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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:17702 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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widget: |
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- source_sentence: zonivan 100 mg 20 caps. |
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sentences: |
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- fuci-top 2% cream 15 gm |
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- فاستاتينال 10/40مجم 7 قرص |
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- زونيفان 100 مجم 20 كبسولة |
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- source_sentence: celeborg 100 mg 10 cap |
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sentences: |
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- celeborg 100mg 10 caps. |
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- هيدرو زد كريم 25 جم |
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- كومبليت زيت للشعر 60 مل |
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- source_sentence: jedcoflacin 200 mg 20 f.c.tab. |
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sentences: |
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- فوتوديرم ماكس حليب للجسم 100 اس بي اف 100 مل |
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- zocozet 10/20mg 14 f.c. tab. |
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- جيدكوفلاسين 200 مجم 20 قرص |
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- source_sentence: isolift cream 30ml |
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sentences: |
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- ايزوليفت كريم 30 مل |
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- linezolid 600mg 7 f.c. tab. |
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- تينسريليف 400 مجم 28 قرص |
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- source_sentence: stress formula 20 capsules |
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sentences: |
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- كورتيكوفيوسيديك كريم موضعي 30 جم |
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- ستريس فورميولا 20 كبسول |
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- silden 25 mg 10 f.c.tab. |
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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/paraphrase-multilingual-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d --> |
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- **Maximum Sequence Length:** 128 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': 128, '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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) |
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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("mohsayed/para_tr_enar_1") |
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# Run inference |
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sentences = [ |
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'stress formula 20 capsules', |
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'ستريس فورميولا 20 كبسول', |
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'كورتيكوفيوسيديك كريم موضعي 30 جم', |
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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: 17,702 training samples |
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* Columns: <code>sentence1</code> and <code>sentence2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 10.29 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.42 tokens</li><li>max: 25 tokens</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | |
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|:----------------------------------------------------------|:--------------------------------------------------------| |
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| <code>azelast plus 125 / 50 mcg nasal spray 25 ml</code> | <code>azelast plus 125/50 mcg nasal spray 25 ml</code> | |
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| <code>ticanase plus 125 / 50 mcg nasal spray 15 ml</code> | <code>ticanase plus 125/50 mcg nasal spray 15 ml</code> | |
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| <code>nasostop 0.1% adult nasal drops 15 ml</code> | <code>nasostop 0.1% adult nasal drops 15 ml</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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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 1,771 evaluation samples |
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* Columns: <code>sentence1</code> and <code>sentence2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.44 tokens</li><li>max: 26 tokens</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | |
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|:----------------------------------------------------------|:---------------------------------------------| |
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| <code>calcibella fortified liquid chocolate 200 gm</code> | <code>كالسيبيلا شيكولاته سائلة 200 جم</code> | |
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| <code>glaryl 4 mg 30 tab</code> | <code>glaryl 4mg 30 tab.</code> | |
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| <code>pixefresh mouth spray 60 ml</code> | <code>بيكسيفريش بخاخ للفم 60 مل</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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 15 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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-05 |
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- `weight_decay`: 0.0 |
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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`: 1.0 |
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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`: True |
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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`: True |
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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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- `tp_size`: 0 |
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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`: batch_sampler |
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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 | Validation Loss | |
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|:-----------:|:---------:|:-------------:|:---------------:| |
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| 0.0903 | 100 | 1.123 | - | |
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| 0.1807 | 200 | 0.2605 | - | |
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| 0.2710 | 300 | 0.1432 | - | |
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| 0.3613 | 400 | 0.1151 | - | |
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| 0.4517 | 500 | 0.09 | - | |
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| 0.5420 | 600 | 0.0666 | - | |
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| 0.6323 | 700 | 0.0534 | - | |
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| 0.7227 | 800 | 0.0593 | - | |
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| 0.8130 | 900 | 0.0484 | - | |
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| 0.9033 | 1000 | 0.0652 | 0.0302 | |
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| 0.9937 | 1100 | 0.0441 | - | |
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| 1.0840 | 1200 | 0.0333 | - | |
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| 1.1743 | 1300 | 0.0395 | - | |
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| 1.2647 | 1400 | 0.0357 | - | |
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| 1.3550 | 1500 | 0.0351 | - | |
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| 1.4453 | 1600 | 0.0338 | - | |
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| 1.5357 | 1700 | 0.0365 | - | |
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| 1.6260 | 1800 | 0.0518 | - | |
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| 1.7164 | 1900 | 0.0426 | - | |
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| 1.8067 | 2000 | 0.0312 | 0.0234 | |
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| 1.8970 | 2100 | 0.041 | - | |
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| 1.9874 | 2200 | 0.0401 | - | |
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| 2.0777 | 2300 | 0.0177 | - | |
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| 2.1680 | 2400 | 0.0216 | - | |
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| 2.2584 | 2500 | 0.0203 | - | |
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| 2.3487 | 2600 | 0.0184 | - | |
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| 2.4390 | 2700 | 0.0203 | - | |
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| 2.5294 | 2800 | 0.024 | - | |
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| 2.6197 | 2900 | 0.0154 | - | |
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| 2.7100 | 3000 | 0.0292 | 0.0147 | |
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| 2.8004 | 3100 | 0.025 | - | |
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| 2.8907 | 3200 | 0.02 | - | |
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| 2.9810 | 3300 | 0.0187 | - | |
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| 3.0714 | 3400 | 0.0264 | - | |
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| 3.1617 | 3500 | 0.0153 | - | |
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| 3.2520 | 3600 | 0.01 | - | |
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| 3.3424 | 3700 | 0.0156 | - | |
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| 3.4327 | 3800 | 0.014 | - | |
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| 3.5230 | 3900 | 0.027 | - | |
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| 3.6134 | 4000 | 0.014 | 0.0093 | |
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| 3.7037 | 4100 | 0.0134 | - | |
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| 3.7940 | 4200 | 0.0127 | - | |
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| 3.8844 | 4300 | 0.0223 | - | |
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| 3.9747 | 4400 | 0.0137 | - | |
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| 4.0650 | 4500 | 0.01 | - | |
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| 4.1554 | 4600 | 0.0135 | - | |
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| 4.2457 | 4700 | 0.0082 | - | |
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| 4.3360 | 4800 | 0.013 | - | |
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| 4.4264 | 4900 | 0.0075 | - | |
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| 4.5167 | 5000 | 0.0064 | 0.0060 | |
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| 4.6070 | 5100 | 0.0113 | - | |
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| 4.6974 | 5200 | 0.0109 | - | |
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| 4.7877 | 5300 | 0.0116 | - | |
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| 4.8780 | 5400 | 0.0105 | - | |
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| 4.9684 | 5500 | 0.0074 | - | |
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| 5.0587 | 5600 | 0.0084 | - | |
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| 5.1491 | 5700 | 0.0111 | - | |
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| 5.2394 | 5800 | 0.0027 | - | |
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| 5.3297 | 5900 | 0.0066 | - | |
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| 5.4201 | 6000 | 0.0064 | 0.0045 | |
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| 5.5104 | 6100 | 0.0044 | - | |
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| 5.6007 | 6200 | 0.0096 | - | |
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| 5.6911 | 6300 | 0.0065 | - | |
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| 5.7814 | 6400 | 0.0093 | - | |
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| 5.8717 | 6500 | 0.0136 | - | |
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| 5.9621 | 6600 | 0.0214 | - | |
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| 6.0524 | 6700 | 0.0054 | - | |
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| 6.1427 | 6800 | 0.0028 | - | |
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| 6.2331 | 6900 | 0.008 | - | |
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| 6.3234 | 7000 | 0.0115 | 0.0021 | |
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| 6.4137 | 7100 | 0.0045 | - | |
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| 6.5041 | 7200 | 0.0053 | - | |
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| 6.5944 | 7300 | 0.0083 | - | |
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| 6.6847 | 7400 | 0.0081 | - | |
|
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| 6.7751 | 7500 | 0.0035 | - | |
|
|
| 6.8654 | 7600 | 0.0081 | - | |
|
|
| 6.9557 | 7700 | 0.0063 | - | |
|
|
| 7.0461 | 7800 | 0.0056 | - | |
|
|
| 7.1364 | 7900 | 0.0034 | - | |
|
|
| 7.2267 | 8000 | 0.0069 | 0.0025 | |
|
|
| 7.3171 | 8100 | 0.0026 | - | |
|
|
| 7.4074 | 8200 | 0.0047 | - | |
|
|
| 7.4977 | 8300 | 0.0034 | - | |
|
|
| 7.5881 | 8400 | 0.0052 | - | |
|
|
| 7.6784 | 8500 | 0.0081 | - | |
|
|
| 7.7687 | 8600 | 0.0023 | - | |
|
|
| 7.8591 | 8700 | 0.004 | - | |
|
|
| 7.9494 | 8800 | 0.004 | - | |
|
|
| 8.0397 | 8900 | 0.003 | - | |
|
|
| 8.1301 | 9000 | 0.0032 | 0.0031 | |
|
|
| 8.2204 | 9100 | 0.0054 | - | |
|
|
| 8.3107 | 9200 | 0.0058 | - | |
|
|
| 8.4011 | 9300 | 0.0044 | - | |
|
|
| 8.4914 | 9400 | 0.0029 | - | |
|
|
| 8.5818 | 9500 | 0.0039 | - | |
|
|
| 8.6721 | 9600 | 0.0033 | - | |
|
|
| 8.7624 | 9700 | 0.0061 | - | |
|
|
| 8.8528 | 9800 | 0.0029 | - | |
|
|
| 8.9431 | 9900 | 0.0037 | - | |
|
|
| 9.0334 | 10000 | 0.0024 | 0.0020 | |
|
|
| 9.1238 | 10100 | 0.0046 | - | |
|
|
| 9.2141 | 10200 | 0.0037 | - | |
|
|
| 9.3044 | 10300 | 0.0041 | - | |
|
|
| 9.3948 | 10400 | 0.0064 | - | |
|
|
| 9.4851 | 10500 | 0.0058 | - | |
|
|
| 9.5754 | 10600 | 0.0058 | - | |
|
|
| 9.6658 | 10700 | 0.0031 | - | |
|
|
| 9.7561 | 10800 | 0.0015 | - | |
|
|
| 9.8464 | 10900 | 0.0037 | - | |
|
|
| 9.9368 | 11000 | 0.0045 | 0.0013 | |
|
|
| 10.0271 | 11100 | 0.0038 | - | |
|
|
| 10.1174 | 11200 | 0.0027 | - | |
|
|
| 10.2078 | 11300 | 0.0061 | - | |
|
|
| 10.2981 | 11400 | 0.0046 | - | |
|
|
| 10.3884 | 11500 | 0.0028 | - | |
|
|
| 10.4788 | 11600 | 0.0021 | - | |
|
|
| 10.5691 | 11700 | 0.0029 | - | |
|
|
| 10.6594 | 11800 | 0.005 | - | |
|
|
| 10.7498 | 11900 | 0.002 | - | |
|
|
| 10.8401 | 12000 | 0.0058 | 0.0012 | |
|
|
| 10.9304 | 12100 | 0.003 | - | |
|
|
| 11.0208 | 12200 | 0.0005 | - | |
|
|
| 11.1111 | 12300 | 0.0022 | - | |
|
|
| 11.2014 | 12400 | 0.0046 | - | |
|
|
| 11.2918 | 12500 | 0.0028 | - | |
|
|
| 11.3821 | 12600 | 0.0016 | - | |
|
|
| 11.4724 | 12700 | 0.0026 | - | |
|
|
| 11.5628 | 12800 | 0.0025 | - | |
|
|
| 11.6531 | 12900 | 0.0009 | - | |
|
|
| 11.7435 | 13000 | 0.0022 | 0.0014 | |
|
|
| 11.8338 | 13100 | 0.0021 | - | |
|
|
| 11.9241 | 13200 | 0.0018 | - | |
|
|
| 12.0145 | 13300 | 0.0032 | - | |
|
|
| 12.1048 | 13400 | 0.0024 | - | |
|
|
| 12.1951 | 13500 | 0.0029 | - | |
|
|
| 12.2855 | 13600 | 0.0009 | - | |
|
|
| 12.3758 | 13700 | 0.0009 | - | |
|
|
| 12.4661 | 13800 | 0.002 | - | |
|
|
| 12.5565 | 13900 | 0.0026 | - | |
|
|
| 12.6468 | 14000 | 0.0008 | 0.0011 | |
|
|
| 12.7371 | 14100 | 0.0016 | - | |
|
|
| 12.8275 | 14200 | 0.0012 | - | |
|
|
| 12.9178 | 14300 | 0.0009 | - | |
|
|
| 13.0081 | 14400 | 0.0013 | - | |
|
|
| 13.0985 | 14500 | 0.0013 | - | |
|
|
| 13.1888 | 14600 | 0.004 | - | |
|
|
| 13.2791 | 14700 | 0.0006 | - | |
|
|
| 13.3695 | 14800 | 0.0025 | - | |
|
|
| 13.4598 | 14900 | 0.0004 | - | |
|
|
| 13.5501 | 15000 | 0.0021 | 0.0010 | |
|
|
| 13.6405 | 15100 | 0.0023 | - | |
|
|
| 13.7308 | 15200 | 0.0054 | - | |
|
|
| 13.8211 | 15300 | 0.0014 | - | |
|
|
| 13.9115 | 15400 | 0.0028 | - | |
|
|
| 14.0018 | 15500 | 0.0008 | - | |
|
|
| 14.0921 | 15600 | 0.0006 | - | |
|
|
| 14.1825 | 15700 | 0.0015 | - | |
|
|
| 14.2728 | 15800 | 0.0004 | - | |
|
|
| 14.3631 | 15900 | 0.005 | - | |
|
|
| **14.4535** | **16000** | **0.0009** | **0.0011** | |
|
|
| 14.5438 | 16100 | 0.0022 | - | |
|
|
| 14.6341 | 16200 | 0.0015 | - | |
|
|
| 14.7245 | 16300 | 0.0021 | - | |
|
|
| 14.8148 | 16400 | 0.0012 | - | |
|
|
| 14.9051 | 16500 | 0.0005 | - | |
|
|
| 14.9955 | 16600 | 0.0019 | - | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 4.0.2 |
|
|
- Transformers: 4.50.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.5.2 |
|
|
- Datasets: 3.5.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
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