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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_allMiniLM_onV9f") |
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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`: 12 |
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- `learning_rate`: 5e-06 |
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- `weight_decay`: 0.01 |
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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_allMiniLM_onV9f |
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- `eval_on_start`: True |
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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`: 12 |
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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`: 5e-06 |
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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`: 3 |
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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_allMiniLM_onV9f |
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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`: True |
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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 | 0 | - | |
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| 0.0091 | 100 | 1.4865 | |
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| 0.0183 | 200 | 1.4429 | |
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| 0.0274 | 300 | 1.2725 | |
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| 0.0366 | 400 | 1.1602 | |
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| 0.0457 | 500 | 0.9429 | |
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| 0.0549 | 600 | 0.829 | |
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| 0.0640 | 700 | 0.7771 | |
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| 0.0732 | 800 | 0.6597 | |
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| 0.0823 | 900 | 0.5981 | |
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| 0.0915 | 1000 | 0.5826 | |
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| 0.1006 | 1100 | 0.5956 | |
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| 0.1098 | 1200 | 0.5254 | |
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| 0.1189 | 1300 | 0.5434 | |
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| 0.1281 | 1400 | 0.5495 | |
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| 0.1372 | 1500 | 0.4934 | |
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| 0.1464 | 1600 | 0.4684 | |
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| 0.1555 | 1700 | 0.4489 | |
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| 0.1647 | 1800 | 0.4401 | |
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| 0.1738 | 1900 | 0.4712 | |
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| 0.1830 | 2000 | 0.4407 | |
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| 0.1921 | 2100 | 0.4082 | |
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| 0.2013 | 2200 | 0.4384 | |
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| 0.2104 | 2300 | 0.3621 | |
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| 0.2196 | 2400 | 0.4423 | |
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| 0.2287 | 2500 | 0.4163 | |
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| 0.2379 | 2600 | 0.3769 | |
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| 0.2470 | 2700 | 0.3967 | |
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| 0.2562 | 2800 | 0.3812 | |
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| 0.2653 | 2900 | 0.3813 | |
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| 0.2745 | 3000 | 0.359 | |
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| 0.2836 | 3100 | 0.3454 | |
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| 0.2928 | 3200 | 0.3518 | |
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| 0.3019 | 3300 | 0.3306 | |
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| 0.3111 | 3400 | 0.3138 | |
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| 0.3202 | 3500 | 0.3416 | |
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| 0.3294 | 3600 | 0.3474 | |
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| 0.3385 | 3700 | 0.3153 | |
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| 0.3477 | 3800 | 0.2896 | |
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| 0.3568 | 3900 | 0.2737 | |
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| 0.3660 | 4000 | 0.3004 | |
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| 0.3751 | 4100 | 0.3109 | |
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| 0.3843 | 4200 | 0.2829 | |
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| 0.3934 | 4300 | 0.2729 | |
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| 0.4026 | 4400 | 0.2714 | |
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| 0.4117 | 4500 | 0.3014 | |
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| 0.4209 | 4600 | 0.27 | |
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| 0.4300 | 4700 | 0.3632 | |
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| 0.4392 | 4800 | 0.2571 | |
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| 0.4483 | 4900 | 0.2464 | |
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| 0.4575 | 5000 | 0.2681 | |
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| 0.4666 | 5100 | 0.2579 | |
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| 0.4758 | 5200 | 0.2377 | |
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| 0.4849 | 5300 | 0.2471 | |
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| 0.4941 | 5400 | 0.2625 | |
|
|
| 0.5032 | 5500 | 0.2336 | |
|
|
| 0.5124 | 5600 | 0.2553 | |
|
|
| 0.5215 | 5700 | 0.2549 | |
|
|
| 0.5306 | 5800 | 0.22 | |
|
|
| 0.5398 | 5900 | 0.2682 | |
|
|
| 0.5489 | 6000 | 0.2329 | |
|
|
| 0.5581 | 6100 | 0.2244 | |
|
|
| 0.5672 | 6200 | 0.2458 | |
|
|
| 0.5764 | 6300 | 0.1881 | |
|
|
| 0.5855 | 6400 | 0.209 | |
|
|
| 0.5947 | 6500 | 0.2103 | |
|
|
| 0.6038 | 6600 | 0.1982 | |
|
|
| 0.6130 | 6700 | 0.2023 | |
|
|
| 0.6221 | 6800 | 0.2244 | |
|
|
| 0.6313 | 6900 | 0.2051 | |
|
|
| 0.6404 | 7000 | 0.224 | |
|
|
| 0.6496 | 7100 | 0.2113 | |
|
|
| 0.6587 | 7200 | 0.2386 | |
|
|
| 0.6679 | 7300 | 0.1685 | |
|
|
| 0.6770 | 7400 | 0.2092 | |
|
|
| 0.6862 | 7500 | 0.1832 | |
|
|
| 0.6953 | 7600 | 0.1957 | |
|
|
| 0.7045 | 7700 | 0.2082 | |
|
|
| 0.7136 | 7800 | 0.2213 | |
|
|
| 0.7228 | 7900 | 0.177 | |
|
|
| 0.7319 | 8000 | 0.196 | |
|
|
| 0.7411 | 8100 | 0.2034 | |
|
|
| 0.7502 | 8200 | 0.2017 | |
|
|
| 0.7594 | 8300 | 0.1741 | |
|
|
| 0.7685 | 8400 | 0.2092 | |
|
|
| 0.7777 | 8500 | 0.1684 | |
|
|
| 0.7868 | 8600 | 0.1874 | |
|
|
| 0.7960 | 8700 | 0.1866 | |
|
|
| 0.8051 | 8800 | 0.2291 | |
|
|
| 0.8143 | 8900 | 0.1796 | |
|
|
| 0.8234 | 9000 | 0.2036 | |
|
|
| 0.8326 | 9100 | 0.2173 | |
|
|
| 0.8417 | 9200 | 0.2074 | |
|
|
| 0.8509 | 9300 | 0.1914 | |
|
|
| 0.8600 | 9400 | 0.1639 | |
|
|
| 0.8692 | 9500 | 0.1798 | |
|
|
| 0.8783 | 9600 | 0.1926 | |
|
|
| 0.8875 | 9700 | 0.1672 | |
|
|
| 0.8966 | 9800 | 0.1727 | |
|
|
| 0.9058 | 9900 | 0.189 | |
|
|
| 0.9149 | 10000 | 0.2055 | |
|
|
| 0.9241 | 10100 | 0.2043 | |
|
|
| 0.9332 | 10200 | 0.1515 | |
|
|
| 0.9424 | 10300 | 0.1675 | |
|
|
| 0.9515 | 10400 | 0.1764 | |
|
|
| 0.9607 | 10500 | 0.1709 | |
|
|
| 0.9698 | 10600 | 0.1861 | |
|
|
| 0.9790 | 10700 | 0.1928 | |
|
|
| 0.9881 | 10800 | 0.1756 | |
|
|
| 0.9973 | 10900 | 0.1611 | |
|
|
| 1.0064 | 11000 | 0.1371 | |
|
|
| 1.0156 | 11100 | 0.1499 | |
|
|
| 1.0247 | 11200 | 0.2001 | |
|
|
| 1.0339 | 11300 | 0.197 | |
|
|
| 1.0430 | 11400 | 0.2035 | |
|
|
| 1.0522 | 11500 | 0.1524 | |
|
|
| 1.0613 | 11600 | 0.1988 | |
|
|
| 1.0704 | 11700 | 0.1643 | |
|
|
| 1.0796 | 11800 | 0.1488 | |
|
|
| 1.0887 | 11900 | 0.1402 | |
|
|
| 1.0979 | 12000 | 0.1501 | |
|
|
| 1.1070 | 12100 | 0.1476 | |
|
|
| 1.1162 | 12200 | 0.1703 | |
|
|
| 1.1253 | 12300 | 0.1437 | |
|
|
| 1.1345 | 12400 | 0.1684 | |
|
|
| 1.1436 | 12500 | 0.1583 | |
|
|
| 1.1528 | 12600 | 0.1554 | |
|
|
| 1.1619 | 12700 | 0.1453 | |
|
|
| 1.1711 | 12800 | 0.1592 | |
|
|
| 1.1802 | 12900 | 0.1508 | |
|
|
| 1.1894 | 13000 | 0.1585 | |
|
|
| 1.1985 | 13100 | 0.1381 | |
|
|
| 1.2077 | 13200 | 0.1442 | |
|
|
| 1.2168 | 13300 | 0.183 | |
|
|
| 1.2260 | 13400 | 0.1704 | |
|
|
| 1.2351 | 13500 | 0.152 | |
|
|
| 1.2443 | 13600 | 0.136 | |
|
|
| 1.2534 | 13700 | 0.1596 | |
|
|
| 1.2626 | 13800 | 0.151 | |
|
|
| 1.2717 | 13900 | 0.1597 | |
|
|
| 1.2809 | 14000 | 0.1547 | |
|
|
| 1.2900 | 14100 | 0.1717 | |
|
|
| 1.2992 | 14200 | 0.1037 | |
|
|
| 1.3083 | 14300 | 0.1452 | |
|
|
| 1.3175 | 14400 | 0.155 | |
|
|
| 1.3266 | 14500 | 0.189 | |
|
|
| 1.3358 | 14600 | 0.1384 | |
|
|
| 1.3449 | 14700 | 0.1711 | |
|
|
| 1.3541 | 14800 | 0.1255 | |
|
|
| 1.3632 | 14900 | 0.1439 | |
|
|
| 1.3724 | 15000 | 0.1583 | |
|
|
| 1.3815 | 15100 | 0.1586 | |
|
|
| 1.3907 | 15200 | 0.1502 | |
|
|
| 1.3998 | 15300 | 0.1199 | |
|
|
| 1.4090 | 15400 | 0.1362 | |
|
|
| 1.4181 | 15500 | 0.1502 | |
|
|
| 1.4273 | 15600 | 0.191 | |
|
|
| 1.4364 | 15700 | 0.1495 | |
|
|
| 1.4456 | 15800 | 0.1313 | |
|
|
| 1.4547 | 15900 | 0.1429 | |
|
|
| 1.4639 | 16000 | 0.1004 | |
|
|
| 1.4730 | 16100 | 0.1267 | |
|
|
| 1.4822 | 16200 | 0.1382 | |
|
|
| 1.4913 | 16300 | 0.1535 | |
|
|
| 1.5005 | 16400 | 0.1328 | |
|
|
| 1.5096 | 16500 | 0.1268 | |
|
|
| 1.5188 | 16600 | 0.1819 | |
|
|
| 1.5279 | 16700 | 0.133 | |
|
|
| 1.5371 | 16800 | 0.1503 | |
|
|
| 1.5462 | 16900 | 0.1217 | |
|
|
| 1.5554 | 17000 | 0.1414 | |
|
|
| 1.5645 | 17100 | 0.1413 | |
|
|
| 1.5737 | 17200 | 0.124 | |
|
|
| 1.5828 | 17300 | 0.1111 | |
|
|
| 1.5919 | 17400 | 0.1641 | |
|
|
| 1.6011 | 17500 | 0.1217 | |
|
|
| 1.6102 | 17600 | 0.1148 | |
|
|
| 1.6194 | 17700 | 0.1452 | |
|
|
| 1.6285 | 17800 | 0.1245 | |
|
|
| 1.6377 | 17900 | 0.1184 | |
|
|
| 1.6468 | 18000 | 0.1333 | |
|
|
| 1.6560 | 18100 | 0.1421 | |
|
|
| 1.6651 | 18200 | 0.1243 | |
|
|
| 1.6743 | 18300 | 0.1173 | |
|
|
| 1.6834 | 18400 | 0.117 | |
|
|
| 1.6926 | 18500 | 0.1145 | |
|
|
| 1.7017 | 18600 | 0.1365 | |
|
|
| 1.7109 | 18700 | 0.1404 | |
|
|
| 1.7200 | 18800 | 0.1254 | |
|
|
| 1.7292 | 18900 | 0.1131 | |
|
|
| 1.7383 | 19000 | 0.1503 | |
|
|
| 1.7475 | 19100 | 0.1429 | |
|
|
| 1.7566 | 19200 | 0.1057 | |
|
|
| 1.7658 | 19300 | 0.1221 | |
|
|
| 1.7749 | 19400 | 0.1034 | |
|
|
| 1.7841 | 19500 | 0.1154 | |
|
|
| 1.7932 | 19600 | 0.1106 | |
|
|
| 1.8024 | 19700 | 0.1568 | |
|
|
| 1.8115 | 19800 | 0.1332 | |
|
|
| 1.8207 | 19900 | 0.1238 | |
|
|
| 1.8298 | 20000 | 0.1321 | |
|
|
| 1.8390 | 20100 | 0.1629 | |
|
|
| 1.8481 | 20200 | 0.135 | |
|
|
| 1.8573 | 20300 | 0.1097 | |
|
|
| 1.8664 | 20400 | 0.1233 | |
|
|
| 1.8756 | 20500 | 0.1198 | |
|
|
| 1.8847 | 20600 | 0.1151 | |
|
|
| 1.8939 | 20700 | 0.1206 | |
|
|
| 1.9030 | 20800 | 0.1295 | |
|
|
| 1.9122 | 20900 | 0.126 | |
|
|
| 1.9213 | 21000 | 0.147 | |
|
|
| 1.9305 | 21100 | 0.1316 | |
|
|
| 1.9396 | 21200 | 0.1019 | |
|
|
| 1.9488 | 21300 | 0.1328 | |
|
|
| 1.9579 | 21400 | 0.1127 | |
|
|
| 1.9671 | 21500 | 0.1416 | |
|
|
| 1.9762 | 21600 | 0.1428 | |
|
|
| 1.9854 | 21700 | 0.1481 | |
|
|
| 1.9945 | 21800 | 0.1169 | |
|
|
| 2.0037 | 21900 | 0.1005 | |
|
|
| 2.0128 | 22000 | 0.1114 | |
|
|
| 2.0220 | 22100 | 0.1301 | |
|
|
| 2.0311 | 22200 | 0.1554 | |
|
|
| 2.0403 | 22300 | 0.1623 | |
|
|
| 2.0494 | 22400 | 0.1153 | |
|
|
| 2.0586 | 22500 | 0.1152 | |
|
|
| 2.0677 | 22600 | 0.1406 | |
|
|
| 2.0769 | 22700 | 0.1196 | |
|
|
| 2.0860 | 22800 | 0.1172 | |
|
|
| 2.0952 | 22900 | 0.1153 | |
|
|
| 2.1043 | 23000 | 0.1126 | |
|
|
| 2.1134 | 23100 | 0.1157 | |
|
|
| 2.1226 | 23200 | 0.1102 | |
|
|
| 2.1317 | 23300 | 0.1102 | |
|
|
| 2.1409 | 23400 | 0.1198 | |
|
|
| 2.1500 | 23500 | 0.1241 | |
|
|
| 2.1592 | 23600 | 0.1124 | |
|
|
| 2.1683 | 23700 | 0.1172 | |
|
|
| 2.1775 | 23800 | 0.1161 | |
|
|
| 2.1866 | 23900 | 0.1162 | |
|
|
| 2.1958 | 24000 | 0.1209 | |
|
|
| 2.2049 | 24100 | 0.1039 | |
|
|
| 2.2141 | 24200 | 0.1183 | |
|
|
| 2.2232 | 24300 | 0.1155 | |
|
|
| 2.2324 | 24400 | 0.1168 | |
|
|
| 2.2415 | 24500 | 0.1116 | |
|
|
| 2.2507 | 24600 | 0.1173 | |
|
|
| 2.2598 | 24700 | 0.1321 | |
|
|
| 2.2690 | 24800 | 0.1217 | |
|
|
| 2.2781 | 24900 | 0.1153 | |
|
|
| 2.2873 | 25000 | 0.1464 | |
|
|
| 2.2964 | 25100 | 0.101 | |
|
|
| 2.3056 | 25200 | 0.1042 | |
|
|
| 2.3147 | 25300 | 0.1382 | |
|
|
| 2.3239 | 25400 | 0.1489 | |
|
|
| 2.3330 | 25500 | 0.1187 | |
|
|
| 2.3422 | 25600 | 0.1184 | |
|
|
| 2.3513 | 25700 | 0.0971 | |
|
|
| 2.3605 | 25800 | 0.0986 | |
|
|
| 2.3696 | 25900 | 0.1114 | |
|
|
| 2.3788 | 26000 | 0.1175 | |
|
|
| 2.3879 | 26100 | 0.1136 | |
|
|
| 2.3971 | 26200 | 0.1251 | |
|
|
| 2.4062 | 26300 | 0.1097 | |
|
|
| 2.4154 | 26400 | 0.1123 | |
|
|
| 2.4245 | 26500 | 0.1446 | |
|
|
| 2.4337 | 26600 | 0.1282 | |
|
|
| 2.4428 | 26700 | 0.0988 | |
|
|
| 2.4520 | 26800 | 0.1172 | |
|
|
| 2.4611 | 26900 | 0.0903 | |
|
|
| 2.4703 | 27000 | 0.1049 | |
|
|
| 2.4794 | 27100 | 0.1043 | |
|
|
| 2.4886 | 27200 | 0.1081 | |
|
|
| 2.4977 | 27300 | 0.1265 | |
|
|
| 2.5069 | 27400 | 0.1131 | |
|
|
| 2.5160 | 27500 | 0.1403 | |
|
|
| 2.5252 | 27600 | 0.1033 | |
|
|
| 2.5343 | 27700 | 0.1175 | |
|
|
| 2.5435 | 27800 | 0.1247 | |
|
|
| 2.5526 | 27900 | 0.1115 | |
|
|
| 2.5618 | 28000 | 0.1173 | |
|
|
| 2.5709 | 28100 | 0.1209 | |
|
|
| 2.5801 | 28200 | 0.0894 | |
|
|
| 2.5892 | 28300 | 0.1238 | |
|
|
| 2.5984 | 28400 | 0.1011 | |
|
|
| 2.6075 | 28500 | 0.0976 | |
|
|
| 2.6167 | 28600 | 0.0968 | |
|
|
| 2.6258 | 28700 | 0.1065 | |
|
|
| 2.6349 | 28800 | 0.1011 | |
|
|
| 2.6441 | 28900 | 0.0975 | |
|
|
| 2.6532 | 29000 | 0.1291 | |
|
|
| 2.6624 | 29100 | 0.1118 | |
|
|
| 2.6715 | 29200 | 0.0983 | |
|
|
| 2.6807 | 29300 | 0.1119 | |
|
|
| 2.6898 | 29400 | 0.0728 | |
|
|
| 2.6990 | 29500 | 0.1241 | |
|
|
| 2.7081 | 29600 | 0.1045 | |
|
|
| 2.7173 | 29700 | 0.1186 | |
|
|
| 2.7264 | 29800 | 0.1037 | |
|
|
| 2.7356 | 29900 | 0.129 | |
|
|
| 2.7447 | 30000 | 0.0921 | |
|
|
| 2.7539 | 30100 | 0.1006 | |
|
|
| 2.7630 | 30200 | 0.1068 | |
|
|
| 2.7722 | 30300 | 0.099 | |
|
|
| 2.7813 | 30400 | 0.0949 | |
|
|
| 2.7905 | 30500 | 0.1066 | |
|
|
| 2.7996 | 30600 | 0.1025 | |
|
|
| 2.8088 | 30700 | 0.1148 | |
|
|
| 2.8179 | 30800 | 0.1164 | |
|
|
| 2.8271 | 30900 | 0.1147 | |
|
|
| 2.8362 | 31000 | 0.1298 | |
|
|
| 2.8454 | 31100 | 0.1245 | |
|
|
| 2.8545 | 31200 | 0.087 | |
|
|
| 2.8637 | 31300 | 0.1115 | |
|
|
| 2.8728 | 31400 | 0.1129 | |
|
|
| 2.8820 | 31500 | 0.1121 | |
|
|
| 2.8911 | 31600 | 0.0985 | |
|
|
| 2.9003 | 31700 | 0.1094 | |
|
|
| 2.9094 | 31800 | 0.1296 | |
|
|
| 2.9186 | 31900 | 0.1149 | |
|
|
| 2.9277 | 32000 | 0.1146 | |
|
|
| 2.9369 | 32100 | 0.1147 | |
|
|
| 2.9460 | 32200 | 0.1045 | |
|
|
| 2.9552 | 32300 | 0.0962 | |
|
|
| 2.9643 | 32400 | 0.1065 | |
|
|
| 2.9735 | 32500 | 0.1169 | |
|
|
| 2.9826 | 32600 | 0.1162 | |
|
|
| 2.9918 | 32700 | 0.1134 | |
|
|
|
|
|
</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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