--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_ndcg@100 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9000 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: 個人向け資産事業の商品、能力、専門性を維持していくこともできるでしょう」 シティのプライベート・バンクは、世界で最も富裕な個人、家族、法律事務所向けに資産の 保護と責任ある蓄積を支援しています。シティ・プライベートバンクの顧客ビジネスの合計 は約 5,500 億ドルに上ります。1 万 3,000 を超える超富裕層のお客様にサービスを提供して おり、その中には� sentences: - How does the Citi Private Bank assist its clients and what is the total customer business of Citi Private Bank? - What are the effects of opening new card accounts for balance transfer? - What are some resources to learn about personal finance and credit? - source_sentence: "今後とも一層のお引立てを賜ります よう、お願い申し上げます。 ◆管理会社 ◆代行協会員 シティグループ・ファースト・ シティグループ証券株式会社\ \ インベストメント・マネジメント・リミテッド \n 目 次 頁 Ⅰ．運用の経過等 １ Ⅱ．直近10期の運用実績 ５ Ⅲ．ファンドの経理状況 ６ Ⅳ．お知らせ\ \ 36 （注１）米ドルの円換算額は、便宜上、2016年４月28日現在" sentences: - What are the specifications of Citi® Savings Account? - What are the fees for Citi Miles AheadSM Savings Account? - What are some regulations that might affect my use of your accounts and products? - source_sentence: 'antage® Miles earned from the Miles Boost do not count toward elite-status Exclusions qualification or AAdvantage Million MilerSM status. and Citi Miles Ahead Savings account owners will not earn a Miles Boost for: Restrictions • Purchases made using a different Eligible Card than the one associated with your Citi Miles Ahead Savings account; • Purchases appearing on an Eligible Card after the Eligible Card or Citi Miles Ahead Savings account closes; • Purchases appearing on an Eligible Card billing statement if the AMB in your Citi Miles Ahead Savings account was less than 10,000 for the calendar month preceding the Eligible Card billing statement date. For example, if your Eligible Card billing statement is dated July 10, and the AMB in your Citi Miles Ahead Savings account for the month of June was nine thousand ($9,000) dollars, you will not earn a Miles Boost for purchases appearing on that July 10 billing statement. • AAdvantage® Miles' sentences: - What impact will China's tech advancement have on global market? - What are the bonus miles requirements for Citi Miles Ahead Savings account? - What features are being phased out at Citibank ATMs between June 1 and June 23, 2023? - source_sentence: ' collateral movements as agreed Reinvestment of Cash money market funds by the parties. Citi has controls in Citi offers opportunities to reduce • Offers client-friendly dashboard for place to help prevent unauthorized service expenses through the one-stop access to balances and an movements of collateral. Earnings Credit Rate (ECR) Program interface for research and trading. or cash investment capabilities Tri-Party ACA Solutions through Citi Margin Manager. Where subject to an ACA, the pledgor Under a tri-party ACA with Citi, may be allowed to reinvest cash secured parties can choose to 1) allow Earning Credit Rate (ECR) Program collateral given secured party’s the pledgor to withdraw or replace With the ECR Program, Citi assists approval. collateral at their discretion or 2) clients with collateral accounts to require the pledgor to obtain approval earn credits on U.S. dollar deposits to • Displays portfolio information online for asset release. help offset services expenses. Citi’s and identifies eligible investments ECR' sentences: - In what situations is Citibank not liable to consumers under the agreement? - What services does Citi provide in relation to collateral margin management? - What are the changes in equity and reserves? - source_sentence: " be sure you had enough cash on hand to pay the fare. Channels,\ \ TTS Of course, all of that changed in the blink of an eye with the advent of\ \ ride sharing. Now getting from point A to point B is as easy as opening an app\ \ on your smart phone. Not only is it simple to book the ride, but once your account\ \ is set up, payment is absolutely seamless. No longer do you have to search your\ \ wallet for cash. The app knows who you are and the entire transaction happens\ \ in the background, without further input on the part of the rider or the driver.\ \ Payment is embedded in the experience as part of the natural flow, so you don’t\ \ have to think about it. \n 2 Treasury and Trade Solutions The invisible bank\ \ 3 This is just one example of the changes happening in today’s hyper-connected\ \ world. Artificial intelligence (AI), cloud is poised to deliver the “invisible\ \ bank,” where treasury and banking functions meld together. The continuous evolution\ \ of banking computing" sentences: - How is Mexico's credit rating affecting its economy? - How is a credit card introductory APR beneficial? - What is the advent of ride sharing? model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.049 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.115 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.15 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.205 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.049 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.03833333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0205 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.049 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.115 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.15 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.205 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.11801851461489118 name: Cosine Ndcg@10 - type: cosine_ndcg@100 value: 0.17325672881676993 name: Cosine Ndcg@100 - type: cosine_mrr@10 value: 0.09126269841269843 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1008423759256844 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("MugheesAwan11/bge-base-citi-dataset-9k-1k-e1") # Run inference sentences = [ ' be sure you had enough cash on hand to pay the fare. Channels, TTS Of course, all of that changed in the blink of an eye with the advent of ride sharing. Now getting from point A to point B is as easy as opening an app on your smart phone. Not only is it simple to book the ride, but once your account is set up, payment is absolutely seamless. No longer do you have to search your wallet for cash. The app knows who you are and the entire transaction happens in the background, without further input on the part of the rider or the driver. Payment is embedded in the experience as part of the natural flow, so you don’t have to think about it. \n 2 Treasury and Trade Solutions The invisible bank 3 This is just one example of the changes happening in today’s hyper-connected world. Artificial intelligence (AI), cloud is poised to deliver the “invisible bank,” where treasury and banking functions meld together. The continuous evolution of banking computing', 'What is the advent of ride sharing?', 'How is a credit card introductory APR beneficial?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.049 | | cosine_accuracy@3 | 0.115 | | cosine_accuracy@5 | 0.15 | | cosine_accuracy@10 | 0.205 | | cosine_precision@1 | 0.049 | | cosine_precision@3 | 0.0383 | | cosine_precision@5 | 0.03 | | cosine_precision@10 | 0.0205 | | cosine_recall@1 | 0.049 | | cosine_recall@3 | 0.115 | | cosine_recall@5 | 0.15 | | cosine_recall@10 | 0.205 | | cosine_ndcg@10 | 0.118 | | cosine_ndcg@100 | 0.1733 | | cosine_mrr@10 | 0.0913 | | **cosine_map@100** | **0.1008** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,000 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | ation. US 10-year Treasury yields have risen more than 30 basis points since the Sep 20 FOMC meeting, but just 5 basis points for 2-year notes, steepening the yield curve. Most importantly, the recent rise in rates was a “break out” for yields above post-COVID expansion highs (Figure 4). Figure 4: 10-year US Nominal Treasury yield and 10-year Inflation Indexed US Treasury Source: Haver Analytics as of September 28, 2023. Gray areas are recessions. Past performance is no guarantee of future results. Real results may vary. Citi Global Wealth Investments | CIO Strategy Bulletin | 3
So, why would rates jump dramatically with the Fed announcement? One reason is that price-insensitive Treasury buyers of the past 15 years are moving to the sidelines. The Fed has reduced US Treasury and mortgage-backed securities holdings by more than $1 trillion since starting quantitative tightening (QT) in | What is the return on average assets for Citigroup? | | extension How to Save Money Using the Citi Shop Extension | Citi.com Save money online shopping Save shopping online How to save money shopping online How to Save Money Shopping Online | Citi.com How to get coupons online How to get discounts online Online shopping best deals 11 Ways to Get Online Deals and Discounts | Citi.com Coupons browser extension Chrome extension for coupon codes Coupon extensions for chrome Google Chrome Browser Extensions to Help You Save Money | Citi.com benefits of shopping online why is shopping online better reasons to shop online Advantages of Online Shopping | Citi.com View All (5) View All Categories > Additional Resources • Insights and Tools Utilize these resources to help you assess your current finances & plan for the future. • FICO® Score Learn how FICO® Scores are determined, why they matter and more. • Glossary Review financial terms & definitions to help you better understand credit & finances. Back to Top Back to Top Equal housing lender Contact Us • Consumer: 1-800- | How can one redeem rewards for the Costco Anywhere Visa Card by Citi? | | common control of Citigroup. Outside the U.S., investment products and services are provided by other Citigroup affiliates. Investment Management services (including portfolio management) are available through CGMI, CGA, Citibank, N.A. and other affiliated advisory businesses. These Citigroup affiliates, including CGA, will be compensated for the respective investment management, advisory, administrative, distribution and placement services they may provide. International Personal Bank U.S. (“IPB U.S.”) is a business of Citigroup which provides its clients access to a broad array of products and services available through Citigroup, its bank and non-bank affiliates worldwide (collectively, “Citi”). Through IPB U.S. prospects and clients have access to the Citigold® Private Client International, Citigold® International, International Personal, Citi Global Executive Preferred, and Citi Global Executive Account Packages. Investment products and services are made available through Citi Personal Investments International (“CPII”), a business | What are the typical assumpitons given in the report? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768 ], "matryoshka_weights": [ 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_map@100 | |:-------:|:-------:|:-------------:|:----------------------:| | 0.0355 | 10 | 2.0527 | - | | 0.0709 | 20 | 2.3092 | - | | 0.1064 | 30 | 1.8688 | - | | 0.1418 | 40 | 1.8818 | - | | 0.1773 | 50 | 1.75 | - | | 0.2128 | 60 | 1.8462 | - | | 0.2482 | 70 | 1.7534 | - | | 0.2837 | 80 | 1.7534 | - | | 0.3191 | 90 | 1.7454 | - | | 0.3546 | 100 | 1.7037 | - | | 0.3901 | 110 | 1.6765 | - | | 0.4255 | 120 | 1.5392 | - | | 0.4610 | 130 | 1.722 | - | | 0.4965 | 140 | 1.5609 | - | | 0.5319 | 150 | 1.6001 | - | | 0.5674 | 160 | 1.5694 | - | | 0.6028 | 170 | 1.7528 | - | | 0.6383 | 180 | 1.5393 | - | | 0.6738 | 190 | 1.6765 | - | | 0.7092 | 200 | 1.4197 | - | | 0.7447 | 210 | 1.5231 | - | | 0.7801 | 220 | 1.7733 | - | | 0.8156 | 230 | 1.5464 | - | | 0.8511 | 240 | 1.5321 | - | | 0.8865 | 250 | 1.5727 | - | | 0.9220 | 260 | 1.5909 | - | | 0.9574 | 270 | 1.6485 | - | | 0.9929 | 280 | 1.6605 | - | | **1.0** | **282** | **-** | **0.1008** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```