--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:554030 - loss:MultipleNegativesSymmetricRankingLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: pacman smoked turkey sentences: - omelette with fresh basil & cherry tomatoes - mozzarella pacman - ' tote ' - source_sentence: mfk 140 static kite - pulpy sentences: - kite for young children - 'leather wrap skirt available in two colors white and black. outside materials: leather.' - adult long-sleeved thermal football base layer top keepcomfort 100 - black - source_sentence: large zk diffuser - pack 7 sentences: - ' wrap' - zk diffuser - leo - source_sentence: emerald green double-face drape pajama (short pants) sentences: - fiber cushion - 'the double-faced design pajama of the fabric ensures that both sides have a glossy finish, providing a stunning look and feel. inside and outside material: double face satin' - sky blue seashell set - source_sentence: to - do - dahab sentences: - notebook ruled glue binding soft cover 14.2 x 20.8 cm 160 sheets 80 gsm leather cover heeton no a25-835 - ' notebook' - ' advance repair lotion' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9594950079917908 name: Cosine Accuracy --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## 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) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### 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({'max_seq_length': 256, '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("LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine") # Run inference sentences = [ 'to - do - dahab', ' notebook', 'notebook ruled glue binding soft cover 14.2 x 20.8 cm 160 sheets 80 gsm leather cover heeton no a25-835', ] 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, 0.3069, 0.3096], # [0.3069, 1.0000, 0.7117], # [0.3096, 0.7117, 1.0000]]) ``` ## Evaluation ### Metrics #### Triplet * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9595** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 554,030 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------|:------------------------------------| | grass fed butter basbousa | coconut flour basbousa | | silver printer tape | printer labels | | top | charcoal tee | * Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 9,505 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------|:-----------------------------------------|:--------------------------------------------------------------------------| | pilot mechanical pencil progrex h-127 - 0.7 mm | progrex pencil | canvas frame 100% cotton 380 gsm 2040 cm rectangular m e5305 | | superior drawing marker -pen - set of 12 colors - 2 nib | marker pen | blue to-do list | | first person singular author: haruki murakami | first person singular book | sesame street 5-minute stories | * Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 2e-05 - `weight_decay`: 0.001 - `num_train_epochs`: 5 - `warmup_steps`: 1082 - `fp16`: True - `dataloader_num_workers`: 1 - `dataloader_prefetch_factor`: 2 - `dataloader_persistent_workers`: True - `push_to_hub`: True - `hub_model_id`: LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `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`: 2e-05 - `weight_decay`: 0.001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0 - `warmup_steps`: 1082 - `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`: True - `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`: 1 - `dataloader_prefetch_factor`: 2 - `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} - `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`: True - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: LamaDiab/MiniLM-V9Data-256BATCH-SemanticEngine - `hub_strategy`: all_checkpoints - `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`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | |:------:|:-----:|:-------------:|:---------------:|:---------------:| | 0.0005 | 1 | 3.6183 | - | - | | 0.4619 | 1000 | - | 1.2476 | 0.9518 | | 0.9238 | 2000 | - | 1.1979 | 0.9533 | | 1.0 | 2165 | 2.4107 | - | - | | 1.3857 | 3000 | - | 1.1493 | 0.9572 | | 1.8476 | 4000 | - | 1.1210 | 0.9597 | | 2.0 | 4330 | 1.8868 | - | - | | 2.3095 | 5000 | - | 1.1083 | 0.9584 | | 2.7714 | 6000 | - | 1.0890 | 0.9596 | | 3.0 | 6495 | 1.7202 | - | - | | 3.2333 | 7000 | - | 1.0836 | 0.9591 | | 3.6952 | 8000 | - | 1.0752 | 0.9600 | | 4.0 | 8660 | 1.6355 | - | - | | 4.1570 | 9000 | - | 1.0678 | 0.9590 | | 4.6189 | 10000 | - | 1.0703 | 0.9595 | | 5.0 | 10825 | 1.5986 | - | - | ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.1.2 - Transformers: 4.53.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.9.0 - Datasets: 4.4.1 - Tokenizers: 0.21.2 ## 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", } ```