--- base_model: prajjwal1/bert-tiny datasets: [] language: [] library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:277277 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Tall man being stopped by an officer. sentences: - The man is short. - There is a tall man. - Male in brown leather jacket and tight black slacks, looking down at his phone - source_sentence: Man relaxing on a bench at the bus stop. sentences: - The man stood next to the bench. - The man relaxes on a bench. - A dog running outside. - source_sentence: Police officer with riot shield stands in front of crowd. sentences: - A police officer teaches two children something. - The kid is at the beach. - A police officer stands in front of a crowd. - source_sentence: A woman in a red shirt and blue jeans is walking outside while a man in a khaki jacket is right behind her. sentences: - A man and a woman are walking outside. - A woman is outside. - A man in an army jacket is following a woman in a pink dress. - source_sentence: A waitress with a pink shirt and black pants walking through a restaurant carrying bowls of soup. sentences: - Nobody has pants - A person with pants - a young kid jumps into the water co2_eq_emissions: emissions: 1.9590621986924506 energy_consumed: 0.005040010596015587 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.029 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on prajjwal1/bert-tiny results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7526013757467193 name: Pearson Cosine - type: spearman_cosine value: 0.7614153421868329 name: Spearman Cosine - type: pearson_manhattan value: 0.7622035611835871 name: Pearson Manhattan - type: spearman_manhattan value: 0.7597498090089608 name: Spearman Manhattan - type: pearson_euclidean value: 0.7632410201154781 name: Pearson Euclidean - type: spearman_euclidean value: 0.7614153421868329 name: Spearman Euclidean - type: pearson_dot value: 0.7526013835604672 name: Pearson Dot - type: spearman_dot value: 0.7614153421868329 name: Spearman Dot - type: pearson_max value: 0.7632410201154781 name: Pearson Max - type: spearman_max value: 0.7614153421868329 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.69132863091579 name: Pearson Cosine - type: spearman_cosine value: 0.6775246001958918 name: Spearman Cosine - type: pearson_manhattan value: 0.6993315331718462 name: Pearson Manhattan - type: spearman_manhattan value: 0.6760860789893309 name: Spearman Manhattan - type: pearson_euclidean value: 0.7005700491110102 name: Pearson Euclidean - type: spearman_euclidean value: 0.6775246001958918 name: Spearman Euclidean - type: pearson_dot value: 0.6913286275793098 name: Pearson Dot - type: spearman_dot value: 0.6775246001958918 name: Spearman Dot - type: pearson_max value: 0.7005700491110102 name: Pearson Max - type: spearman_max value: 0.6775246001958918 name: Spearman Max --- # SentenceTransformer based on prajjwal1/bert-tiny This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny). It maps sentences & paragraphs to a 256-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:** [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 256 tokens - **Similarity Function:** Cosine Similarity ### 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': 384, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 128, '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): Dense({'in_features': 128, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): 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-testing/all-nli-bert-tiny-dense") # Run inference sentences = [ 'A waitress with a pink shirt and black pants walking through a restaurant carrying bowls of soup.', 'A person with pants', 'Nobody has pants', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 256] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7526 | | **spearman_cosine** | **0.7614** | | pearson_manhattan | 0.7622 | | spearman_manhattan | 0.7597 | | pearson_euclidean | 0.7632 | | spearman_euclidean | 0.7614 | | pearson_dot | 0.7526 | | spearman_dot | 0.7614 | | pearson_max | 0.7632 | | spearman_max | 0.7614 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6913 | | **spearman_cosine** | **0.6775** | | pearson_manhattan | 0.6993 | | spearman_manhattan | 0.6761 | | pearson_euclidean | 0.7006 | | spearman_euclidean | 0.6775 | | pearson_dot | 0.6913 | | spearman_dot | 0.6775 | | pearson_max | 0.7006 | | spearman_max | 0.6775 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 277,277 training 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 | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | | Children smiling and waving at camera | There are children present | The kids are frowning | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 5,875 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: 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`: 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`: 0.0 - `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`: 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`: 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 - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.0923 | 100 | 3.4021 | 2.1678 | 0.7247 | - | | 0.1845 | 200 | 2.3398 | 1.7482 | 0.7480 | - | | 0.2768 | 300 | 2.0893 | 1.6365 | 0.7537 | - | | 0.3690 | 400 | 2.0035 | 1.5782 | 0.7552 | - | | 0.4613 | 500 | 1.9023 | 1.5376 | 0.7587 | - | | 0.5535 | 600 | 1.8647 | 1.5059 | 0.7597 | - | | 0.6458 | 700 | 1.8511 | 1.4836 | 0.7605 | - | | 0.7380 | 800 | 1.8094 | 1.4698 | 0.7613 | - | | 0.8303 | 900 | 1.8338 | 1.4593 | 0.7609 | - | | 0.9225 | 1000 | 1.7951 | 1.4553 | 0.7614 | - | | 1.0 | 1084 | - | - | - | 0.6775 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.005 kWh - **Carbon Emitted**: 0.002 kg of CO2 - **Hours Used**: 0.029 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.1.0.dev0 - Transformers: 4.43.4 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```