--- base_model: sentence-transformers/all-MiniLM-L6-v2 datasets: - youssefkhalil320/pairs_three_scores_v5 language: - en library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:80000003 - loss:CoSENTLoss widget: - source_sentence: durable pvc swim ring sentences: - flaky croissant - urban shoes - warm drinks mug - source_sentence: iso mak retard capsules sentences: - savory baguette - shea butter body cream - softwheeled cruiser - source_sentence: love sandra potty sentences: - utensil holder - olive pants - headwear - source_sentence: dusky hair brush sentences: - back compartment laptop - rubber feet platter - honed blade knife - source_sentence: nkd skn sentences: - fruit fragrances nail polish remover - panini salmon - hand drawing bag --- # all-MiniLM-L6-v8-pair_score 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) on the [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) dataset. 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 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) - **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': 256, 'do_lower_case': False}) with Transformer model: 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("sentence_transformers_model_id") # Run inference sentences = [ 'nkd skn', 'hand drawing bag', 'panini salmon', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### pairs_three_scores_v5 * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1) * Size: 80,000,003 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------------------|:---------------------------------------|:-----------------| | vanilla hair cream | free of paraben hair mask | 0.5 | | nourishing shampoo | cumin lemon tea | 0.0 | | safe materials pacifier | facial serum | 0.5 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### pairs_three_scores_v5 * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1) * Size: 20,000,001 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------|:-----------------------------------|:-----------------| | teddy bear toy | long lasting cat food | 0.0 | | eva hair treatment | fresh pineapple | 0.0 | | soft wave hair conditioner | hybrid seat bike | 0.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 0.0 - `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`: linear - `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`: 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`: 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} - `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`: 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 - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0002 | 100 | 10.8792 | | 0.0003 | 200 | 10.9284 | | 0.0005 | 300 | 10.6466 | | 0.0006 | 400 | 10.841 | | 0.0008 | 500 | 10.8094 | | 0.0010 | 600 | 10.4323 | | 0.0011 | 700 | 10.3032 | | 0.0013 | 800 | 10.4006 | | 0.0014 | 900 | 10.4743 | | 0.0016 | 1000 | 10.2334 | | 0.0018 | 1100 | 10.0135 | | 0.0019 | 1200 | 9.7874 | | 0.0021 | 1300 | 9.7419 | | 0.0022 | 1400 | 9.7412 | | 0.0024 | 1500 | 9.4585 | | 0.0026 | 1600 | 9.5339 | | 0.0027 | 1700 | 9.4345 | | 0.0029 | 1800 | 9.1733 | | 0.0030 | 1900 | 8.9952 | | 0.0032 | 2000 | 8.9669 | | 0.0034 | 2100 | 8.8152 | | 0.0035 | 2200 | 8.7936 | | 0.0037 | 2300 | 8.6771 | | 0.0038 | 2400 | 8.4648 | | 0.0040 | 2500 | 8.5764 | | 0.0042 | 2600 | 8.4587 | | 0.0043 | 2700 | 8.2966 | | 0.0045 | 2800 | 8.2329 | | 0.0046 | 2900 | 8.1415 | | 0.0048 | 3000 | 8.0404 | | 0.0050 | 3100 | 7.9698 | | 0.0051 | 3200 | 7.9205 | | 0.0053 | 3300 | 7.8314 | | 0.0054 | 3400 | 7.8369 | | 0.0056 | 3500 | 7.6403 | | 0.0058 | 3600 | 7.5842 | | 0.0059 | 3700 | 7.5812 | | 0.0061 | 3800 | 7.4335 | | 0.0062 | 3900 | 7.4917 | | 0.0064 | 4000 | 7.3204 | | 0.0066 | 4100 | 7.2971 | | 0.0067 | 4200 | 7.2233 | | 0.0069 | 4300 | 7.2081 | | 0.0070 | 4400 | 7.1364 | | 0.0072 | 4500 | 7.0663 | | 0.0074 | 4600 | 6.9601 | | 0.0075 | 4700 | 6.9546 | | 0.0077 | 4800 | 6.9019 | | 0.0078 | 4900 | 6.8801 | | 0.0080 | 5000 | 6.7734 | | 0.0082 | 5100 | 6.7648 | | 0.0083 | 5200 | 6.7498 | | 0.0085 | 5300 | 6.6872 | | 0.0086 | 5400 | 6.6264 | | 0.0088 | 5500 | 6.579 | | 0.0090 | 5600 | 6.6001 | | 0.0091 | 5700 | 6.5971 | | 0.0093 | 5800 | 6.4694 | | 0.0094 | 5900 | 6.3983 | | 0.0096 | 6000 | 6.4477 | | 0.0098 | 6100 | 6.4308 | | 0.0099 | 6200 | 6.4248 | | 0.0101 | 6300 | 6.2642 | | 0.0102 | 6400 | 6.2763 | | 0.0104 | 6500 | 6.3878 | | 0.0106 | 6600 | 6.2601 | | 0.0107 | 6700 | 6.1789 | | 0.0109 | 6800 | 6.1773 | | 0.0110 | 6900 | 6.1439 | | 0.0112 | 7000 | 6.1863 | | 0.0114 | 7100 | 6.0513 | | 0.0115 | 7200 | 6.0671 | | 0.0117 | 7300 | 6.0212 | | 0.0118 | 7400 | 6.0043 | | 0.0120 | 7500 | 6.0166 | | 0.0122 | 7600 | 5.9754 | | 0.0123 | 7700 | 5.9211 | | 0.0125 | 7800 | 5.7867 | | 0.0126 | 7900 | 5.8534 | | 0.0128 | 8000 | 5.7708 | | 0.0130 | 8100 | 5.8328 | | 0.0131 | 8200 | 5.7417 | | 0.0133 | 8300 | 5.8097 | | 0.0134 | 8400 | 5.7578 | | 0.0136 | 8500 | 5.643 | | 0.0138 | 8600 | 5.6401 | | 0.0139 | 8700 | 5.6627 | | 0.0141 | 8800 | 5.6167 | | 0.0142 | 8900 | 5.6539 | | 0.0144 | 9000 | 5.4513 | | 0.0146 | 9100 | 5.4132 | | 0.0147 | 9200 | 5.4714 | | 0.0149 | 9300 | 5.4786 | | 0.0150 | 9400 | 5.3928 | | 0.0152 | 9500 | 5.4774 | | 0.0154 | 9600 | 5.2881 | | 0.0155 | 9700 | 5.3699 | | 0.0157 | 9800 | 5.1483 | | 0.0158 | 9900 | 5.3051 | | 0.0160 | 10000 | 5.2546 | | 0.0162 | 10100 | 5.2314 | | 0.0163 | 10200 | 5.1783 | | 0.0165 | 10300 | 5.2074 | | 0.0166 | 10400 | 5.2825 | | 0.0168 | 10500 | 5.1715 | | 0.0170 | 10600 | 5.087 | | 0.0171 | 10700 | 5.082 | | 0.0173 | 10800 | 4.9111 | | 0.0174 | 10900 | 5.0213 | | 0.0176 | 11000 | 4.9898 | | 0.0178 | 11100 | 4.7734 | | 0.0179 | 11200 | 4.9511 | | 0.0181 | 11300 | 5.0481 | | 0.0182 | 11400 | 4.8441 | | 0.0184 | 11500 | 4.873 | | 0.0186 | 11600 | 4.9988 | | 0.0187 | 11700 | 4.7653 | | 0.0189 | 11800 | 4.804 | | 0.0190 | 11900 | 4.8288 | | 0.0192 | 12000 | 4.7053 | | 0.0194 | 12100 | 4.6887 | | 0.0195 | 12200 | 4.7832 | | 0.0197 | 12300 | 4.6817 | | 0.0198 | 12400 | 4.6252 | | 0.0200 | 12500 | 4.5936 | | 0.0202 | 12600 | 4.7452 | | 0.0203 | 12700 | 4.5321 | | 0.0205 | 12800 | 4.4964 | | 0.0206 | 12900 | 4.4421 | | 0.0208 | 13000 | 4.3782 | | 0.0210 | 13100 | 4.5169 | | 0.0211 | 13200 | 4.533 | | 0.0213 | 13300 | 4.3725 | | 0.0214 | 13400 | 4.2911 | | 0.0216 | 13500 | 4.2261 | | 0.0218 | 13600 | 4.2467 | | 0.0219 | 13700 | 4.1558 | | 0.0221 | 13800 | 4.2794 | | 0.0222 | 13900 | 4.2383 | | 0.0224 | 14000 | 4.1654 | | 0.0226 | 14100 | 4.158 | | 0.0227 | 14200 | 4.1299 | | 0.0229 | 14300 | 4.1902 | | 0.0230 | 14400 | 3.7853 | | 0.0232 | 14500 | 4.0514 | | 0.0234 | 14600 | 4.1655 | | 0.0235 | 14700 | 4.051 | | 0.0237 | 14800 | 4.078 | | 0.0238 | 14900 | 4.1193 | | 0.0240 | 15000 | 4.1536 | | 0.0242 | 15100 | 3.935 | | 0.0243 | 15200 | 3.9535 | | 0.0245 | 15300 | 3.7051 | | 0.0246 | 15400 | 3.8329 | | 0.0248 | 15500 | 3.9412 |
### Framework Versions - Python: 3.8.10 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.4.1+cu118 - Accelerate: 1.0.1 - Datasets: 3.0.1 - Tokenizers: 0.20.3 ## 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```