--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10000 - loss:SoftmaxLoss base_model: google-bert/bert-base-uncased datasets: [] 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 selling donuts to a customer during a world exhibition event held in the city of Angeles sentences: - The man is doing tricks. - A woman drinks her coffee in a small cafe. - The building is made of logs. - source_sentence: A group of people prepare hot air balloons for takeoff. sentences: - There are hot air balloons on the ground and air. - A man is in an art museum. - People watch another person do a trick. - source_sentence: Three workers are trimming down trees. sentences: - The goalie is sleeping at home. - There are three workers - The girl has brown hair. - source_sentence: Two brown-haired men wearing short-sleeved shirts and shorts are climbing stairs. sentences: - The men have blonde hair. - A bicyclist passes an esthetically beautiful building on a sunny day - Two men are dancing. - source_sentence: A man is sitting in on the side of the street with brass pots. sentences: - a younger boy looks at his father - Children are at the beach. - a man does not have brass pots pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 147.28843774992524 energy_consumed: 0.2758298255748315 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: AMD EPYC 7H12 64-Core Processor ram_total_size: 229.14864349365234 hours_used: 0.351 hardware_used: 8 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on google-bert/bert-base-uncased results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.47725003430658275 name: Pearson Cosine - type: spearman_cosine value: 0.5475746919034576 name: Spearman Cosine - type: pearson_manhattan value: 0.5043805022296893 name: Pearson Manhattan - type: spearman_manhattan value: 0.5420702830995872 name: Spearman Manhattan - type: pearson_euclidean value: 0.5083739540394052 name: Pearson Euclidean - type: spearman_euclidean value: 0.544209699690841 name: Spearman Euclidean - type: pearson_dot value: 0.4458579859528435 name: Pearson Dot - type: spearman_dot value: 0.4698642508787034 name: Spearman Dot - type: pearson_max value: 0.5083739540394052 name: Pearson Max - type: spearman_max value: 0.5475746919034576 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.5320947494943107 name: Pearson Cosine - type: spearman_cosine value: 0.5317279446221387 name: Spearman Cosine - type: pearson_manhattan value: 0.5575308236485216 name: Pearson Manhattan - type: spearman_manhattan value: 0.5554390408837996 name: Spearman Manhattan - type: pearson_euclidean value: 0.55587770863865 name: Pearson Euclidean - type: spearman_euclidean value: 0.5535804159700501 name: Spearman Euclidean - type: pearson_dot value: 0.2787697886285483 name: Pearson Dot - type: spearman_dot value: 0.2710358104528421 name: Spearman Dot - type: pearson_max value: 0.5575308236485216 name: Pearson Max - type: spearman_max value: 0.5554390408837996 name: Spearman Max - type: pearson_cosine value: 0.4493844540252116 name: Pearson Cosine - type: spearman_cosine value: 0.4694611677633312 name: Spearman Cosine - type: pearson_manhattan value: 0.4773641092320219 name: Pearson Manhattan - type: spearman_manhattan value: 0.4763054309792941 name: Spearman Manhattan - type: pearson_euclidean value: 0.4796801942910325 name: Pearson Euclidean - type: spearman_euclidean value: 0.47774521406648734 name: Spearman Euclidean - type: pearson_dot value: 0.4081600817978359 name: Pearson Dot - type: spearman_dot value: 0.3898881150281674 name: Spearman Dot - type: pearson_max value: 0.4796801942910325 name: Pearson Max - type: spearman_max value: 0.47774521406648734 name: Spearman Max --- # SentenceTransformer based on google-bert/bert-base-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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}) ) ``` ## 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("jilangdi/bert-base-uncased-nli-v1") # Run inference sentences = [ 'A man is sitting in on the side of the street with brass pots.', 'a man does not have brass pots', 'Children are at the beach.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # 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 | 0.4773 | | **spearman_cosine** | **0.5476** | | pearson_manhattan | 0.5044 | | spearman_manhattan | 0.5421 | | pearson_euclidean | 0.5084 | | spearman_euclidean | 0.5442 | | pearson_dot | 0.4459 | | spearman_dot | 0.4699 | | pearson_max | 0.5084 | | spearman_max | 0.5476 | #### 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 | 0.5321 | | **spearman_cosine** | **0.5317** | | pearson_manhattan | 0.5575 | | spearman_manhattan | 0.5554 | | pearson_euclidean | 0.5559 | | spearman_euclidean | 0.5536 | | pearson_dot | 0.2788 | | spearman_dot | 0.271 | | pearson_max | 0.5575 | | spearman_max | 0.5554 | #### 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 | 0.4494 | | **spearman_cosine** | **0.4695** | | pearson_manhattan | 0.4774 | | spearman_manhattan | 0.4763 | | pearson_euclidean | 0.4797 | | spearman_euclidean | 0.4777 | | pearson_dot | 0.4082 | | spearman_dot | 0.3899 | | pearson_max | 0.4797 | | spearman_max | 0.4777 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,000 training samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------| | A person on a horse jumps over a broken down airplane. | A person is training his horse for a competition. | 1 | | A person on a horse jumps over a broken down airplane. | A person is at a diner, ordering an omelette. | 2 | | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | 0 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 1,000 evaluation samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| | Two women are embracing while holding to go packages. | The sisters are hugging goodbye while holding to go packages after just eating lunch. | 1 | | Two women are embracing while holding to go packages. | Two woman are holding packages. | 0 | | Two women are embracing while holding to go packages. | The men are fighting outside a deli. | 2 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 5 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5 - `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 - `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 | 0 | - | - | 0.5931 | - | | 1.0 | 79 | - | - | - | 0.5317 | | 1.2658 | 100 | 0.545 | 0.9351 | 0.5973 | - | | 2.5316 | 200 | 0.5286 | 0.9535 | 0.5660 | - | | 3.7975 | 300 | 0.3553 | 1.0364 | 0.5476 | - | | 5.0 | 395 | - | - | - | 0.4695 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.276 kWh - **Carbon Emitted**: 0.147 kg of CO2 - **Hours Used**: 0.351 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 8 x NVIDIA GeForce RTX 3090 - **CPU Model**: AMD EPYC 7H12 64-Core Processor - **RAM Size**: 229.15 GB ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers and SoftmaxLoss ```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", } ```