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--- |
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language: |
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- en |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- loss:Matryoshka2dLoss |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: distilbert/distilroberta-base |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: A woman is reading. |
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sentences: |
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- A woman is writing something. |
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- A man helps a boy ride a bike. |
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- A group wading across a ditch |
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- source_sentence: A man shoots a man. |
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sentences: |
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- A man with a pistol shoots another man. |
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- Suicide bomber strikes in Syria |
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- China and Taiwan hold historic talks |
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- source_sentence: A boy is vacuuming. |
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sentences: |
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- A little boy is vacuuming the floor. |
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- 'Breivik: Jail term ''ridiculous''' |
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- Glorious triple-gold night for Britain |
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- source_sentence: A man is spitting. |
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sentences: |
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- A man is speaking. |
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- The boy is jumping into a lake. |
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- 10 Things to Know for Thursday |
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- source_sentence: A plane in the sky. |
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sentences: |
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- Two airplanes in the sky. |
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- Nelson Mandela undergoes surgery |
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- Nelson Mandela undergoes surgery |
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pipeline_tag: sentence-similarity |
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co2_eq_emissions: |
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emissions: 69.2573690422145 |
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energy_consumed: 0.1781760038338226 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.626 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilroberta-base |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.8395203447657347 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8424556124488326 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8432537220190851 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8435994230515586 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8440900768179745 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8449067313707376 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.763767029856877 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.7569706383510251 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.8440900768179745 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8449067313707376 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.8186702838538092 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8170686920551 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8117192659894803 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.804879002947593 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8127154744140831 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8058410028545979 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.7396245702595934 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.7256120569318246 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.8186702838538092 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8170686920551 |
|
name: Spearman Max |
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--- |
|
|
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# SentenceTransformer based on distilbert/distilroberta-base |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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. |
|
|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
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(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}) |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("tomaarsen/distilroberta-base-nli-2d-matryoshka") |
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# Run inference |
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sentences = [ |
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'A plane in the sky.', |
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'Two airplanes in the sky.', |
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'Nelson Mandela undergoes surgery', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8395 | |
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| **spearman_cosine** | **0.8425** | |
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| pearson_manhattan | 0.8433 | |
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| spearman_manhattan | 0.8436 | |
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| pearson_euclidean | 0.8441 | |
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| spearman_euclidean | 0.8449 | |
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| pearson_dot | 0.7638 | |
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| spearman_dot | 0.757 | |
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| pearson_max | 0.8441 | |
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| spearman_max | 0.8449 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8187 | |
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| **spearman_cosine** | **0.8171** | |
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| pearson_manhattan | 0.8117 | |
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| spearman_manhattan | 0.8049 | |
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| pearson_euclidean | 0.8127 | |
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| spearman_euclidean | 0.8058 | |
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| pearson_dot | 0.7396 | |
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| spearman_dot | 0.7256 | |
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| pearson_max | 0.8187 | |
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| spearman_max | 0.8171 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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### Training Dataset |
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#### sentence-transformers/all-nli |
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* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [65dd388](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe) |
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* Size: 557,850 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
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| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | |
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* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3, |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": 1 |
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} |
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``` |
|
|
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### Evaluation Dataset |
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|
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#### sentence-transformers/stsb |
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|
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* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) |
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* Size: 1,500 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:--------------------------------------------------|:------------------------------------------------------|:------------------| |
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| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | |
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| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | |
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| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | |
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* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
|
"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3, |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": 1 |
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} |
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``` |
|
|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
|
|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: False |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
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- `group_by_length`: False |
|
- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: None |
|
- `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_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
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| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
|
| 0.0229 | 100 | 6.2779 | 3.9959 | 0.8008 | - | |
|
| 0.0459 | 200 | 4.3212 | 3.5818 | 0.7956 | - | |
|
| 0.0688 | 300 | 3.7135 | 3.4422 | 0.7940 | - | |
|
| 0.0918 | 400 | 3.5567 | 3.5458 | 0.7951 | - | |
|
| 0.1147 | 500 | 3.1297 | 3.1253 | 0.8050 | - | |
|
| 0.1376 | 600 | 2.7001 | 3.4366 | 0.7996 | - | |
|
| 0.1606 | 700 | 2.8664 | 3.6609 | 0.8033 | - | |
|
| 0.1835 | 800 | 2.6656 | 3.3736 | 0.7975 | - | |
|
| 0.2065 | 900 | 2.633 | 3.3735 | 0.8076 | - | |
|
| 0.2294 | 1000 | 2.4335 | 3.6499 | 0.7996 | - | |
|
| 0.2524 | 1100 | 2.4165 | 3.6301 | 0.8015 | - | |
|
| 0.2753 | 1200 | 2.2942 | 3.1541 | 0.7994 | - | |
|
| 0.2982 | 1300 | 2.2402 | 3.4284 | 0.7977 | - | |
|
| 0.3212 | 1400 | 2.2148 | 3.3775 | 0.7988 | - | |
|
| 0.3441 | 1500 | 2.2285 | 3.6097 | 0.8016 | - | |
|
| 0.3671 | 1600 | 2.0591 | 3.3839 | 0.7926 | - | |
|
| 0.3900 | 1700 | 2.0253 | 3.1113 | 0.7981 | - | |
|
| 0.4129 | 1800 | 2.0244 | 3.8289 | 0.7954 | - | |
|
| 0.4359 | 1900 | 1.8582 | 3.3515 | 0.8000 | - | |
|
| 0.4588 | 2000 | 1.977 | 3.3054 | 0.7917 | - | |
|
| 0.4818 | 2100 | 1.9028 | 3.2166 | 0.7927 | - | |
|
| 0.5047 | 2200 | 1.8316 | 3.6504 | 0.7955 | - | |
|
| 0.5276 | 2300 | 1.8404 | 3.2822 | 0.7843 | - | |
|
| 0.5506 | 2400 | 1.8455 | 3.2583 | 0.7941 | - | |
|
| 0.5735 | 2500 | 1.9488 | 3.3970 | 0.7971 | - | |
|
| 0.5965 | 2600 | 1.9403 | 2.8948 | 0.7959 | - | |
|
| 0.6194 | 2700 | 1.8884 | 3.2227 | 0.8008 | - | |
|
| 0.6423 | 2800 | 1.8655 | 3.1948 | 0.7920 | - | |
|
| 0.6653 | 2900 | 1.8567 | 3.4374 | 0.7913 | - | |
|
| 0.6882 | 3000 | 1.8423 | 3.1118 | 0.7949 | - | |
|
| 0.7112 | 3100 | 1.7475 | 3.1359 | 0.8062 | - | |
|
| 0.7341 | 3200 | 1.8166 | 2.9927 | 0.7984 | - | |
|
| 0.7571 | 3300 | 1.5626 | 3.5143 | 0.8405 | - | |
|
| 0.7800 | 3400 | 1.2038 | 3.3909 | 0.8411 | - | |
|
| 0.8029 | 3500 | 1.1579 | 3.2458 | 0.8413 | - | |
|
| 0.8259 | 3600 | 1.0978 | 3.1592 | 0.8404 | - | |
|
| 0.8488 | 3700 | 1.0283 | 2.9557 | 0.8408 | - | |
|
| 0.8718 | 3800 | 0.9993 | 3.4073 | 0.8430 | - | |
|
| 0.8947 | 3900 | 0.9727 | 3.0570 | 0.8434 | - | |
|
| 0.9176 | 4000 | 0.9692 | 2.9357 | 0.8439 | - | |
|
| 0.9406 | 4100 | 0.9412 | 2.9494 | 0.8428 | - | |
|
| 0.9635 | 4200 | 1.0063 | 3.4047 | 0.8422 | - | |
|
| 0.9865 | 4300 | 0.9678 | 3.4299 | 0.8425 | - | |
|
| 1.0 | 4359 | - | - | - | 0.8171 | |
|
|
|
|
|
### Environmental Impact |
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
- **Energy Consumed**: 0.178 kWh |
|
- **Carbon Emitted**: 0.069 kg of CO2 |
|
- **Hours Used**: 0.626 hours |
|
|
|
### Training Hardware |
|
- **On Cloud**: No |
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
|
- **RAM Size**: 31.78 GB |
|
|
|
### Framework Versions |
|
- Python: 3.11.6 |
|
- Sentence Transformers: 3.0.0.dev0 |
|
- Transformers: 4.41.0.dev0 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.26.1 |
|
- Datasets: 2.18.0 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### Matryoshka2dLoss |
|
```bibtex |
|
@misc{li20242d, |
|
title={2D Matryoshka Sentence Embeddings}, |
|
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, |
|
year={2024}, |
|
eprint={2402.14776}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
#### 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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} |
|
``` |
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