامپ چگونه بر کانادا تأثیر خواهد گذاشت؟ |
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- اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA در آیداهو چیست؟ |
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- مزایای خرید بیمه عمر چیست؟ |
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- source_sentence: چرا این همه افراد ناراضی هستند؟ |
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sentences: |
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- چرا آب نبات تافی آب شور در مغولستان وارد می شود؟ |
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- برای یک رابطه موفق از راه دور چه چیزی طول می کشد؟ |
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- چرا مردم ناراضی هستند؟ |
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- source_sentence: برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟ |
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sentences: |
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- چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟ |
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- چرا بسیاری از افرادی که سؤالاتی را در Quora ارسال می کنند ، ابتدا Google را بررسی |
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می کنند؟ |
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- من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام |
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یک را بخرید؟ |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, '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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("codersan/validadted_all-MiniLM_onV9") |
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# Run inference |
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sentences = [ |
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'برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟', |
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'چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟', |
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'من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام یک را بخرید؟', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 131,157 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 44.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.6 tokens</li><li>max: 154 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>وقتی سوال من به عنوان "این سوال ممکن است به ویرایش نیاز داشته باشد" چه کاری باید انجام دهم ، اما نمی توانم دلیل آن را پیدا کنم؟</code> | <code>چرا سوال من به عنوان نیاز به پیشرفت مشخص شده است؟</code> | |
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| <code>چگونه می توانید یک فایل رمزگذاری شده را با دانستن اینکه این یک فایل تصویری است بدون دانستن گسترش پرونده یا کلید ، رمزگشایی کنید؟</code> | <code>چگونه می توانید یک فایل رمزگذاری شده را رمزگشایی کنید و بدانید که این یک فایل تصویری است بدون اینکه از پسوند پرونده اطلاع داشته باشید؟</code> | |
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| <code>احساس می کنم خودکشی می کنم ، چگونه باید با آن برخورد کنم؟</code> | <code>احساس می کنم خودکشی می کنم.چه کاری باید انجام دهم؟</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 64 |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 15 |
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- `warmup_ratio`: 0.1 |
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- `push_to_hub`: True |
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- `hub_model_id`: codersan/validadted_all-MiniLM_onV9 |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.01 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 15 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: True |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: codersan/validadted_all-MiniLM_onV9 |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | |
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|:-------:|:-----:|:-------------:| |
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| 0.0488 | 100 | 2.841 | |
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| 0.0976 | 200 | 2.1716 | |
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| 0.1463 | 300 | 1.5024 | |
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| 0.1951 | 400 | 1.2579 | |
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| 0.2439 | 500 | 1.1434 | |
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| 0.2927 | 600 | 1.0665 | |
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| 0.3415 | 700 | 0.9581 | |
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| 0.3902 | 800 | 0.9106 | |
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| 0.4390 | 900 | 0.87 | |
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| 0.4878 | 1000 | 0.7785 | |
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| 0.5366 | 1100 | 0.7591 | |
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| 0.5854 | 1200 | 0.6928 | |
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| 0.6341 | 1300 | 0.6778 | |
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| 0.6829 | 1400 | 0.6395 | |
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| 0.7317 | 1500 | 0.6145 | |
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| 0.7805 | 1600 | 0.5678 | |
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| 0.8293 | 1700 | 0.5602 | |
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| 0.8780 | 1800 | 0.5498 | |
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| 0.9268 | 1900 | 0.5292 | |
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| 0.9756 | 2000 | 0.4819 | |
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| 1.0244 | 2100 | 0.4717 | |
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| 1.0732 | 2200 | 0.4837 | |
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| 1.1220 | 2300 | 0.4404 | |
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| 1.1707 | 2400 | 0.4359 | |
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| 1.2195 | 2500 | 0.4121 | |
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| 1.2683 | 2600 | 0.434 | |
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| 1.3171 | 2700 | 0.4018 | |
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| 1.3659 | 2800 | 0.3866 | |
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| 1.4146 | 2900 | 0.3889 | |
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| 1.4634 | 3000 | 0.3595 | |
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| 1.5122 | 3100 | 0.3547 | |
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| 1.5610 | 3200 | 0.3517 | |
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| 1.6098 | 3300 | 0.3331 | |
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| 1.6585 | 3400 | 0.3228 | |
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| 1.7073 | 3500 | 0.3101 | |
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| 1.7561 | 3600 | 0.3071 | |
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| 1.8049 | 3700 | 0.288 | |
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| 1.8537 | 3800 | 0.3115 | |
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| 1.9024 | 3900 | 0.2777 | |
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| 1.9512 | 4000 | 0.2902 | |
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| 2.0 | 4100 | 0.2926 | |
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| 2.0488 | 4200 | 0.2958 | |
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| 2.0976 | 4300 | 0.2688 | |
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| 2.1463 | 4400 | 0.2647 | |
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| 2.1951 | 4500 | 0.2523 | |
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| 2.2439 | 4600 | 0.2681 | |
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| 2.2927 | 4700 | 0.2714 | |
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| 2.3415 | 4800 | 0.2575 | |
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| 2.3902 | 4900 | 0.2462 | |
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| 2.4390 | 5000 | 0.2466 | |
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| 2.4878 | 5100 | 0.2215 | |
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| 2.5366 | 5200 | 0.2424 | |
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| 2.5854 | 5300 | 0.2264 | |
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| 2.6341 | 5400 | 0.2252 | |
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| 2.6829 | 5500 | 0.2228 | |
|
|
| 2.7317 | 5600 | 0.2337 | |
|
|
| 2.7805 | 5700 | 0.1983 | |
|
|
| 2.8293 | 5800 | 0.2156 | |
|
|
| 2.8780 | 5900 | 0.2088 | |
|
|
| 2.9268 | 6000 | 0.2196 | |
|
|
| 2.9756 | 6100 | 0.2054 | |
|
|
| 3.0244 | 6200 | 0.2114 | |
|
|
| 3.0732 | 6300 | 0.2191 | |
|
|
| 3.1220 | 6400 | 0.1899 | |
|
|
| 3.1707 | 6500 | 0.1958 | |
|
|
| 3.2195 | 6600 | 0.1907 | |
|
|
| 3.2683 | 6700 | 0.2151 | |
|
|
| 3.3171 | 6800 | 0.1918 | |
|
|
| 3.3659 | 6900 | 0.1859 | |
|
|
| 3.4146 | 7000 | 0.1962 | |
|
|
| 3.4634 | 7100 | 0.1807 | |
|
|
| 3.5122 | 7200 | 0.1874 | |
|
|
| 3.5610 | 7300 | 0.179 | |
|
|
| 3.6098 | 7400 | 0.1779 | |
|
|
| 3.6585 | 7500 | 0.1726 | |
|
|
| 3.7073 | 7600 | 0.1693 | |
|
|
| 3.7561 | 7700 | 0.1708 | |
|
|
| 3.8049 | 7800 | 0.1697 | |
|
|
| 3.8537 | 7900 | 0.1744 | |
|
|
| 3.9024 | 8000 | 0.1581 | |
|
|
| 3.9512 | 8100 | 0.1761 | |
|
|
| 4.0 | 8200 | 0.1724 | |
|
|
| 4.0488 | 8300 | 0.1777 | |
|
|
| 4.0976 | 8400 | 0.1591 | |
|
|
| 4.1463 | 8500 | 0.1559 | |
|
|
| 4.1951 | 8600 | 0.1518 | |
|
|
| 4.2439 | 8700 | 0.1608 | |
|
|
| 4.2927 | 8800 | 0.1751 | |
|
|
| 4.3415 | 8900 | 0.1572 | |
|
|
| 4.3902 | 9000 | 0.1498 | |
|
|
| 4.4390 | 9100 | 0.16 | |
|
|
| 4.4878 | 9200 | 0.137 | |
|
|
| 4.5366 | 9300 | 0.1545 | |
|
|
| 4.5854 | 9400 | 0.1443 | |
|
|
| 4.6341 | 9500 | 0.1482 | |
|
|
| 4.6829 | 9600 | 0.1383 | |
|
|
| 4.7317 | 9700 | 0.1468 | |
|
|
| 4.7805 | 9800 | 0.1331 | |
|
|
| 4.8293 | 9900 | 0.1471 | |
|
|
| 4.8780 | 10000 | 0.1352 | |
|
|
| 4.9268 | 10100 | 0.1474 | |
|
|
| 4.9756 | 10200 | 0.1465 | |
|
|
| 5.0244 | 10300 | 0.1401 | |
|
|
| 5.0732 | 10400 | 0.1488 | |
|
|
| 5.1220 | 10500 | 0.1285 | |
|
|
| 5.1707 | 10600 | 0.1326 | |
|
|
| 5.2195 | 10700 | 0.1246 | |
|
|
| 5.2683 | 10800 | 0.1532 | |
|
|
| 5.3171 | 10900 | 0.1345 | |
|
|
| 5.3659 | 11000 | 0.1246 | |
|
|
| 5.4146 | 11100 | 0.1344 | |
|
|
| 5.4634 | 11200 | 0.1214 | |
|
|
| 5.5122 | 11300 | 0.1283 | |
|
|
| 5.5610 | 11400 | 0.1235 | |
|
|
| 5.6098 | 11500 | 0.1265 | |
|
|
| 5.6585 | 11600 | 0.1248 | |
|
|
| 5.7073 | 11700 | 0.1204 | |
|
|
| 5.7561 | 11800 | 0.119 | |
|
|
| 5.8049 | 11900 | 0.1174 | |
|
|
| 5.8537 | 12000 | 0.1273 | |
|
|
| 5.9024 | 12100 | 0.1107 | |
|
|
| 5.9512 | 12200 | 0.1277 | |
|
|
| 6.0 | 12300 | 0.1178 | |
|
|
| 6.0488 | 12400 | 0.1286 | |
|
|
| 6.0976 | 12500 | 0.1145 | |
|
|
| 6.1463 | 12600 | 0.1164 | |
|
|
| 6.1951 | 12700 | 0.1134 | |
|
|
| 6.2439 | 12800 | 0.1211 | |
|
|
| 6.2927 | 12900 | 0.125 | |
|
|
| 6.3415 | 13000 | 0.1187 | |
|
|
| 6.3902 | 13100 | 0.1108 | |
|
|
| 6.4390 | 13200 | 0.1148 | |
|
|
| 6.4878 | 13300 | 0.1046 | |
|
|
| 6.5366 | 13400 | 0.1097 | |
|
|
| 6.5854 | 13500 | 0.1066 | |
|
|
| 6.6341 | 13600 | 0.1078 | |
|
|
| 6.6829 | 13700 | 0.102 | |
|
|
| 6.7317 | 13800 | 0.107 | |
|
|
| 6.7805 | 13900 | 0.1008 | |
|
|
| 6.8293 | 14000 | 0.1113 | |
|
|
| 6.8780 | 14100 | 0.0987 | |
|
|
| 6.9268 | 14200 | 0.1123 | |
|
|
| 6.9756 | 14300 | 0.1062 | |
|
|
| 7.0244 | 14400 | 0.1101 | |
|
|
| 7.0732 | 14500 | 0.1129 | |
|
|
| 7.1220 | 14600 | 0.0963 | |
|
|
| 7.1707 | 14700 | 0.1053 | |
|
|
| 7.2195 | 14800 | 0.0988 | |
|
|
| 7.2683 | 14900 | 0.119 | |
|
|
| 7.3171 | 15000 | 0.0993 | |
|
|
| 7.3659 | 15100 | 0.0986 | |
|
|
| 7.4146 | 15200 | 0.1012 | |
|
|
| 7.4634 | 15300 | 0.0902 | |
|
|
| 7.5122 | 15400 | 0.103 | |
|
|
| 7.5610 | 15500 | 0.0961 | |
|
|
| 7.6098 | 15600 | 0.0981 | |
|
|
| 7.6585 | 15700 | 0.0972 | |
|
|
| 7.7073 | 15800 | 0.0965 | |
|
|
| 7.7561 | 15900 | 0.0916 | |
|
|
| 7.8049 | 16000 | 0.0943 | |
|
|
| 7.8537 | 16100 | 0.0973 | |
|
|
| 7.9024 | 16200 | 0.0828 | |
|
|
| 7.9512 | 16300 | 0.1036 | |
|
|
| 8.0 | 16400 | 0.0986 | |
|
|
| 8.0488 | 16500 | 0.1008 | |
|
|
| 8.0976 | 16600 | 0.0897 | |
|
|
| 8.1463 | 16700 | 0.092 | |
|
|
| 8.1951 | 16800 | 0.0901 | |
|
|
| 8.2439 | 16900 | 0.0979 | |
|
|
| 8.2927 | 17000 | 0.0989 | |
|
|
| 8.3415 | 17100 | 0.0937 | |
|
|
| 8.3902 | 17200 | 0.0882 | |
|
|
| 8.4390 | 17300 | 0.0902 | |
|
|
| 8.4878 | 17400 | 0.0792 | |
|
|
| 8.5366 | 17500 | 0.0893 | |
|
|
| 8.5854 | 17600 | 0.0861 | |
|
|
| 8.6341 | 17700 | 0.0866 | |
|
|
| 8.6829 | 17800 | 0.0831 | |
|
|
| 8.7317 | 17900 | 0.0893 | |
|
|
| 8.7805 | 18000 | 0.0785 | |
|
|
| 8.8293 | 18100 | 0.093 | |
|
|
| 8.8780 | 18200 | 0.0815 | |
|
|
| 8.9268 | 18300 | 0.0929 | |
|
|
| 8.9756 | 18400 | 0.0869 | |
|
|
| 9.0244 | 18500 | 0.0874 | |
|
|
| 9.0732 | 18600 | 0.0944 | |
|
|
| 9.1220 | 18700 | 0.0809 | |
|
|
| 9.1707 | 18800 | 0.0845 | |
|
|
| 9.2195 | 18900 | 0.0812 | |
|
|
| 9.2683 | 19000 | 0.0966 | |
|
|
| 9.3171 | 19100 | 0.0819 | |
|
|
| 9.3659 | 19200 | 0.08 | |
|
|
| 9.4146 | 19300 | 0.0849 | |
|
|
| 9.4634 | 19400 | 0.0773 | |
|
|
| 9.5122 | 19500 | 0.0822 | |
|
|
| 9.5610 | 19600 | 0.0781 | |
|
|
| 9.6098 | 19700 | 0.0798 | |
|
|
| 9.6585 | 19800 | 0.0745 | |
|
|
| 9.7073 | 19900 | 0.0763 | |
|
|
| 9.7561 | 20000 | 0.074 | |
|
|
| 9.8049 | 20100 | 0.0786 | |
|
|
| 9.8537 | 20200 | 0.082 | |
|
|
| 9.9024 | 20300 | 0.0685 | |
|
|
| 9.9512 | 20400 | 0.0857 | |
|
|
| 10.0 | 20500 | 0.0791 | |
|
|
| 10.0488 | 20600 | 0.0865 | |
|
|
| 10.0976 | 20700 | 0.0801 | |
|
|
| 10.1463 | 20800 | 0.0792 | |
|
|
| 10.1951 | 20900 | 0.0754 | |
|
|
| 10.2439 | 21000 | 0.082 | |
|
|
| 10.2927 | 21100 | 0.0849 | |
|
|
| 10.3415 | 21200 | 0.0765 | |
|
|
| 10.3902 | 21300 | 0.0749 | |
|
|
| 10.4390 | 21400 | 0.0793 | |
|
|
| 10.4878 | 21500 | 0.0702 | |
|
|
| 10.5366 | 21600 | 0.0751 | |
|
|
| 10.5854 | 21700 | 0.074 | |
|
|
| 10.6341 | 21800 | 0.0733 | |
|
|
| 10.6829 | 21900 | 0.0743 | |
|
|
| 10.7317 | 22000 | 0.0747 | |
|
|
| 10.7805 | 22100 | 0.0658 | |
|
|
| 10.8293 | 22200 | 0.0787 | |
|
|
| 10.8780 | 22300 | 0.07 | |
|
|
| 10.9268 | 22400 | 0.0803 | |
|
|
| 10.9756 | 22500 | 0.074 | |
|
|
| 11.0244 | 22600 | 0.0737 | |
|
|
| 11.0732 | 22700 | 0.0769 | |
|
|
| 11.1220 | 22800 | 0.0652 | |
|
|
| 11.1707 | 22900 | 0.0714 | |
|
|
| 11.2195 | 23000 | 0.0682 | |
|
|
| 11.2683 | 23100 | 0.0873 | |
|
|
| 11.3171 | 23200 | 0.0693 | |
|
|
| 11.3659 | 23300 | 0.069 | |
|
|
| 11.4146 | 23400 | 0.0747 | |
|
|
| 11.4634 | 23500 | 0.0647 | |
|
|
| 11.5122 | 23600 | 0.0737 | |
|
|
| 11.5610 | 23700 | 0.0714 | |
|
|
| 11.6098 | 23800 | 0.0715 | |
|
|
| 11.6585 | 23900 | 0.0666 | |
|
|
| 11.7073 | 24000 | 0.0702 | |
|
|
| 11.7561 | 24100 | 0.0643 | |
|
|
| 11.8049 | 24200 | 0.0654 | |
|
|
| 11.8537 | 24300 | 0.0685 | |
|
|
| 11.9024 | 24400 | 0.0593 | |
|
|
| 11.9512 | 24500 | 0.0775 | |
|
|
| 12.0 | 24600 | 0.0721 | |
|
|
| 12.0488 | 24700 | 0.076 | |
|
|
| 12.0976 | 24800 | 0.0653 | |
|
|
| 12.1463 | 24900 | 0.0677 | |
|
|
| 12.1951 | 25000 | 0.0652 | |
|
|
| 12.2439 | 25100 | 0.076 | |
|
|
| 12.2927 | 25200 | 0.0741 | |
|
|
| 12.3415 | 25300 | 0.0677 | |
|
|
| 12.3902 | 25400 | 0.065 | |
|
|
| 12.4390 | 25500 | 0.0709 | |
|
|
| 12.4878 | 25600 | 0.0625 | |
|
|
| 12.5366 | 25700 | 0.0666 | |
|
|
| 12.5854 | 25800 | 0.0665 | |
|
|
| 12.6341 | 25900 | 0.0679 | |
|
|
| 12.6829 | 26000 | 0.0636 | |
|
|
| 12.7317 | 26100 | 0.0638 | |
|
|
| 12.7805 | 26200 | 0.0596 | |
|
|
| 12.8293 | 26300 | 0.0693 | |
|
|
| 12.8780 | 26400 | 0.0588 | |
|
|
| 12.9268 | 26500 | 0.0726 | |
|
|
| 12.9756 | 26600 | 0.0671 | |
|
|
| 13.0244 | 26700 | 0.0666 | |
|
|
| 13.0732 | 26800 | 0.0711 | |
|
|
| 13.1220 | 26900 | 0.0604 | |
|
|
| 13.1707 | 27000 | 0.0687 | |
|
|
| 13.2195 | 27100 | 0.0613 | |
|
|
| 13.2683 | 27200 | 0.0781 | |
|
|
| 13.3171 | 27300 | 0.0596 | |
|
|
| 13.3659 | 27400 | 0.0627 | |
|
|
| 13.4146 | 27500 | 0.0655 | |
|
|
| 13.4634 | 27600 | 0.0589 | |
|
|
| 13.5122 | 27700 | 0.0633 | |
|
|
| 13.5610 | 27800 | 0.0622 | |
|
|
| 13.6098 | 27900 | 0.065 | |
|
|
| 13.6585 | 28000 | 0.06 | |
|
|
| 13.7073 | 28100 | 0.063 | |
|
|
| 13.7561 | 28200 | 0.0589 | |
|
|
| 13.8049 | 28300 | 0.0623 | |
|
|
| 13.8537 | 28400 | 0.062 | |
|
|
| 13.9024 | 28500 | 0.0559 | |
|
|
| 13.9512 | 28600 | 0.0723 | |
|
|
| 14.0 | 28700 | 0.0658 | |
|
|
| 14.0488 | 28800 | 0.0687 | |
|
|
| 14.0976 | 28900 | 0.0606 | |
|
|
| 14.1463 | 29000 | 0.0622 | |
|
|
| 14.1951 | 29100 | 0.0604 | |
|
|
| 14.2439 | 29200 | 0.0657 | |
|
|
| 14.2927 | 29300 | 0.067 | |
|
|
| 14.3415 | 29400 | 0.0653 | |
|
|
| 14.3902 | 29500 | 0.0587 | |
|
|
| 14.4390 | 29600 | 0.0641 | |
|
|
| 14.4878 | 29700 | 0.0558 | |
|
|
| 14.5366 | 29800 | 0.0625 | |
|
|
| 14.5854 | 29900 | 0.0613 | |
|
|
| 14.6341 | 30000 | 0.0618 | |
|
|
| 14.6829 | 30100 | 0.0596 | |
|
|
| 14.7317 | 30200 | 0.0575 | |
|
|
| 14.7805 | 30300 | 0.0552 | |
|
|
| 14.8293 | 30400 | 0.0669 | |
|
|
| 14.8780 | 30500 | 0.0552 | |
|
|
| 14.9268 | 30600 | 0.0665 | |
|
|
| 14.9756 | 30700 | 0.0625 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.12 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.47.0 |
|
|
- PyTorch: 2.5.1+cu121 |
|
|
- Accelerate: 1.2.1 |
|
|
- Datasets: 3.2.0 |
|
|
- 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} |
|
|
} |
|
|
``` |
|
|
|
|
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