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--- |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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pipeline_tag: sentence-similarity |
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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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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: Consumer Products segment decreased 10% to $3,572.5 million. |
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sentences: |
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- What was the impact of the Federal Reserve’s policy changes on Schwab money market |
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funds in 2022? |
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- What was the total revenue of Hasbro's Consumer Products segment in 2022? |
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- How much did the company's currently payable U.S. taxes amount to in 2023? |
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- source_sentence: PricewaterhouseCoopers LLP is mentioned as the Firm’s independent |
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registered public accounting firm (PCAOB ID 238) in the audit of the Consolidated |
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Financial Statements. |
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sentences: |
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- Where in the document can the Consolidated Financial Statements be found as mentioned |
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in a 2024 report? |
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- What type of firm is PricewaterhouseCoopers LLP as mentioned in the context of |
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auditing? |
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- Which note in the report provides details about legal proceedings? |
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- source_sentence: If, in the future, foreign exchange or capital control restrictions |
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were to be imposed and become applicable to us, such restrictions could potentially |
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reduce the amounts that we would be able to receive from our Macao, Hong Kong |
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and mainland China subsidiaries. |
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sentences: |
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- What are the potential consequences for the parent company if foreign exchange |
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or capital control restrictions were imposed in the future? |
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- What is described under Item 8 in the context of a financial document? |
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- What types of investments are primarily included in the Goldman Sachs' investments |
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in funds at NAV as of December 2023? |
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- source_sentence: Determining income tax provisions involves forecasting future financial |
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results, planning potential tax strategies, and evaluating the probability of |
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sustaining tax positions against audits. |
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sentences: |
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- What type of company is Johnson & Johnson described as? |
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- What determines the fair value of available-for-sale short-term investments? |
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- What factors influence the determination of income tax provisions and related |
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tax balances? |
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- source_sentence: During the fiscal year ended March 31, 2023, a $118 million tax |
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charge increased the valuation allowance on Swiss deferred tax assets, leading |
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to a higher effective tax rate. |
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sentences: |
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- What accounted for the significant tax rate increase in fiscal year 2023? |
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- What percentage of the box office revenue in the U.S./Canada was generated by |
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the three largest exhibitors in 2023? |
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- What percentage of eBay's 2023 net revenues were attributed to international markets? |
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--- |
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# BGE base Financial Matryoshka |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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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:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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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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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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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("ValentinaKim/bge-base-financial-matryoshka4") |
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# Run inference |
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sentences = [ |
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'During the fiscal year ended March 31, 2023, a $118 million tax charge increased the valuation allowance on Swiss deferred tax assets, leading to a higher effective tax rate.', |
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'What accounted for the significant tax rate increase in fiscal year 2023?', |
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'What percentage of the box office revenue in the U.S./Canada was generated by the three largest exhibitors in 2023?', |
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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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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, 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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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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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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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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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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## 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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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 46.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.35 tokens</li><li>max: 51 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>For the year ended December 31, 2023, net cash used in financing activities included $1.8 billion for dividends to GM, which are eliminated within the consolidated statements of cash flows.</code> | <code>What amount of dividends to GM were included in the net cash used in financing activities for GM Financial for the year ended December 31, 2023?</code> | |
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| <code>Assets and liabilities of these foreign entities are translated at exchange rates in effect as of the balance sheet date.</code> | <code>At what values are assets and liabilities of foreign entities translated in financial statements?</code> | |
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| <code>The 21st Century Cures Act broadened patient access to certain enhanced benefits offered by Medicare Advantage plans, increasing the percentage of patients on these plans.</code> | <code>How did the 21st Century Cures Act affect patient access to Medicare Advantage plans?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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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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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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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}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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