--- base_model: BAAI/bge-base-en-v1.5 language: - en library_name: sentence-transformers license: apache-2.0 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:56355 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: "\n Given the Column informations, generate an SQL query for\ \ the following question:\n Column: Finishing position | Points awarded (Platinum)\ \ | Points awarded (Gold) | Points awarded (Silver) | Points awarded (Satellite)\n\ \ Question: How many platinum points were awarded when 6 gold points were awarded?\n\ \ SQL Query: SELECT MAX Points awarded (Platinum) FROM table WHERE Points awarded\ \ (Gold) = 6\n " sentences: - How many platinum points were awarded when 6 gold points were awarded? - Did any team score games that totaled up to 860.5? - Who had the pole position at the German Grand Prix? - source_sentence: "\n Given the Column informations, generate an SQL query for\ \ the following question:\n Column: Player | No. | Nationality | Position | Years\ \ in Toronto | School/Club Team\n Question: What's Dell Curry nationality?\n\ \ SQL Query: SELECT Nationality FROM table WHERE Player = Dell Curry\n " sentences: - What is the title when original air date is may15,2008? - What's Dell Curry nationality? - What's the minimum total attendance of the Premier League association football? - source_sentence: "\n Given the Column informations, generate an SQL query for\ \ the following question:\n Column: Sepal length | Sepal width | Petal length\ \ | Petal width | Species\n Question: Name the species when petal width is 2.0\ \ and petal length is 4.9\n SQL Query: SELECT Species FROM table WHERE Petal\ \ width = 2.0 AND Petal length = 4.9\n " sentences: - What year was the championship in Wimbledon (2)? - Who wrote Series 38? - Name the species when petal width is 2.0 and petal length is 4.9 - source_sentence: "\n Given the Column informations, generate an SQL query for\ \ the following question:\n Column: No. in season | No. in series | Title | Directed\ \ by | Written by | Original air date | U.S. viewers (million)\n Question: How\ \ many millions of U.S. viewers watched the episode that first aired on March\ \ 31, 2013?\n SQL Query: SELECT U.S. viewers (million) FROM table WHERE Original\ \ air date = March 31, 2013\n " sentences: - How many millions of U.S. viewers watched the episode that first aired on March 31, 2013? - How many viewers were there for the premier with 34 - What is Bruce Cerone overall? - source_sentence: "\n Given the Column informations, generate an SQL query for\ \ the following question:\n Column: Nomination | Actors Name | Film Name | Director\ \ | Country\n Question: What was the film Falling up nominated for?\n SQL Query:\ \ SELECT Nomination FROM table WHERE Film Name = Falling Up\n " sentences: - What was the film Falling up nominated for? - Who wrote an episode watched by 19.01 million US viewers? - What player is on the Montreal Alouettes CFl team? model-index: - name: BGE base SQL Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.4676281647562665 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4697065121551833 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4697065121551833 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4697065121551833 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4676281647562665 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15656883738506108 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09394130243103667 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.046970651215518334 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4676281647562665 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4697065121551833 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4697065121551833 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4697065121551833 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.46889822604232273 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4686148549355503 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4686406337350657 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.46775412520468573 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4697065121551833 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4697065121551833 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4697065121551833 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46775412520468573 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15656883738506108 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09394130243103667 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.046970651215518334 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.46775412520468573 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4697065121551833 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4697065121551833 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4697065121551833 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4689612062665323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.46869882856782963 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4687237988187482 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.46750220430784734 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4697065121551833 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4697065121551833 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.46976949237939286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46750220430784734 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15656883738506108 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09394130243103667 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04697694923793929 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.46750220430784734 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4697065121551833 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4697065121551833 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.46976949237939286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4688906637675648 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4685833648234455 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.468602927990512 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.46769114498047615 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4696435319309737 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46976949237939286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.46976949237939286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46769114498047615 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1565478439769912 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09395389847587858 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04697694923793929 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.46769114498047615 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4696435319309737 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.46976949237939286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.46976949237939286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4689469541953942 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.468661040433304 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4686773555936371 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.46775412520468573 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4696435319309737 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4696435319309737 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4697065121551833 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46775412520468573 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1565478439769912 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09392870638619474 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.046970651215518334 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.46775412520468573 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4696435319309737 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4696435319309737 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4697065121551833 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4689578301883334 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.468696204391821 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.46870770760703784 name: Cosine Map@100 --- # BGE base SQL Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-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 - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### 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': 768, '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("dat-ai/bge-base-for_text2sql") # Run inference sentences = [ '\n Given the Column informations, generate an SQL query for the following question:\n Column: Nomination | Actors Name | Film Name | Director | Country\n Question: What was the film Falling up nominated for?\n SQL Query: SELECT Nomination FROM table WHERE Film Name = Falling Up\n ', 'What was the film Falling up nominated for?', 'Who wrote an episode watched by 19.01 million US viewers?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:----------|:-----------|:-----------|:----------| | cosine_accuracy@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | | cosine_accuracy@3 | 0.4697 | 0.4697 | 0.4697 | 0.4696 | 0.4696 | | cosine_accuracy@5 | 0.4697 | 0.4697 | 0.4697 | 0.4698 | 0.4696 | | cosine_accuracy@10 | 0.4697 | 0.4697 | 0.4698 | 0.4698 | 0.4697 | | cosine_precision@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | | cosine_precision@3 | 0.1566 | 0.1566 | 0.1566 | 0.1565 | 0.1565 | | cosine_precision@5 | 0.0939 | 0.0939 | 0.0939 | 0.094 | 0.0939 | | cosine_precision@10 | 0.047 | 0.047 | 0.047 | 0.047 | 0.047 | | cosine_recall@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | | cosine_recall@3 | 0.4697 | 0.4697 | 0.4697 | 0.4696 | 0.4696 | | cosine_recall@5 | 0.4697 | 0.4697 | 0.4697 | 0.4698 | 0.4696 | | cosine_recall@10 | 0.4697 | 0.4697 | 0.4698 | 0.4698 | 0.4697 | | **cosine_ndcg@10** | **0.4689** | **0.469** | **0.4689** | **0.4689** | **0.469** | | cosine_mrr@10 | 0.4686 | 0.4687 | 0.4686 | 0.4687 | 0.4687 | | cosine_map@100 | 0.4686 | 0.4687 | 0.4686 | 0.4687 | 0.4687 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 56,355 training samples * Columns: context and question * Approximate statistics based on the first 1000 samples: | | context | question | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | context | question | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
Given the Column informations, generate an SQL query for the following question:
Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes
Question: Tell me what the notes are for South Australia
SQL Query: SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA
 | Tell me what the notes are for South Australia | |
Given the Column informations, generate an SQL query for the following question:
Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes
Question: What is the current series where the new series began in June 2011?
SQL Query: SELECT Current series FROM table WHERE Notes = New series began in June 2011
 | What is the current series where the new series began in June 2011? | |
Given the Column informations, generate an SQL query for the following question:
Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes
Question: What is the format for South Australia?
SQL Query: SELECT Format FROM table WHERE State/territory = South Australia
 | What is the format for South Australia? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `gradient_accumulation_steps`: 8 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `fp16`: 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`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 8 - `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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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 - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.0227 | 10 | 1.773 | - | - | - | - | - | | 0.0454 | 20 | 1.3231 | - | - | - | - | - | | 0.0681 | 30 | 0.713 | - | - | - | - | - | | 0.0908 | 40 | 0.286 | - | - | - | - | - | | 0.1135 | 50 | 0.1013 | - | - | - | - | - | | 0.1362 | 60 | 0.0635 | - | - | - | - | - | | 0.1590 | 70 | 0.0453 | - | - | - | - | - | | 0.1817 | 80 | 0.041 | - | - | - | - | - | | 0.2044 | 90 | 0.039 | - | - | - | - | - | | 0.2271 | 100 | 0.027 | - | - | - | - | - | | 0.2498 | 110 | 0.0193 | - | - | - | - | - | | 0.2725 | 120 | 0.0167 | - | - | - | - | - | | 0.2952 | 130 | 0.016 | - | - | - | - | - | | 0.3179 | 140 | 0.0197 | - | - | - | - | - | | 0.3406 | 150 | 0.0217 | - | - | - | - | - | | 0.3633 | 160 | 0.0162 | - | - | - | - | - | | 0.3860 | 170 | 0.012 | - | - | - | - | - | | 0.4087 | 180 | 0.013 | - | - | - | - | - | | 0.4315 | 190 | 0.0255 | - | - | - | - | - | | 0.4542 | 200 | 0.0229 | - | - | - | - | - | | 0.4769 | 210 | 0.0181 | - | - | - | - | - | | 0.4996 | 220 | 0.0195 | - | - | - | - | - | | 0.5223 | 230 | 0.0199 | - | - | - | - | - | | 0.5450 | 240 | 0.0144 | - | - | - | - | - | | 0.5677 | 250 | 0.0102 | - | - | - | - | - | | 0.5904 | 260 | 0.0101 | - | - | - | - | - | | 0.6131 | 270 | 0.0095 | - | - | - | - | - | | 0.6358 | 280 | 0.0173 | - | - | - | - | - | | 0.6585 | 290 | 0.01 | - | - | - | - | - | | 0.6812 | 300 | 0.0129 | - | - | - | - | - | | 0.7039 | 310 | 0.0177 | - | - | - | - | - | | 0.7267 | 320 | 0.0106 | - | - | - | - | - | | 0.7494 | 330 | 0.0146 | - | - | - | - | - | | 0.7721 | 340 | 0.0185 | - | - | - | - | - | | 0.7948 | 350 | 0.0203 | - | - | - | - | - | | 0.8175 | 360 | 0.0146 | - | - | - | - | - | | 0.8402 | 370 | 0.0072 | - | - | - | - | - | | 0.8629 | 380 | 0.0102 | - | - | - | - | - | | 0.8856 | 390 | 0.0075 | - | - | - | - | - | | 0.9083 | 400 | 0.0064 | - | - | - | - | - | | 0.9310 | 410 | 0.0163 | - | - | - | - | - | | 0.9537 | 420 | 0.0069 | - | - | - | - | - | | 0.9764 | 430 | 0.0072 | - | - | - | - | - | | 0.9991 | 440 | 0.0147 | 0.4688 | 0.4689 | 0.4688 | 0.4689 | 0.4689 | | 1.0219 | 450 | 0.0151 | - | - | - | - | - | | 1.0446 | 460 | 0.0135 | - | - | - | - | - | | 1.0673 | 470 | 0.0189 | - | - | - | - | - | | 1.0900 | 480 | 0.0121 | - | - | - | - | - | | 1.1127 | 490 | 0.0064 | - | - | - | - | - | | 1.1354 | 500 | 0.0111 | - | - | - | - | - | | 1.1581 | 510 | 0.0103 | - | - | - | - | - | | 1.1808 | 520 | 0.0144 | - | - | - | - | - | | 1.2035 | 530 | 0.0151 | - | - | - | - | - | | 1.2262 | 540 | 0.0062 | - | - | - | - | - | | 1.2489 | 550 | 0.0104 | - | - | - | - | - | | 1.2716 | 560 | 0.0046 | - | - | - | - | - | | 1.2944 | 570 | 0.0056 | - | - | - | - | - | | 1.3171 | 580 | 0.0073 | - | - | - | - | - | | 1.3398 | 590 | 0.007 | - | - | - | - | - | | 1.3625 | 600 | 0.0074 | - | - | - | - | - | | 1.3852 | 610 | 0.0057 | - | - | - | - | - | | 1.4079 | 620 | 0.0052 | - | - | - | - | - | | 1.4306 | 630 | 0.0114 | - | - | - | - | - | | 1.4533 | 640 | 0.0075 | - | - | - | - | - | | 1.4760 | 650 | 0.0116 | - | - | - | - | - | | 1.4987 | 660 | 0.0092 | - | - | - | - | - | | 1.5214 | 670 | 0.0137 | - | - | - | - | - | | 1.5441 | 680 | 0.0066 | - | - | - | - | - | | 1.5668 | 690 | 0.0042 | - | - | - | - | - | | 1.5896 | 700 | 0.0036 | - | - | - | - | - | | 1.6123 | 710 | 0.0039 | - | - | - | - | - | | 1.6350 | 720 | 0.0065 | - | - | - | - | - | | 1.6577 | 730 | 0.0051 | - | - | - | - | - | | 1.6804 | 740 | 0.0054 | - | - | - | - | - | | 1.7031 | 750 | 0.0086 | - | - | - | - | - | | 1.7258 | 760 | 0.0062 | - | - | - | - | - | | 1.7485 | 770 | 0.0071 | - | - | - | - | - | | 1.7712 | 780 | 0.0108 | - | - | - | - | - | | 1.7939 | 790 | 0.009 | - | - | - | - | - | | 1.8166 | 800 | 0.0075 | - | - | - | - | - | | 1.8393 | 810 | 0.0039 | - | - | - | - | - | | 1.8620 | 820 | 0.0047 | - | - | - | - | - | | 1.8848 | 830 | 0.0037 | - | - | - | - | - | | 1.9075 | 840 | 0.0037 | - | - | - | - | - | | 1.9302 | 850 | 0.0064 | - | - | - | - | - | | 1.9529 | 860 | 0.0047 | - | - | - | - | - | | 1.9756 | 870 | 0.0034 | - | - | - | - | - | | 1.9983 | 880 | 0.0061 | 0.4689 | 0.4689 | 0.4689 | 0.4690 | 0.4690 | | 2.0210 | 890 | 0.0096 | - | - | - | - | - | | 2.0437 | 900 | 0.0071 | - | - | - | - | - | | 2.0664 | 910 | 0.0101 | - | - | - | - | - | | 2.0891 | 920 | 0.0054 | - | - | - | - | - | | 2.1118 | 930 | 0.0039 | - | - | - | - | - | | 2.1345 | 940 | 0.0074 | - | - | - | - | - | | 2.1573 | 950 | 0.0044 | - | - | - | - | - | | 2.1800 | 960 | 0.0088 | - | - | - | - | - | | 2.2027 | 970 | 0.0096 | - | - | - | - | - | | 2.2254 | 980 | 0.0057 | - | - | - | - | - | | 2.2481 | 990 | 0.0063 | - | - | - | - | - | | 2.2708 | 1000 | 0.0026 | - | - | - | - | - | | 2.2935 | 1010 | 0.0032 | - | - | - | - | - | | 2.3162 | 1020 | 0.0027 | - | - | - | - | - | | 2.3389 | 1030 | 0.0041 | - | - | - | - | - | | 2.3616 | 1040 | 0.0052 | - | - | - | - | - | | 2.3843 | 1050 | 0.0035 | - | - | - | - | - | | 2.4070 | 1060 | 0.0025 | - | - | - | - | - | | 2.4297 | 1070 | 0.0059 | - | - | - | - | - | | 2.4525 | 1080 | 0.0048 | - | - | - | - | - | | 2.4752 | 1090 | 0.0064 | - | - | - | - | - | | 2.4979 | 1100 | 0.0066 | - | - | - | - | - | | 2.5206 | 1110 | 0.0078 | - | - | - | - | - | | 2.5433 | 1120 | 0.0057 | - | - | - | - | - | | 2.5660 | 1130 | 0.0026 | - | - | - | - | - | | 2.5887 | 1140 | 0.0021 | - | - | - | - | - | | 2.6114 | 1150 | 0.0021 | - | - | - | - | - | | 2.6341 | 1160 | 0.0047 | - | - | - | - | - | | 2.6568 | 1170 | 0.0034 | - | - | - | - | - | | 2.6795 | 1180 | 0.0044 | - | - | - | - | - | | 2.7022 | 1190 | 0.0058 | - | - | - | - | - | | 2.7250 | 1200 | 0.0043 | - | - | - | - | - | | 2.7477 | 1210 | 0.0056 | - | - | - | - | - | | 2.7704 | 1220 | 0.0076 | - | - | - | - | - | | 2.7931 | 1230 | 0.0063 | - | - | - | - | - | | 2.8158 | 1240 | 0.0033 | - | - | - | - | - | | 2.8385 | 1250 | 0.0025 | - | - | - | - | - | | 2.8612 | 1260 | 0.0019 | - | - | - | - | - | | 2.8839 | 1270 | 0.0052 | - | - | - | - | - | | 2.9066 | 1280 | 0.0021 | - | - | - | - | - | | 2.9293 | 1290 | 0.0041 | - | - | - | - | - | | 2.9520 | 1300 | 0.0035 | - | - | - | - | - | | 2.9747 | 1310 | 0.0044 | - | - | - | - | - | | 2.9974 | 1320 | 0.0035 | - | - | - | - | - | | **2.9997** | **1321** | **-** | **0.469** | **0.469** | **0.469** | **0.469** | **0.469** | | 3.0202 | 1330 | 0.0062 | - | - | - | - | - | | 3.0429 | 1340 | 0.0047 | - | - | - | - | - | | 3.0656 | 1350 | 0.008 | - | - | - | - | - | | 3.0883 | 1360 | 0.0033 | - | - | - | - | - | | 3.1110 | 1370 | 0.0025 | - | - | - | - | - | | 3.1337 | 1380 | 0.0069 | - | - | - | - | - | | 3.1564 | 1390 | 0.0035 | - | - | - | - | - | | 3.1791 | 1400 | 0.0085 | - | - | - | - | - | | 3.2018 | 1410 | 0.007 | - | - | - | - | - | | 3.2245 | 1420 | 0.007 | - | - | - | - | - | | 3.2472 | 1430 | 0.0052 | - | - | - | - | - | | 3.2699 | 1440 | 0.0019 | - | - | - | - | - | | 3.2926 | 1450 | 0.0022 | - | - | - | - | - | | 3.3154 | 1460 | 0.0019 | - | - | - | - | - | | 3.3381 | 1470 | 0.0028 | - | - | - | - | - | | 3.3608 | 1480 | 0.0042 | - | - | - | - | - | | 3.3835 | 1490 | 0.0023 | - | - | - | - | - | | 3.4062 | 1500 | 0.0024 | - | - | - | - | - | | 3.4289 | 1510 | 0.0036 | - | - | - | - | - | | 3.4516 | 1520 | 0.0038 | - | - | - | - | - | | 3.4743 | 1530 | 0.0063 | - | - | - | - | - | | 3.4970 | 1540 | 0.0044 | - | - | - | - | - | | 3.5197 | 1550 | 0.0064 | - | - | - | - | - | | 3.5424 | 1560 | 0.0053 | - | - | - | - | - | | 3.5651 | 1570 | 0.0019 | - | - | - | - | - | | 3.5879 | 1580 | 0.0019 | - | - | - | - | - | | 3.6106 | 1590 | 0.0017 | - | - | - | - | - | | 3.6333 | 1600 | 0.004 | - | - | - | - | - | | 3.6560 | 1610 | 0.0026 | - | - | - | - | - | | 3.6787 | 1620 | 0.0031 | - | - | - | - | - | | 3.7014 | 1630 | 0.0043 | - | - | - | - | - | | 3.7241 | 1640 | 0.0032 | - | - | - | - | - | | 3.7468 | 1650 | 0.0041 | - | - | - | - | - | | 3.7695 | 1660 | 0.0069 | - | - | - | - | - | | 3.7922 | 1670 | 0.0063 | - | - | - | - | - | | 3.8149 | 1680 | 0.0038 | - | - | - | - | - | | 3.8376 | 1690 | 0.0024 | - | - | - | - | - | | 3.8603 | 1700 | 0.0018 | - | - | - | - | - | | 3.8831 | 1710 | 0.0034 | - | - | - | - | - | | 3.9058 | 1720 | 0.0016 | - | - | - | - | - | | 3.9285 | 1730 | 0.0026 | - | - | - | - | - | | 3.9512 | 1740 | 0.0037 | - | - | - | - | - | | 3.9739 | 1750 | 0.0024 | - | - | - | - | - | | 3.9966 | 1760 | 0.0027 | 0.4689 | 0.4690 | 0.4689 | 0.4689 | 0.4690 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.3.0 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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} } ```