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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: intfloat/multilingual-e5-small |
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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 intfloat/multilingual-e5-small |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 --> |
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- **Maximum Sequence Length:** 512 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': 512, '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/eFuck") |
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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: 6 tokens</li><li>mean: 16.81 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.59 tokens</li><li>max: 59 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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- `per_device_train_batch_size`: 32 |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.005 |
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- `num_train_epochs`: 10 |
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- `warmup_ratio`: 0.1 |
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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`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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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`: 0.005 |
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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`: 10 |
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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`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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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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| 0.0244 | 100 | 1.3984 | |
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| 0.0488 | 200 | 0.8762 | |
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| 0.0732 | 300 | 0.2492 | |
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| 0.0976 | 400 | 0.0754 | |
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| 0.1220 | 500 | 0.0809 | |
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| 0.1464 | 600 | 0.0789 | |
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| 0.1708 | 700 | 0.076 | |
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| 0.1952 | 800 | 0.0642 | |
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| 0.2196 | 900 | 0.0743 | |
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| 0.2440 | 1000 | 0.0605 | |
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| 0.2684 | 1100 | 0.0705 | |
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| 0.2928 | 1200 | 0.0594 | |
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| 0.3172 | 1300 | 0.0565 | |
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| 0.3415 | 1400 | 0.071 | |
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| 0.3659 | 1500 | 0.0476 | |
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| 0.3903 | 1600 | 0.0514 | |
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| 0.4147 | 1700 | 0.0584 | |
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| 0.4391 | 1800 | 0.0649 | |
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| 0.4635 | 1900 | 0.0485 | |
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| 0.4879 | 2000 | 0.0556 | |
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| 0.5123 | 2100 | 0.0594 | |
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| 0.5367 | 2200 | 0.0556 | |
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| 0.5611 | 2300 | 0.0439 | |
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| 0.5855 | 2400 | 0.0619 | |
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| 0.6099 | 2500 | 0.0553 | |
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| 0.6343 | 2600 | 0.0393 | |
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| 0.6587 | 2700 | 0.0458 | |
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| 0.6831 | 2800 | 0.0476 | |
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| 0.7075 | 2900 | 0.0535 | |
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| 0.7319 | 3000 | 0.0439 | |
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| 0.7563 | 3100 | 0.0438 | |
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| 0.7807 | 3200 | 0.052 | |
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| 0.8051 | 3300 | 0.0514 | |
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| 0.8295 | 3400 | 0.0549 | |
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| 0.8539 | 3500 | 0.0439 | |
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| 0.8783 | 3600 | 0.0429 | |
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| 0.9027 | 3700 | 0.0442 | |
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| 0.9271 | 3800 | 0.0643 | |
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| 0.9515 | 3900 | 0.0408 | |
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| 0.9758 | 4000 | 0.0403 | |
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| 1.0002 | 4100 | 0.0446 | |
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| 1.0246 | 4200 | 0.0527 | |
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| 1.0490 | 4300 | 0.0545 | |
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| 1.0734 | 4400 | 0.0517 | |
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| 1.0978 | 4500 | 0.0299 | |
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| 1.1222 | 4600 | 0.0444 | |
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| 1.1466 | 4700 | 0.0475 | |
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| 1.1710 | 4800 | 0.0414 | |
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| 1.1954 | 4900 | 0.0386 | |
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| 1.2198 | 5000 | 0.0508 | |
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| 1.2442 | 5100 | 0.0384 | |
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| 1.2686 | 5200 | 0.0453 | |
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| 1.2930 | 5300 | 0.0401 | |
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| 1.3174 | 5400 | 0.0328 | |
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| 1.3418 | 5500 | 0.0456 | |
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| 1.3662 | 5600 | 0.0295 | |
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| 1.3906 | 5700 | 0.0366 | |
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| 1.4150 | 5800 | 0.0431 | |
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| 1.4394 | 5900 | 0.0442 | |
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| 1.4638 | 6000 | 0.0343 | |
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| 1.4882 | 6100 | 0.0405 | |
|
|
| 1.5126 | 6200 | 0.0357 | |
|
|
| 1.5370 | 6300 | 0.0423 | |
|
|
| 1.5614 | 6400 | 0.0288 | |
|
|
| 1.5858 | 6500 | 0.0393 | |
|
|
| 1.6101 | 6600 | 0.0369 | |
|
|
| 1.6345 | 6700 | 0.0245 | |
|
|
| 1.6589 | 6800 | 0.0286 | |
|
|
| 1.6833 | 6900 | 0.0325 | |
|
|
| 1.7077 | 7000 | 0.0311 | |
|
|
| 1.7321 | 7100 | 0.0272 | |
|
|
| 1.7565 | 7200 | 0.0261 | |
|
|
| 1.7809 | 7300 | 0.0296 | |
|
|
| 1.8053 | 7400 | 0.0343 | |
|
|
| 1.8297 | 7500 | 0.036 | |
|
|
| 1.8541 | 7600 | 0.0225 | |
|
|
| 1.8785 | 7700 | 0.0232 | |
|
|
| 1.9029 | 7800 | 0.0275 | |
|
|
| 1.9273 | 7900 | 0.0394 | |
|
|
| 1.9517 | 8000 | 0.0297 | |
|
|
| 1.9761 | 8100 | 0.0249 | |
|
|
| 2.0005 | 8200 | 0.0268 | |
|
|
| 2.0249 | 8300 | 0.0269 | |
|
|
| 2.0493 | 8400 | 0.0296 | |
|
|
| 2.0737 | 8500 | 0.0326 | |
|
|
| 2.0981 | 8600 | 0.0183 | |
|
|
| 2.1225 | 8700 | 0.024 | |
|
|
| 2.1469 | 8800 | 0.0298 | |
|
|
| 2.1713 | 8900 | 0.0273 | |
|
|
| 2.1957 | 9000 | 0.0244 | |
|
|
| 2.2201 | 9100 | 0.0308 | |
|
|
| 2.2444 | 9200 | 0.0247 | |
|
|
| 2.2688 | 9300 | 0.0299 | |
|
|
| 2.2932 | 9400 | 0.0222 | |
|
|
| 2.3176 | 9500 | 0.0213 | |
|
|
| 2.3420 | 9600 | 0.0316 | |
|
|
| 2.3664 | 9700 | 0.0157 | |
|
|
| 2.3908 | 9800 | 0.0248 | |
|
|
| 2.4152 | 9900 | 0.028 | |
|
|
| 2.4396 | 10000 | 0.0269 | |
|
|
| 2.4640 | 10100 | 0.0214 | |
|
|
| 2.4884 | 10200 | 0.0242 | |
|
|
| 2.5128 | 10300 | 0.0222 | |
|
|
| 2.5372 | 10400 | 0.0253 | |
|
|
| 2.5616 | 10500 | 0.0175 | |
|
|
| 2.5860 | 10600 | 0.0269 | |
|
|
| 2.6104 | 10700 | 0.0281 | |
|
|
| 2.6348 | 10800 | 0.014 | |
|
|
| 2.6592 | 10900 | 0.0187 | |
|
|
| 2.6836 | 11000 | 0.0204 | |
|
|
| 2.7080 | 11100 | 0.0228 | |
|
|
| 2.7324 | 11200 | 0.0193 | |
|
|
| 2.7568 | 11300 | 0.014 | |
|
|
| 2.7812 | 11400 | 0.0171 | |
|
|
| 2.8056 | 11500 | 0.0213 | |
|
|
| 2.8300 | 11600 | 0.025 | |
|
|
| 2.8544 | 11700 | 0.0138 | |
|
|
| 2.8788 | 11800 | 0.0133 | |
|
|
| 2.9031 | 11900 | 0.021 | |
|
|
| 2.9275 | 12000 | 0.0256 | |
|
|
| 2.9519 | 12100 | 0.019 | |
|
|
| 2.9763 | 12200 | 0.0149 | |
|
|
| 3.0007 | 12300 | 0.0192 | |
|
|
| 3.0251 | 12400 | 0.0194 | |
|
|
| 3.0495 | 12500 | 0.0179 | |
|
|
| 3.0739 | 12600 | 0.0218 | |
|
|
| 3.0983 | 12700 | 0.0126 | |
|
|
| 3.1227 | 12800 | 0.018 | |
|
|
| 3.1471 | 12900 | 0.0188 | |
|
|
| 3.1715 | 13000 | 0.0181 | |
|
|
| 3.1959 | 13100 | 0.0186 | |
|
|
| 3.2203 | 13200 | 0.0235 | |
|
|
| 3.2447 | 13300 | 0.0172 | |
|
|
| 3.2691 | 13400 | 0.0183 | |
|
|
| 3.2935 | 13500 | 0.0155 | |
|
|
| 3.3179 | 13600 | 0.0135 | |
|
|
| 3.3423 | 13700 | 0.0236 | |
|
|
| 3.3667 | 13800 | 0.0115 | |
|
|
| 3.3911 | 13900 | 0.0162 | |
|
|
| 3.4155 | 14000 | 0.0207 | |
|
|
| 3.4399 | 14100 | 0.0174 | |
|
|
| 3.4643 | 14200 | 0.0128 | |
|
|
| 3.4887 | 14300 | 0.0202 | |
|
|
| 3.5131 | 14400 | 0.0165 | |
|
|
| 3.5374 | 14500 | 0.0162 | |
|
|
| 3.5618 | 14600 | 0.015 | |
|
|
| 3.5862 | 14700 | 0.0203 | |
|
|
| 3.6106 | 14800 | 0.0222 | |
|
|
| 3.6350 | 14900 | 0.0105 | |
|
|
| 3.6594 | 15000 | 0.014 | |
|
|
| 3.6838 | 15100 | 0.0146 | |
|
|
| 3.7082 | 15200 | 0.015 | |
|
|
| 3.7326 | 15300 | 0.0153 | |
|
|
| 3.7570 | 15400 | 0.0099 | |
|
|
| 3.7814 | 15500 | 0.0105 | |
|
|
| 3.8058 | 15600 | 0.0168 | |
|
|
| 3.8302 | 15700 | 0.0185 | |
|
|
| 3.8546 | 15800 | 0.0104 | |
|
|
| 3.8790 | 15900 | 0.01 | |
|
|
| 3.9034 | 16000 | 0.0142 | |
|
|
| 3.9278 | 16100 | 0.0197 | |
|
|
| 3.9522 | 16200 | 0.013 | |
|
|
| 3.9766 | 16300 | 0.0137 | |
|
|
| 4.0010 | 16400 | 0.0133 | |
|
|
| 4.0254 | 16500 | 0.0132 | |
|
|
| 4.0498 | 16600 | 0.0124 | |
|
|
| 4.0742 | 16700 | 0.0141 | |
|
|
| 4.0986 | 16800 | 0.0099 | |
|
|
| 4.1230 | 16900 | 0.0113 | |
|
|
| 4.1474 | 17000 | 0.0149 | |
|
|
| 4.1717 | 17100 | 0.0145 | |
|
|
| 4.1961 | 17200 | 0.0129 | |
|
|
| 4.2205 | 17300 | 0.0185 | |
|
|
| 4.2449 | 17400 | 0.0138 | |
|
|
| 4.2693 | 17500 | 0.0133 | |
|
|
| 4.2937 | 17600 | 0.0107 | |
|
|
| 4.3181 | 17700 | 0.0092 | |
|
|
| 4.3425 | 17800 | 0.0175 | |
|
|
| 4.3669 | 17900 | 0.0097 | |
|
|
| 4.3913 | 18000 | 0.0111 | |
|
|
| 4.4157 | 18100 | 0.0136 | |
|
|
| 4.4401 | 18200 | 0.0122 | |
|
|
| 4.4645 | 18300 | 0.0095 | |
|
|
| 4.4889 | 18400 | 0.0141 | |
|
|
| 4.5133 | 18500 | 0.0094 | |
|
|
| 4.5377 | 18600 | 0.0123 | |
|
|
| 4.5621 | 18700 | 0.0108 | |
|
|
| 4.5865 | 18800 | 0.0145 | |
|
|
| 4.6109 | 18900 | 0.0195 | |
|
|
| 4.6353 | 19000 | 0.0099 | |
|
|
| 4.6597 | 19100 | 0.0107 | |
|
|
| 4.6841 | 19200 | 0.0105 | |
|
|
| 4.7085 | 19300 | 0.0124 | |
|
|
| 4.7329 | 19400 | 0.012 | |
|
|
| 4.7573 | 19500 | 0.0081 | |
|
|
| 4.7817 | 19600 | 0.0081 | |
|
|
| 4.8061 | 19700 | 0.0111 | |
|
|
| 4.8304 | 19800 | 0.0141 | |
|
|
| 4.8548 | 19900 | 0.0073 | |
|
|
| 4.8792 | 20000 | 0.0094 | |
|
|
| 4.9036 | 20100 | 0.011 | |
|
|
| 4.9280 | 20200 | 0.0157 | |
|
|
| 4.9524 | 20300 | 0.0086 | |
|
|
| 4.9768 | 20400 | 0.0093 | |
|
|
| 5.0012 | 20500 | 0.011 | |
|
|
| 5.0256 | 20600 | 0.0107 | |
|
|
| 5.0500 | 20700 | 0.0094 | |
|
|
| 5.0744 | 20800 | 0.008 | |
|
|
| 5.0988 | 20900 | 0.0076 | |
|
|
| 5.1232 | 21000 | 0.0088 | |
|
|
| 5.1476 | 21100 | 0.0119 | |
|
|
| 5.1720 | 21200 | 0.0118 | |
|
|
| 5.1964 | 21300 | 0.0105 | |
|
|
| 5.2208 | 21400 | 0.0138 | |
|
|
| 5.2452 | 21500 | 0.0109 | |
|
|
| 5.2696 | 21600 | 0.0101 | |
|
|
| 5.2940 | 21700 | 0.008 | |
|
|
| 5.3184 | 21800 | 0.0068 | |
|
|
| 5.3428 | 21900 | 0.0123 | |
|
|
| 5.3672 | 22000 | 0.0086 | |
|
|
| 5.3916 | 22100 | 0.0084 | |
|
|
| 5.4160 | 22200 | 0.0113 | |
|
|
| 5.4404 | 22300 | 0.0086 | |
|
|
| 5.4647 | 22400 | 0.0076 | |
|
|
| 5.4891 | 22500 | 0.0101 | |
|
|
| 5.5135 | 22600 | 0.0083 | |
|
|
| 5.5379 | 22700 | 0.0116 | |
|
|
| 5.5623 | 22800 | 0.0083 | |
|
|
| 5.5867 | 22900 | 0.0137 | |
|
|
| 5.6111 | 23000 | 0.0144 | |
|
|
| 5.6355 | 23100 | 0.0081 | |
|
|
| 5.6599 | 23200 | 0.006 | |
|
|
| 5.6843 | 23300 | 0.0096 | |
|
|
| 5.7087 | 23400 | 0.0098 | |
|
|
| 5.7331 | 23500 | 0.0096 | |
|
|
| 5.7575 | 23600 | 0.0063 | |
|
|
| 5.7819 | 23700 | 0.0052 | |
|
|
| 5.8063 | 23800 | 0.008 | |
|
|
| 5.8307 | 23900 | 0.0117 | |
|
|
| 5.8551 | 24000 | 0.0053 | |
|
|
| 5.8795 | 24100 | 0.0077 | |
|
|
| 5.9039 | 24200 | 0.0086 | |
|
|
| 5.9283 | 24300 | 0.0129 | |
|
|
| 5.9527 | 24400 | 0.0085 | |
|
|
| 5.9771 | 24500 | 0.0064 | |
|
|
| 6.0015 | 24600 | 0.0092 | |
|
|
| 6.0259 | 24700 | 0.0076 | |
|
|
| 6.0503 | 24800 | 0.0078 | |
|
|
| 6.0747 | 24900 | 0.0074 | |
|
|
| 6.0990 | 25000 | 0.0064 | |
|
|
| 6.1234 | 25100 | 0.0067 | |
|
|
| 6.1478 | 25200 | 0.0091 | |
|
|
| 6.1722 | 25300 | 0.0087 | |
|
|
| 6.1966 | 25400 | 0.0076 | |
|
|
| 6.2210 | 25500 | 0.0104 | |
|
|
| 6.2454 | 25600 | 0.0077 | |
|
|
| 6.2698 | 25700 | 0.0074 | |
|
|
| 6.2942 | 25800 | 0.0055 | |
|
|
| 6.3186 | 25900 | 0.0059 | |
|
|
| 6.3430 | 26000 | 0.0092 | |
|
|
| 6.3674 | 26100 | 0.0051 | |
|
|
| 6.3918 | 26200 | 0.0075 | |
|
|
| 6.4162 | 26300 | 0.0093 | |
|
|
| 6.4406 | 26400 | 0.0073 | |
|
|
| 6.4650 | 26500 | 0.0051 | |
|
|
| 6.4894 | 26600 | 0.0093 | |
|
|
| 6.5138 | 26700 | 0.0065 | |
|
|
| 6.5382 | 26800 | 0.0072 | |
|
|
| 6.5626 | 26900 | 0.0075 | |
|
|
| 6.5870 | 27000 | 0.0111 | |
|
|
| 6.6114 | 27100 | 0.0139 | |
|
|
| 6.6358 | 27200 | 0.0066 | |
|
|
| 6.6602 | 27300 | 0.0062 | |
|
|
| 6.6846 | 27400 | 0.0078 | |
|
|
| 6.7090 | 27500 | 0.0084 | |
|
|
| 6.7333 | 27600 | 0.0077 | |
|
|
| 6.7577 | 27700 | 0.0055 | |
|
|
| 6.7821 | 27800 | 0.0039 | |
|
|
| 6.8065 | 27900 | 0.0082 | |
|
|
| 6.8309 | 28000 | 0.0101 | |
|
|
| 6.8553 | 28100 | 0.0041 | |
|
|
| 6.8797 | 28200 | 0.0058 | |
|
|
| 6.9041 | 28300 | 0.0058 | |
|
|
| 6.9285 | 28400 | 0.0109 | |
|
|
| 6.9529 | 28500 | 0.0054 | |
|
|
| 6.9773 | 28600 | 0.0061 | |
|
|
| 7.0017 | 28700 | 0.0078 | |
|
|
| 7.0261 | 28800 | 0.0065 | |
|
|
| 7.0505 | 28900 | 0.0061 | |
|
|
| 7.0749 | 29000 | 0.0049 | |
|
|
| 7.0993 | 29100 | 0.0062 | |
|
|
| 7.1237 | 29200 | 0.0052 | |
|
|
| 7.1481 | 29300 | 0.0073 | |
|
|
| 7.1725 | 29400 | 0.0072 | |
|
|
| 7.1969 | 29500 | 0.0067 | |
|
|
| 7.2213 | 29600 | 0.0093 | |
|
|
| 7.2457 | 29700 | 0.008 | |
|
|
| 7.2701 | 29800 | 0.0057 | |
|
|
| 7.2945 | 29900 | 0.0051 | |
|
|
| 7.3189 | 30000 | 0.0046 | |
|
|
| 7.3433 | 30100 | 0.0078 | |
|
|
| 7.3677 | 30200 | 0.0041 | |
|
|
| 7.3920 | 30300 | 0.0054 | |
|
|
| 7.4164 | 30400 | 0.008 | |
|
|
| 7.4408 | 30500 | 0.0056 | |
|
|
| 7.4652 | 30600 | 0.0037 | |
|
|
| 7.4896 | 30700 | 0.0071 | |
|
|
| 7.5140 | 30800 | 0.0058 | |
|
|
| 7.5384 | 30900 | 0.0074 | |
|
|
| 7.5628 | 31000 | 0.0059 | |
|
|
| 7.5872 | 31100 | 0.0088 | |
|
|
| 7.6116 | 31200 | 0.0102 | |
|
|
| 7.6360 | 31300 | 0.0058 | |
|
|
| 7.6604 | 31400 | 0.0044 | |
|
|
| 7.6848 | 31500 | 0.0065 | |
|
|
| 7.7092 | 31600 | 0.007 | |
|
|
| 7.7336 | 31700 | 0.0078 | |
|
|
| 7.7580 | 31800 | 0.0048 | |
|
|
| 7.7824 | 31900 | 0.0033 | |
|
|
| 7.8068 | 32000 | 0.0063 | |
|
|
| 7.8312 | 32100 | 0.008 | |
|
|
| 7.8556 | 32200 | 0.004 | |
|
|
| 7.8800 | 32300 | 0.0057 | |
|
|
| 7.9044 | 32400 | 0.005 | |
|
|
| 7.9288 | 32500 | 0.0095 | |
|
|
| 7.9532 | 32600 | 0.0042 | |
|
|
| 7.9776 | 32700 | 0.0058 | |
|
|
| 8.0020 | 32800 | 0.006 | |
|
|
| 8.0263 | 32900 | 0.006 | |
|
|
| 8.0507 | 33000 | 0.0054 | |
|
|
| 8.0751 | 33100 | 0.0041 | |
|
|
| 8.0995 | 33200 | 0.0045 | |
|
|
| 8.1239 | 33300 | 0.0052 | |
|
|
| 8.1483 | 33400 | 0.0067 | |
|
|
| 8.1727 | 33500 | 0.008 | |
|
|
| 8.1971 | 33600 | 0.0047 | |
|
|
| 8.2215 | 33700 | 0.0079 | |
|
|
| 8.2459 | 33800 | 0.0071 | |
|
|
| 8.2703 | 33900 | 0.0043 | |
|
|
| 8.2947 | 34000 | 0.0041 | |
|
|
| 8.3191 | 34100 | 0.0035 | |
|
|
| 8.3435 | 34200 | 0.0059 | |
|
|
| 8.3679 | 34300 | 0.004 | |
|
|
| 8.3923 | 34400 | 0.005 | |
|
|
| 8.4167 | 34500 | 0.0067 | |
|
|
| 8.4411 | 34600 | 0.0049 | |
|
|
| 8.4655 | 34700 | 0.0034 | |
|
|
| 8.4899 | 34800 | 0.0057 | |
|
|
| 8.5143 | 34900 | 0.0052 | |
|
|
| 8.5387 | 35000 | 0.005 | |
|
|
| 8.5631 | 35100 | 0.0047 | |
|
|
| 8.5875 | 35200 | 0.0089 | |
|
|
| 8.6119 | 35300 | 0.0066 | |
|
|
| 8.6363 | 35400 | 0.0044 | |
|
|
| 8.6606 | 35500 | 0.0037 | |
|
|
| 8.6850 | 35600 | 0.0059 | |
|
|
| 8.7094 | 35700 | 0.0069 | |
|
|
| 8.7338 | 35800 | 0.0069 | |
|
|
| 8.7582 | 35900 | 0.0038 | |
|
|
| 8.7826 | 36000 | 0.0028 | |
|
|
| 8.8070 | 36100 | 0.0047 | |
|
|
| 8.8314 | 36200 | 0.007 | |
|
|
| 8.8558 | 36300 | 0.0036 | |
|
|
| 8.8802 | 36400 | 0.0049 | |
|
|
| 8.9046 | 36500 | 0.0041 | |
|
|
| 8.9290 | 36600 | 0.0085 | |
|
|
| 8.9534 | 36700 | 0.004 | |
|
|
| 8.9778 | 36800 | 0.0044 | |
|
|
| 9.0022 | 36900 | 0.0053 | |
|
|
| 9.0266 | 37000 | 0.006 | |
|
|
| 9.0510 | 37100 | 0.0051 | |
|
|
| 9.0754 | 37200 | 0.0029 | |
|
|
| 9.0998 | 37300 | 0.0041 | |
|
|
| 9.1242 | 37400 | 0.0046 | |
|
|
| 9.1486 | 37500 | 0.0057 | |
|
|
| 9.1730 | 37600 | 0.0063 | |
|
|
| 9.1974 | 37700 | 0.0048 | |
|
|
| 9.2218 | 37800 | 0.0077 | |
|
|
| 9.2462 | 37900 | 0.0056 | |
|
|
| 9.2706 | 38000 | 0.0039 | |
|
|
| 9.2949 | 38100 | 0.0036 | |
|
|
| 9.3193 | 38200 | 0.0032 | |
|
|
| 9.3437 | 38300 | 0.0055 | |
|
|
| 9.3681 | 38400 | 0.0037 | |
|
|
| 9.3925 | 38500 | 0.0045 | |
|
|
| 9.4169 | 38600 | 0.0065 | |
|
|
| 9.4413 | 38700 | 0.0047 | |
|
|
| 9.4657 | 38800 | 0.0033 | |
|
|
| 9.4901 | 38900 | 0.0052 | |
|
|
| 9.5145 | 39000 | 0.0043 | |
|
|
| 9.5389 | 39100 | 0.0043 | |
|
|
| 9.5633 | 39200 | 0.0049 | |
|
|
| 9.5877 | 39300 | 0.0074 | |
|
|
| 9.6121 | 39400 | 0.0054 | |
|
|
| 9.6365 | 39500 | 0.004 | |
|
|
| 9.6609 | 39600 | 0.0031 | |
|
|
| 9.6853 | 39700 | 0.0054 | |
|
|
| 9.7097 | 39800 | 0.0061 | |
|
|
| 9.7341 | 39900 | 0.0055 | |
|
|
| 9.7585 | 40000 | 0.0033 | |
|
|
| 9.7829 | 40100 | 0.0028 | |
|
|
| 9.8073 | 40200 | 0.0046 | |
|
|
| 9.8317 | 40300 | 0.0062 | |
|
|
| 9.8561 | 40400 | 0.0033 | |
|
|
| 9.8805 | 40500 | 0.0047 | |
|
|
| 9.9049 | 40600 | 0.0045 | |
|
|
| 9.9293 | 40700 | 0.0075 | |
|
|
| 9.9536 | 40800 | 0.0035 | |
|
|
| 9.9780 | 40900 | 0.0038 | |
|
|
|
|
|
</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: 4.0.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
|
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year = "2019", |
|
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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|
eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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|
} |
|
|
``` |
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|
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