| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - biencoder |
| - sentence-transformers |
| - text-classification |
| - sentence-pair-classification |
| - semantic-similarity |
| - semantic-search |
| - retrieval |
| - reranking |
| - generated_from_trainer |
| - loss:ArcFaceInBatchLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_precision@1 |
| - cosine_recall@1 |
| - cosine_ndcg@10 |
| - cosine_mrr@1 |
| - cosine_map@100 |
| - cosine_auc_precision_cache_hit_ratio |
| - cosine_auc_similarity_distribution |
| model-index: |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache |
| results: |
| - task: |
| type: custom-information-retrieval |
| name: Custom Information Retrieval |
| dataset: |
| name: test |
| type: test |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.5474394601032155 |
| name: Cosine Accuracy@1 |
| - type: cosine_precision@1 |
| value: 0.5474394601032155 |
| name: Cosine Precision@1 |
| - type: cosine_recall@1 |
| value: 0.5284894589479743 |
| name: Cosine Recall@1 |
| - type: cosine_ndcg@10 |
| value: 0.7464232866184599 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@1 |
| value: 0.5474394601032155 |
| name: Cosine Mrr@1 |
| - type: cosine_map@100 |
| value: 0.6905199963377163 |
| name: Cosine Map@100 |
| - type: cosine_auc_precision_cache_hit_ratio |
| value: 0.31524254043885996 |
| name: Cosine Auc Precision Cache Hit Ratio |
| - type: cosine_auc_similarity_distribution |
| value: 0.16089488030492544 |
| name: Cosine Auc Similarity Distribution |
| --- |
| |
| # Redis fine-tuned BiEncoder model for semantic caching on LangCache |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for sentence pair similarity. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("redis/langcache-embed-v3-mini") |
| # Run inference |
| sentences = [ |
| 'The weather is lovely today.', |
| "It's so sunny outside!", |
| 'He drove to the stadium.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.6650, 0.1040], |
| # [0.6650, 1.0000, 0.1401], |
| # [0.1040, 0.1401, 0.9999]]) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Custom Information Retrieval |
|
|
| * Dataset: `test` |
| * Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code> with these parameters: |
| ```json |
| { |
| "query_prompt": "query:", |
| "corpus_prompt": "query:" |
| } |
| ``` |
| |
| | Metric | Value | |
| |:-------------------------------------|:-----------| |
| | cosine_accuracy@1 | 0.5474 | |
| | cosine_precision@1 | 0.5474 | |
| | cosine_recall@1 | 0.5285 | |
| | **cosine_ndcg@10** | **0.7464** | |
| | cosine_mrr@1 | 0.5474 | |
| | cosine_map@100 | 0.6905 | |
| | cosine_auc_precision_cache_hit_ratio | 0.3152 | |
| | cosine_auc_similarity_distribution | 0.1609 | |
| |
| <!-- |
| ## 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 |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `weight_decay`: 0.001 |
| - `adam_beta2`: 0.98 |
| - `adam_epsilon`: 1e-06 |
| - `max_steps`: 100000 |
| - `warmup_ratio`: 0.15 |
| - `bf16`: True |
| - `load_best_model_at_end`: True |
| - `ddp_find_unused_parameters`: False |
| - `push_to_hub`: True |
| - `hub_model_id`: redis/langcache-embed-v3-mini |
| - `eval_on_start`: True |
| - `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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 5e-05 |
| - `weight_decay`: 0.001 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.98 |
| - `adam_epsilon`: 1e-06 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 3.0 |
| - `max_steps`: 100000 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.15 |
| - `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 |
| - `bf16`: True |
| - `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`: 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} |
| - `parallelism_config`: 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 |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `ddp_find_unused_parameters`: False |
| - `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`: True |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: redis/langcache-embed-v3-mini |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `fp16_backend`: auto |
| - `push_to_hub_model_id`: None |
| - `push_to_hub_organization`: None |
| - `mp_parameters`: |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `torchdynamo`: None |
| - `ray_scope`: last |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: True |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | test_cosine_ndcg@10 | |
| |:-----:|:----:|:-------------------:| |
| | 0 | 0 | 0.7464 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.3 |
| - Sentence Transformers: 5.1.1 |
| - Transformers: 4.57.0 |
| - PyTorch: 2.8.0+cu128 |
| - Accelerate: 1.10.1 |
| - Datasets: 4.1.1 |
| - Tokenizers: 0.22.1 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
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| *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.* |
| --> |
|
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| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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