--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:321 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/modernbert-embed-base widget: - source_sentence: Since what year have they been married? sentences: - 'Graph: Team Coco Knowledge Graph Node ID: 2015_conan_cuba Category: events Name: Conan in Cuba Type: Event Description: Conan O''Brien traveled to Havana to film a historic episode—the first by an American late-night host in over 50 years—part of his ''Conan Without Borders'' specials. Relationships: - Host conan_obrien - Occurred during conan_tbs' - 'Description: Liza Powel O''Brien is an American playwright and podcast host. She met Conan O''Brien in 2000 while working at an advertising agency, and they married in 2002. She has written numerous plays staged at theaters like the Geffen Playhouse and Ojai Playwrights Conference, and in 2022 she launched the history podcast "Significant Others" on Conan''s Team Coco network.' - "Relationships:\n- Spouse conan_obrien (Strength: very strong)\n Description:\ \ Married since 2002; they have two children together.\n- Podcast host team_coco\ \ (Strength: moderate)\n Description: Hosts the \"Significant Others\" podcast\ \ under the Team Coco banner." - source_sentence: Which team produced Conan's final late night episode? sentences: - 'Graph: Team Coco Knowledge Graph Node ID: 2021_conan_finale Category: events Name: Conan''s Final Late Night Episode Type: Event Description: The final episode of ''Conan'' on TBS, marking the end of Conan O''Brien''s 28-year run as a late-night host with heartfelt goodbyes and memorable comedy moments. Relationships: - Honoree conan_obrien - Participant andy_richter - Producer team_coco' - 'References: - ([Conan O''Brien - Wikipedia](https://en.wikipedia.org/wiki/Conan_O%27Brien)) - ([Andy Richter Net Worth | Celebrity Net Worth](https://www.celebritynetworth.com))' - 'Description: Airing on SiriusXM''s Team Coco Radio channel.' - source_sentence: What type of document is referenced for the tour? sentences: - "Relationships:\n- Late-night host conan_obrien (Strength: core talent)\n Description:\ \ Conan's break in late night came through NBC.\n- Production partner conaco (Strength:\ \ strong)\n Description: NBC worked with Conaco on Conan's shows.\n\nAwards and\ \ Recognitions:\n- Legacy of late-night programming" - 'Major Events: - 1993 Joined ''Late Night'' with Conan - 2009 Transitioned to ''The Tonight Show'' - 2010 Concluded run as Conan''s bandleader' - 'References: - ([The Legally Prohibited from Being Funny on Television Tour - Wikipedia](https://en.wikipedia.org/wiki/The_Legally_Prohibited_from_Being_Funny_on_Television_Tour))' - source_sentence: In what year did Triumph the Insult Comic Dog debut? sentences: - "Relationships:\n- Host-guest (Prankster) conan_obrien (Strength: moderate)\n\ \ Description: Repeatedly played the 'Mac and Me' gag, to Conan's feigned exasperation.\n\ \nMajor Events:\n- 2004 First Mac and Me Gag on 'Late Night'\n- 2021 Final TBS\ \ Show Prank cameo" - 'Awards and Recognitions: - MFA in Fiction Writing from Columbia University - Playwright with works at the Geffen Playhouse and Ojai Playwrights Conference - Host of the "Significant Others" podcast (2022–present)' - 'Graph: Team Coco Knowledge Graph Node ID: triumph_insult_comic_dog Category: creative works Name: Triumph the Insult Comic Dog Type: Puppet character Description: A recurring canine puppet character, voiced by Robert Smigel, that debuted on Conan''s ''Late Night'' in 1997, known for roasting celebrities and absurd humor. Relationships: - Creator/performer robert_smigel - Host platform conan_obrien' - source_sentence: Who are the hosts of The Conan & Jordan Show? sentences: - 'Awards and Recognitions: - 7 Primetime Emmy nominations for writing on Conan''s shows - 10 WGA Award nominations (with 2 wins) - 2 Daytime Emmy nominations for Animated Program performance Major Events: - 1993 Late Night Debut – Joined Conan''s first show as sidekick. - 2000 Departure – Left ''Late Night'' to pursue acting. - 2010 Tour & TBS Move – Reunited with Conan on the live tour and TBS.' - 'Graph: Team Coco Knowledge Graph Node ID: the_conan_and_jordan_show Category: shows Name: The Conan & Jordan Show (radio program) Type: Show Description: A spin-off audio series on SiriusXM''s Team Coco Radio, launched in 2023, featuring Conan O''Brien and Jordan Schlansky continuing their comedic odd-couple dynamic.' - 'Major Events: - 2010 Premiere – ''Conan'' debuted on TBS. - 2015 ''Conan Without Borders'' – International travel specials aired. - 2021 Finale – Conan ended his TBS run. References: - ([Conan O''Brien - Wikipedia](https://en.wikipedia.org/wiki/Conan_O%27Brien))' pipeline_tag: sentence-similarity 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 model-index: - name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB) results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7222222222222222 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8611111111111112 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9166666666666666 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444444444444444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7222222222222222 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2870370370370371 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18333333333333338 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444444444444446 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7222222222222222 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8611111111111112 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444444444444444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8363985989991439 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.800925925925926 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8041634291634291 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6944444444444444 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8888888888888888 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9166666666666666 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9722222222222222 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6944444444444444 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29629629629629634 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18333333333333335 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09722222222222224 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6944444444444444 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8888888888888888 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9722222222222222 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8349701465406345 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7909722222222222 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.791703216374269 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6666666666666666 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8611111111111112 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9166666666666666 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444444444444444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6666666666666666 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28703703703703703 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18333333333333335 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444444444444446 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8611111111111112 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444444444444444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8074890903790802 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7627314814814814 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7662037037037037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6388888888888888 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8611111111111112 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9166666666666666 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444444444444444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6388888888888888 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2870370370370371 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18333333333333338 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444444444444446 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6388888888888888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8611111111111112 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444444444444444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.803777679552595 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7574074074074074 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7597654530591711 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6111111111111112 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7777777777777778 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9166666666666666 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6111111111111112 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2592592592592593 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666669 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09166666666666669 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6111111111111112 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7777777777777778 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9166666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7608354868794361 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7111441798941799 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7139831037236697 name: Cosine Map@100 --- # Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB) This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) - **Maximum Sequence Length:** 1024 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 768, '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}) (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("densonsmith/modernbert-embed-quickb") # Run inference sentences = [ 'Who are the hosts of The Conan & Jordan Show?', "Graph: Team Coco Knowledge Graph\nNode ID: the_conan_and_jordan_show\nCategory: shows\nName: The Conan & Jordan Show (radio program)\nType: Show\n\nDescription: A spin-off audio series on SiriusXM's Team Coco Radio, launched in 2023, featuring Conan O'Brien and Jordan Schlansky continuing their comedic odd-couple dynamic.", "Awards and Recognitions:\n- 7 Primetime Emmy nominations for writing on Conan's shows\n- 10 WGA Award nominations (with 2 wins)\n- 2 Daytime Emmy nominations for Animated Program performance\n\nMajor Events:\n- 1993 Late Night Debut – Joined Conan's first show as sidekick.\n- 2000 Departure – Left 'Late Night' to pursue acting.\n- 2010 Tour & TBS Move – Reunited with Conan on the live tour and TBS.", ] 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 | 0.7222 | 0.6944 | 0.6667 | 0.6389 | 0.6111 | | cosine_accuracy@3 | 0.8611 | 0.8889 | 0.8611 | 0.8611 | 0.7778 | | cosine_accuracy@5 | 0.9167 | 0.9167 | 0.9167 | 0.9167 | 0.8333 | | cosine_accuracy@10 | 0.9444 | 0.9722 | 0.9444 | 0.9444 | 0.9167 | | cosine_precision@1 | 0.7222 | 0.6944 | 0.6667 | 0.6389 | 0.6111 | | cosine_precision@3 | 0.287 | 0.2963 | 0.287 | 0.287 | 0.2593 | | cosine_precision@5 | 0.1833 | 0.1833 | 0.1833 | 0.1833 | 0.1667 | | cosine_precision@10 | 0.0944 | 0.0972 | 0.0944 | 0.0944 | 0.0917 | | cosine_recall@1 | 0.7222 | 0.6944 | 0.6667 | 0.6389 | 0.6111 | | cosine_recall@3 | 0.8611 | 0.8889 | 0.8611 | 0.8611 | 0.7778 | | cosine_recall@5 | 0.9167 | 0.9167 | 0.9167 | 0.9167 | 0.8333 | | cosine_recall@10 | 0.9444 | 0.9722 | 0.9444 | 0.9444 | 0.9167 | | **cosine_ndcg@10** | **0.8364** | **0.835** | **0.8075** | **0.8038** | **0.7608** | | cosine_mrr@10 | 0.8009 | 0.791 | 0.7627 | 0.7574 | 0.7111 | | cosine_map@100 | 0.8042 | 0.7917 | 0.7662 | 0.7598 | 0.714 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 321 training samples * Columns: anchor and positive * Approximate statistics based on the first 321 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What brand did Jeff Ross help establish? | Graph: Team Coco Knowledge Graph
Node ID: jeff_ross_producer
Category: people
Name: Jeff Ross (Producer)
Type: Person

Description: Jeff Ross is a television producer who has served as Conan O'Brien's executive producer since 1993. He is a key business partner in Conan's media ventures and helped establish the Team Coco brand. | | In what year did Conan O'Brien launch the travel show 'Conan O'Brien Must Go'? | Description: Conan O'Brien is an American television host, comedian, writer, actor, and producer, best known for hosting late-night shows including "Late Night with Conan O'Brien", "The Tonight Show with Conan O'Brien", and "Conan". He also hosts the podcast "Conan O'Brien Needs a Friend" and, in 2024, launched the travel show "Conan O'Brien Must Go" on Max. | | What is the strength of the network TBS? | - Network tbs (Strength: parent)
Description: TBS provided the platform for the show. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 4 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `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`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_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`: 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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | 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 | |:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 1.0 | 6 | - | 0.7909 | 0.8034 | 0.7711 | 0.7992 | 0.6908 | | 1.7901 | 10 | 16.3044 | - | - | - | - | - | | **2.0** | **12** | **-** | **0.8364** | **0.8294** | **0.8022** | **0.8038** | **0.7691** | | 3.0 | 18 | - | 0.8364 | 0.8313 | 0.8059 | 0.7938 | 0.7599 | | 3.3951 | 20 | 5.6348 | 0.8364 | 0.8350 | 0.8075 | 0.8038 | 0.7608 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.4 - Sentence Transformers: 3.4.0 - Transformers: 4.48.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.2.0 - 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", } ``` #### 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} } ```