--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:346930 - loss:MultipleNegativesRankingLoss widget: - source_sentence: U card sentences: - region - clique - tokens - source_sentence: branch sentences: - block Half - bool Code - schedule - source_sentence: pair le sentences: - trans E - endpoint - selected - source_sentence: loop Trace sentences: - blocks - potential - Is Blowup - source_sentence: hit weight sentences: - machine - config - append pipeline_tag: sentence-similarity library_name: sentence-transformers 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_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer results: - task: type: information-retrieval name: Information Retrieval dataset: name: val ir type: val_ir metrics: - type: cosine_accuracy@1 value: 0.2305 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.3385 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.386 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.426 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2305 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.11283333333333331 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07719999999999999 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0426 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2305 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3385 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.386 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.426 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3258681126672316 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2940464285714284 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3016717487640095 name: Cosine Map@100 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Maximum Sequence Length:** 1000000000000000019884624838656 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modalities:** Text, Image, Audio, Video, Message ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'audio': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'architecture': 'EmbeddingGemma2Model'}) (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True}) (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'}) ) ``` ## 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("sentence_transformers_model_id") # Run inference queries = [ 'resolution Cokernel Map comp', ] documents = [ 'resolution Cokernel Map', 'potential Of', 'canonical exposure supported', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.8557, 0.3572, 0.2455]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `val_ir` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2305 | | cosine_accuracy@3 | 0.3385 | | cosine_accuracy@5 | 0.386 | | cosine_accuracy@10 | 0.426 | | cosine_precision@1 | 0.2305 | | cosine_precision@3 | 0.1128 | | cosine_precision@5 | 0.0772 | | cosine_precision@10 | 0.0426 | | cosine_recall@1 | 0.2305 | | cosine_recall@3 | 0.3385 | | cosine_recall@5 | 0.386 | | cosine_recall@10 | 0.426 | | **cosine_ndcg@10** | **0.3259** | | cosine_mrr@10 | 0.294 | | cosine_map@100 | 0.3017 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 346,930 training samples * Columns: anchor and positive * Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | anchor | positive | |:-----------------------------------------------------------|:------------------------------------------| | resolution Cokernel Map comp | resolution Cokernel Map | | potential Of continuous | potential Of | | sum fully exposed code success probability le | canonical exposure supported | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 256 - `num_train_epochs`: 2 - `warmup_steps`: 200 - `bf16`: True #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 256 - `num_train_epochs`: 2 - `max_steps`: -1 - `learning_rate`: 5e-05 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_steps`: 200 - `optim`: adamw_torch_fused - `optim_args`: None - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `optim_target_modules`: None - `gradient_accumulation_steps`: 1 - `average_tokens_across_devices`: True - `max_grad_norm`: 1.0 - `label_smoothing_factor`: 0.0 - `bf16`: True - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `use_liger_kernel`: False - `liger_kernel_config`: None - `use_cache`: False - `neftune_noise_alpha`: None - `torch_empty_cache_steps`: None - `auto_find_batch_size`: False - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `include_num_input_tokens_seen`: no - `log_level`: passive - `log_level_replica`: warning - `disable_tqdm`: False - `project`: huggingface - `trackio_space_id`: None - `trackio_bucket_id`: None - `trackio_static_space_id`: None - `per_device_eval_batch_size`: 8 - `prediction_loss_only`: True - `eval_on_start`: False - `eval_do_concat_batches`: True - `eval_use_gather_object`: False - `eval_accumulation_steps`: None - `include_for_metrics`: [] - `batch_eval_metrics`: False - `save_only_model`: False - `save_on_each_node`: False - `enable_jit_checkpoint`: False - `push_to_hub`: False - `hub_private_repo`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_always_push`: False - `hub_revision`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `restore_callback_states_from_checkpoint`: False - `full_determinism`: False - `seed`: 42 - `data_seed`: None - `use_cpu`: False - `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 - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `dataloader_prefetch_factor`: None - `dataloader_multiprocessing_context`: None - `dataloader_in_order`: True - `remove_unused_columns`: True - `label_names`: None - `train_sampling_strategy`: random - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `ddp_static_graph`: None - `ddp_backend`: None - `ddp_timeout`: 1800 - `fsdp`: None - `fsdp_config`: None - `deepspeed`: None - `debug`: [] - `skip_memory_metrics`: True - `do_predict`: False - `resume_from_checkpoint`: None - `local_rank`: -1 - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} - `warmup_ratio`: None
### Training Logs | Epoch | Step | Training Loss | val_ir_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------------:| | 0.0737 | 100 | 3.5315 | - | | 0.1475 | 200 | 2.8227 | - | | 0.2212 | 300 | 2.6758 | - | | 0.2950 | 400 | 2.5389 | - | | 0.3687 | 500 | 2.4665 | - | | 0.4425 | 600 | 2.3844 | - | | 0.5162 | 700 | 2.3230 | - | | 0.5900 | 800 | 2.2864 | - | | 0.6637 | 900 | 2.2415 | - | | 0.7375 | 1000 | 2.1594 | 0.3263 | | 0.8112 | 1100 | 2.1646 | - | | 0.8850 | 1200 | 2.1441 | - | | 0.9587 | 1300 | 2.1340 | - | | 1.0324 | 1400 | 1.9848 | - | | 1.1062 | 1500 | 1.8459 | - | | 1.1799 | 1600 | 1.8273 | - | | 1.2537 | 1700 | 1.8094 | - | | 1.3274 | 1800 | 1.7973 | - | | 1.4012 | 1900 | 1.8163 | - | | 1.4749 | 2000 | 1.7998 | 0.3268 | | 1.5487 | 2100 | 1.8070 | - | | 1.6224 | 2200 | 1.8098 | - | | 1.6962 | 2300 | 1.7844 | - | | 1.7699 | 2400 | 1.7933 | - | | 1.8437 | 2500 | 1.7804 | - | | 1.9174 | 2600 | 1.7810 | - | | 1.9912 | 2700 | 1.7693 | - | | 2.0 | 2712 | - | 0.3259 | ### Training Time - **Training**: 43.3 minutes - **Evaluation**: 2.0 minutes - **Total**: 45.3 minutes ### Framework Versions - Python: 3.13.2 - Sentence Transformers: 6.1.0 - Transformers: 5.19.0 - PyTorch: 2.11.0+cu128 - Accelerate: 1.15.0 - Datasets: 5.1.0 - Tokenizers: 0.23.3 ## Additional Resources - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```