| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:36864 |
| - loss:MatryoshkaLoss |
| - loss:CachedMultipleNegativesRankingLoss |
| base_model: redis/langcache-embed-v1 |
| widget: |
| - source_sentence: What are civil cases and what are some examples? |
| sentences: |
| - What are criminal cases and what are no examples? |
| - Civil cases involve disputes between individuals or organizations, typically seeking |
| monetary compensation or specific performance, and *do not* include criminal prosecutions |
| by the government. |
| - Criminal cases involve disputes between individuals or organizations, seeking |
| monetary damages or specific performance, while civil cases concern offenses against |
| the state punishable by imprisonment. |
| - What are some examples of civil cases? |
| - source_sentence: How can you stop your palms from sweating? |
| sentences: |
| - How do I stop my palms from sweating a lot at random times? |
| - How can you *make* your palms sweat? |
| - How can you *cause* your palms to sweat? |
| - How can you start your palms from sweating? |
| - source_sentence: What are the pros and cons of wind turbines? |
| sentences: |
| - What are the pros and cons of solar panels? |
| - What are the cons and pros of solar panels? |
| - What are pros and cons of wind turbines? |
| - Wind turbines have no advantages or disadvantages. |
| - source_sentence: Will Obamacare be repealed now that trump won? |
| sentences: |
| - Despite Trump's victory, Obamacare remains largely intact and has not been fully |
| repealed. |
| - Despite Trump's repeated promises to repeal and replace the Affordable Care Act |
| (ACA), often called Obamacare, it remains the law of the land. Numerous attempts |
| to repeal or significantly alter the ACA failed during his presidency due to Congressional |
| opposition. |
| - Will Obamacare be repealed now that Biden won? |
| - Will Obamacare be repealed / shut down soon? |
| - source_sentence: What are some examples of crimes understood as a moral turpitude? |
| sentences: |
| - What actions are *not* generally considered crimes involving moral turpitude? |
| - What are some examples of crimes understood as a legal aptitude? |
| - What are some examples of crimes understood as a legal turpitude? |
| - What are some examples of crimes of moral turpitude? |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on redis/langcache-embed-v1 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) on the triplet 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. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) <!-- at revision 80fb95b5478a6b6d068faf4452faa2f5bc9f0dfa --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - triplet |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
| (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}) |
| ) |
| ``` |
|
|
| ## 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("redis/langcache-embed-v2") |
| # Run inference |
| sentences = [ |
| 'What are some examples of crimes understood as a moral turpitude?', |
| 'What are some examples of crimes of moral turpitude?', |
| 'What are some examples of crimes understood as a legal aptitude?', |
| ] |
| 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.* |
| --> |
|
|
| <!-- |
| ## 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.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| * Dataset: triplet |
| * Size: 36,864 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>negative_3</code> |
| <!-- * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative_1 | negative_2 | negative_3 | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 13.88 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.68 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.26 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.07 tokens</li><li>max: 108 tokens</li></ul> | --> |
| * Samples: |
| | anchor | positive | negative_1 | negative_2 | negative_3 | |
| |:---------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Is life really what I make of it?</code> | <code>Life is what you make it?</code> | <code>Is life hardly what I take of it?</code> | <code>Life is not entirely what I make of it.</code> | <code>Is life not what I make of it?</code> | |
| | <code>When you visit a website, can a person running the website see your IP address?</code> | <code>Does every website I visit knows my public ip address?</code> | <code>When you avoid a website, can a person hiding the website see your MAC address?</code> | <code>When you send an email, can the recipient see your physical location?</code> | <code>When you visit a website, a person running the website cannot see your IP address.</code> | |
| | <code>What are some cool features about iOS 10?</code> | <code>What are the best new features of iOS 10?</code> | <code>iOS 10 received criticism for its initial bugs and performance issues, and some users found the redesigned apps less intuitive compared to previous versions.</code> | <code>What are the drawbacks of using Android 14?</code> | <code>iOS 10 was widely criticized for its bugs, removal of beloved features, and generally being a downgrade from previous versions.</code> | |
| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| ```json |
| { |
| "loss": "CachedMultipleNegativesRankingLoss", |
| "matryoshka_dims": [768,512,256,128,64], |
| "matryoshka_weights": [1,1,1,1,1], |
| "n_dims_per_step": -1 |
| } |
| ``` |
| |
| ### Evaluation |
|
|
|  |
|  |
|  |
|  |
|
|
|
|
| <!-- ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 2048 |
| - `per_device_eval_batch_size`: 1024 |
| - `learning_rate`: 1e-05 |
| - `num_train_epochs`: 1 |
| - `lr_scheduler_type`: constant |
| - `warmup_steps`: 10 |
| - `gradient_checkpointing`: True |
| - `torch_compile`: True |
| - `torch_compile_backend`: inductor |
| - `batch_sampler`: no_duplicates --> |
| |
| <!-- #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 2048 |
| - `per_device_eval_batch_size`: 1024 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 1e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: constant |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `warmup_steps`: 10 |
| - `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`: False |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `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`: False |
| - `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} |
| - `tp_size`: 0 |
| - `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 |
| - `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`: True |
| - `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`: True |
| - `torch_compile_backend`: inductor |
| - `torch_compile_mode`: 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 | triplet loss | |
| |:------:|:----:|:-------------:|:------------:| |
| | 0.0556 | 1 | 6.4636 | - | |
| | 0.1111 | 2 | 6.1076 | - | |
| | 0.1667 | 3 | 5.8323 | - | |
| | 0.2222 | 4 | 5.6861 | - | |
| | 0.2778 | 5 | 5.5694 | - | |
| | 0.3333 | 6 | 5.2121 | - | |
| | 0.3889 | 7 | 5.0695 | - | |
| | 0.4444 | 8 | 4.81 | - | |
| | 0.5 | 9 | 4.6698 | - | |
| | 0.5556 | 10 | 4.3546 | 1.2224 | |
| | 0.6111 | 11 | 4.1922 | - | |
| | 0.6667 | 12 | 4.1434 | - | |
| | 0.7222 | 13 | 3.9918 | - | |
| | 0.7778 | 14 | 3.702 | - | |
| | 0.8333 | 15 | 3.6501 | - | |
| | 0.8889 | 16 | 3.6641 | - | |
| | 0.9444 | 17 | 3.3196 | - | |
| | 1.0 | 18 | 2.7108 | - | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.11.11 |
| - Sentence Transformers: 4.1.0 |
| - Transformers: 4.51.3 |
| - PyTorch: 2.6.0+cu124 |
| - Accelerate: 1.6.0 |
| - Datasets: 3.5.1 |
| - Tokenizers: 0.21.1 --> |
|
|
| ## Citation |
|
|
|
|
|
|
| #### Redis Langcache-embed Models |
|
|
| We encourage you to cite our work if you use our models or build upon our findings. |
|
|
| ```bibtex |
| @inproceedings{langcache-embed-v1, |
| title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data", |
| author = "Gill, Cechmanek, Hutcherson, Rajamohan, Agarwal, Gulzar, Singh, Dion", |
| month = "04", |
| year = "2025", |
| url = "https://arxiv.org/abs/2504.02268", |
| } |
| ``` |
|
|
|
|
|
|
| #### 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", |
| } |
| |
| @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} |
| } |
| |
| @misc{gao2021scaling, |
| title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
| author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
| year={2021}, |
| eprint={2101.06983}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
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| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
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| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
