--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:CosineSimilarityLoss metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: A man is spitting. sentences: - A man seasoning quail. - A brown horse in a green field. - A woman is playing the guitar. - source_sentence: A woman is reading. sentences: - A woman is slicing carrot. - The man is hiking in the woods. - A man is singing and playing a guitar. - source_sentence: A woman is dancing. sentences: - A woman is dancing in railway station. - A doctor prescribes a medicine. - The man is riding a horse. - source_sentence: Women are running. sentences: - Women are running. - A woman is applying eye shadow. - A woman and man are riding in a car. - source_sentence: A cat is on a robot. sentences: - A cat is pouncing on a trampoline. - A woman is applying eye shadow. - A woman and man are riding in a car. pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 0.11798947049821952 energy_consumed: 0.0003035473717609365 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: 0.002 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7327950331192871 name: Pearson Cosine - type: spearman_cosine value: 0.733720742976967 name: Spearman Cosine - type: pearson_manhattan value: 0.5141829243804352 name: Pearson Manhattan - type: spearman_manhattan value: 0.5088476055041519 name: Spearman Manhattan - type: pearson_euclidean value: 0.5143122485153392 name: Pearson Euclidean - type: spearman_euclidean value: 0.5094438567737941 name: Spearman Euclidean - type: pearson_dot value: 0.5691313208318369 name: Pearson Dot - type: spearman_dot value: 0.6686075432867175 name: Spearman Dot - type: pearson_max value: 0.7327950331192871 name: Pearson Max - type: spearman_max value: 0.733720742976967 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.6515536111902664 name: Pearson Cosine - type: spearman_cosine value: 0.6357551120651417 name: Spearman Cosine - type: pearson_manhattan value: 0.4104283118123022 name: Pearson Manhattan - type: spearman_manhattan value: 0.4057805136887886 name: Spearman Manhattan - type: pearson_euclidean value: 0.4116066558734167 name: Pearson Euclidean - type: spearman_euclidean value: 0.40663312273612934 name: Spearman Euclidean - type: pearson_dot value: 0.4717437134789646 name: Pearson Dot - type: spearman_dot value: 0.5536656048436931 name: Spearman Dot - type: pearson_max value: 0.6515536111902664 name: Pearson Max - type: spearman_max value: 0.6357551120651417 name: Spearman Max --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 512-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 - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** 512 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) - **Language:** en ### 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): BoW() (1): Dense({'in_features': 25000, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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("tomaarsen/wikipedia-tf-idf-bow") # Run inference sentences = [ 'A cat is on a robot.', 'A cat is pouncing on a trampoline.', 'A woman is applying eye shadow.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 512] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7328 | | **spearman_cosine** | **0.7337** | | pearson_manhattan | 0.5142 | | spearman_manhattan | 0.5088 | | pearson_euclidean | 0.5143 | | spearman_euclidean | 0.5094 | | pearson_dot | 0.5691 | | spearman_dot | 0.6686 | | pearson_max | 0.7328 | | spearman_max | 0.7337 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6516 | | **spearman_cosine** | **0.6358** | | pearson_manhattan | 0.4104 | | spearman_manhattan | 0.4058 | | pearson_euclidean | 0.4116 | | spearman_euclidean | 0.4066 | | pearson_dot | 0.4717 | | spearman_dot | 0.5537 | | pearson_max | 0.6516 | | spearman_max | 0.6358 | ## Training Details ### Training Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) * Size: 5,749 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 | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| | A plane is taking off. | An air plane is taking off. | 1.0 | | A man is playing a large flute. | A man is playing a flute. | 0.76 | | A man is spreading shreded cheese on a pizza. | A man is spreading shredded cheese on an uncooked pizza. | 0.76 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) * Size: 1,500 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 | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | A man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | | A young child is riding a horse. | A child is riding a horse. | 0.95 | | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: False - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-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 - `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`: None - `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_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.5556 | 100 | 0.0725 | 0.0436 | 0.7337 | - | | 1.0 | 180 | - | - | - | 0.6358 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.000 kWh - **Carbon Emitted**: 0.000 kg of CO2 - **Hours Used**: 0.002 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: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - 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", } ```