--- datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:42333 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'Tag: chicken & broccoli alfredo For chicken & broccoli alfredo, these dietary tags go well with it: dinner, italian cuisine, meat recipes, lunch, italian american cuisine, american cuisine, pasta recipes, contains dairy, european cuisine, vegetarian' sentences: - 'Tag: chicken & broccoli alfredo What dietary classifications are suitable for chicken & broccoli alfredo?' - 'Tag: vegan pad thai What dietary labels fit vegan pad thai?' - 'Tag: apple pie filling Which dietary tags apply to apple pie filling?' - source_sentence: 'Tag: beef and broccoli A small description of beef and broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.' sentences: - 'Tag: chicken lettuce wrap What are the principal macro ingredients of chicken lettuce wrap?' - 'Tag: teriyaki tofu What are the micro ingredients used in teriyaki tofu?' - 'Tag: beef and broccoli What’s the best way to describe beef and broccoli?' - source_sentence: 'Tag: scrambled eggs with veggies For scrambled eggs with veggies, these dietary tags go well with it: breakfast, american cuisine, protein rich recipes, stir fry recipes, gluten free recipes' sentences: - 'Tag: kimchi fried rice (chicken) What are the vital macro ingredients in kimchi fried rice (chicken)?' - 'Tag: scrambled eggs with veggies What are the key macro ingredients for scrambled eggs with veggies?' - 'Tag: scrambled eggs with veggies How should I label scrambled eggs with veggies in terms of dietary categories?' - source_sentence: 'Tag: mixed vegetable stir-fry Micro ingredients required to cook mixed vegetable stir-fry: Salt, Cornstarch, Black Pepper Powder' sentences: - 'Tag: vegan pad thai Can you provide a thorough explanation of how to cook vegan pad thai?' - 'Tag: chicken & broccoli alfredo What’s involved in preparing the ingredients for chicken & broccoli alfredo?' - 'Tag: mixed vegetable stir-fry What are the main components of mixed vegetable stir-fry?' - source_sentence: 'Tag: vegan pad thai Cook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins' sentences: - 'Tag: vegan pad thai What’s the expected cook time for vegan pad thai?' - 'Tag: scrambled eggs with veggies What dietary classifications suit scrambled eggs with veggies?' - 'Tag: vegetable pulao What are some creative garnishing tips for vegetable pulao?' model-index: - name: SentenceTransformer results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.9688300597779675 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9701110162254484 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9748078565328779 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9946626814688301 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9688300597779675 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8469968687731283 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.8014944491887276 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.4411614005123826 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3285582123541133 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6209009393680616 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8938791122768492 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9605094343458989 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9592536302802654 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9733707623385245 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9539794228951505 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.9679760888129804 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9692570452604612 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9752348420153715 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9948761742100769 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9679760888129804 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8459294050668943 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.7992741246797609 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.43917591801878736 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32842427107478345 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6204243930706356 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8918949005733805 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9569316518238379 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9566533189656364 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9727438392094664 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9511517923410544 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.9694705380017079 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9705380017079419 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9760888129803587 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9948761742100769 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9694705380017079 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8471391972672928 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.798462852263023 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.43800170794193005 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3286967284778983 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6210852039363994 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8912874628929282 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9550379203773738 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9558695124747556 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9739451594756885 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9499982560169666 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.9698975234842016 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9720324508966696 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9771562766865927 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9938087105038429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9698975234842016 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8472815257614573 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.7965841161400511 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.4339666951323655 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3288006791102436 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.621300984099874 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.889481670123216 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9478284738318897 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9517343805870713 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.974398746831496 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9459942940005901 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 32 type: dim_32 metrics: - type: cosine_accuracy@1 value: 0.9690435525192144 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9707514944491887 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9769427839453458 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9929547395388557 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9690435525192144 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.8464987190435526 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.7940222032450898 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.4318531169940221 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3286197185962344 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6208008011060958 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8871009719002887 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9440570228945548 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9489614439178549 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9734810669215016 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9417483259746888 name: Cosine Map@100 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Adi-0-0-Gupta/Embedding-v2") # Run inference sentences = [ 'Tag: vegan pad thai\n\nCook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins', 'Tag: vegan pad thai\n\nWhat’s the expected cook time for vegan pad thai?', 'Tag: scrambled eggs with veggies\n\nWhat dietary classifications suit scrambled eggs with veggies?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.9688 | | cosine_accuracy@3 | 0.9701 | | cosine_accuracy@5 | 0.9748 | | cosine_accuracy@10 | 0.9947 | | cosine_precision@1 | 0.9688 | | cosine_precision@3 | 0.847 | | cosine_precision@5 | 0.8015 | | cosine_precision@10 | 0.4412 | | cosine_recall@1 | 0.3286 | | cosine_recall@3 | 0.6209 | | cosine_recall@5 | 0.8939 | | cosine_recall@10 | 0.9605 | | cosine_ndcg@10 | 0.9593 | | cosine_mrr@10 | 0.9734 | | **cosine_map@100** | **0.954** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.968 | | cosine_accuracy@3 | 0.9693 | | cosine_accuracy@5 | 0.9752 | | cosine_accuracy@10 | 0.9949 | | cosine_precision@1 | 0.968 | | cosine_precision@3 | 0.8459 | | cosine_precision@5 | 0.7993 | | cosine_precision@10 | 0.4392 | | cosine_recall@1 | 0.3284 | | cosine_recall@3 | 0.6204 | | cosine_recall@5 | 0.8919 | | cosine_recall@10 | 0.9569 | | cosine_ndcg@10 | 0.9567 | | cosine_mrr@10 | 0.9727 | | **cosine_map@100** | **0.9512** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:---------| | cosine_accuracy@1 | 0.9695 | | cosine_accuracy@3 | 0.9705 | | cosine_accuracy@5 | 0.9761 | | cosine_accuracy@10 | 0.9949 | | cosine_precision@1 | 0.9695 | | cosine_precision@3 | 0.8471 | | cosine_precision@5 | 0.7985 | | cosine_precision@10 | 0.438 | | cosine_recall@1 | 0.3287 | | cosine_recall@3 | 0.6211 | | cosine_recall@5 | 0.8913 | | cosine_recall@10 | 0.955 | | cosine_ndcg@10 | 0.9559 | | cosine_mrr@10 | 0.9739 | | **cosine_map@100** | **0.95** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.9699 | | cosine_accuracy@3 | 0.972 | | cosine_accuracy@5 | 0.9772 | | cosine_accuracy@10 | 0.9938 | | cosine_precision@1 | 0.9699 | | cosine_precision@3 | 0.8473 | | cosine_precision@5 | 0.7966 | | cosine_precision@10 | 0.434 | | cosine_recall@1 | 0.3288 | | cosine_recall@3 | 0.6213 | | cosine_recall@5 | 0.8895 | | cosine_recall@10 | 0.9478 | | cosine_ndcg@10 | 0.9517 | | cosine_mrr@10 | 0.9744 | | **cosine_map@100** | **0.946** | #### Information Retrieval * Dataset: `dim_32` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.969 | | cosine_accuracy@3 | 0.9708 | | cosine_accuracy@5 | 0.9769 | | cosine_accuracy@10 | 0.993 | | cosine_precision@1 | 0.969 | | cosine_precision@3 | 0.8465 | | cosine_precision@5 | 0.794 | | cosine_precision@10 | 0.4319 | | cosine_recall@1 | 0.3286 | | cosine_recall@3 | 0.6208 | | cosine_recall@5 | 0.8871 | | cosine_recall@10 | 0.9441 | | cosine_ndcg@10 | 0.949 | | cosine_mrr@10 | 0.9735 | | **cosine_map@100** | **0.9417** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 42,333 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | Tag: beef and broccoli

A small description of beef and broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce. | Tag: beef and broccoli

How do you describe beef and broccoli? | | Tag: beef and broccoli

Garnishing tips for beef and broccoli: Best served on it's own or on top of hot rice with chopped scallions! | Tag: beef and broccoli

What are some classic garnishes for beef and broccoli? | | Tag: beef and broccoli

For beef and broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes | Tag: beef and broccoli

What dietary labels suit beef and broccoli? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 100 - `lr_scheduler_type`: constant - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 100 - `max_steps`: -1 - `lr_scheduler_type`: constant - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 | |:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:| | 0.3023 | 25 | 2.7893 | 0.9106 | 0.9169 | 0.8833 | 0.9193 | 0.9013 | | 0.6047 | 50 | 1.6554 | 0.9061 | 0.9153 | 0.8858 | 0.9199 | 0.8970 | | 0.9070 | 75 | 0.7514 | 0.9361 | 0.9382 | 0.9216 | 0.9423 | 0.9292 | | 1.2079 | 100 | 1.2044 | 0.9334 | 0.9370 | 0.9186 | 0.9413 | 0.9263 | | 1.5102 | 125 | 1.4103 | 0.9312 | 0.9342 | 0.9146 | 0.9382 | 0.9222 | | 1.8125 | 150 | 0.6925 | 0.9444 | 0.9463 | 0.9326 | 0.9502 | 0.9385 | | 2.1134 | 175 | 0.7937 | 0.9333 | 0.9376 | 0.9196 | 0.9410 | 0.9256 | | 2.4157 | 200 | 1.3185 | 0.9321 | 0.9355 | 0.9191 | 0.9399 | 0.9245 | | 2.7181 | 225 | 1.0296 | 0.9400 | 0.9426 | 0.9293 | 0.9466 | 0.9345 | | 3.0189 | 250 | 0.3606 | 0.9342 | 0.9373 | 0.9231 | 0.9417 | 0.9282 | | 3.3212 | 275 | 1.2364 | 0.9381 | 0.9410 | 0.9273 | 0.9444 | 0.9312 | | 3.6236 | 300 | 1.2507 | 0.9305 | 0.9340 | 0.9193 | 0.9385 | 0.9233 | | 3.9259 | 325 | 0.3211 | 0.9500 | 0.9512 | 0.9417 | 0.9540 | 0.9460 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```