| | --- |
| | 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) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 --> |
| | - **Maximum Sequence Length:** 1024 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | - **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] |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| |
|
| | #### Information Retrieval |
| |
|
| | * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](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 | |
| | |
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| | ## Bias, Risks and Limitations |
| | |
| | *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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| | ### Recommendations |
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| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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| | |
| | ## Training Details |
| | |
| | ### Training Dataset |
| | |
| | #### Unnamed Dataset |
| | |
| | * Size: 321 training samples |
| | * Columns: <code>anchor</code> and <code>positive</code> |
| | * Approximate statistics based on the first 321 samples: |
| | | | anchor | positive | |
| | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 7 tokens</li><li>mean: 14.03 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 74.79 tokens</li><li>max: 117 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | |
| | |:--------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>What brand did Jeff Ross help establish?</code> | <code>Graph: Team Coco Knowledge Graph<br>Node ID: jeff_ross_producer<br>Category: people<br>Name: Jeff Ross (Producer)<br>Type: Person<br><br>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.</code> | |
| | | <code>In what year did Conan O'Brien launch the travel show 'Conan O'Brien Must Go'?</code> | <code>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.</code> | |
| | | <code>What is the strength of the network TBS?</code> | <code>- Network tbs (Strength: parent)<br> Description: TBS provided the platform for the show.</code> | |
| | * Loss: [<code>MatryoshkaLoss</code>](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 |
| | <details><summary>Click to expand</summary> |
| | |
| | - `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 |
| |
|
| | </details> |
| |
|
| | ### 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} |
| | } |
| | ``` |
| | |
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