--- base_model: google-bert/bert-base-uncased datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:100000 - loss:DenoisingAutoEncoderLoss widget: - source_sentence: 1109/icnsurv sentences: - 1109/icnsurv - A cost function is needed to assign a performance metric value to a particular test run - Aircraft OperationsFuture aircraft will sense, control, communicate, and navigate with increasing levels of autonomy, enabling new concepts in air traffic management - source_sentence: Table 1 of and to well as the median taxi from STBO KDFW sentences: - Table 1 Metrics of accuracy, median and MAD of residuals as compared to STBO predictions, as well as the median taxi time from STBO for KDFW and KCLT airports - ', IEEE, 2005, pp' - 'RESULTS: EFFICIENCY ANALYSIS' - source_sentence: gate time to known sentences: - 3FIVE INPUT VARIABLESParameterDescriptionHead windHead WindGust windGust WindCeiling_ftForecast CeilingVis_ftForecast VisibilityAct_Land_Wgt Actual Landing Weightfive parameters listed in - Instead, gate departure time was assumed to be known - The proof is very similar to that presented for the NP-completeness of ASP, and is based on reduction from PLANAR-P3( 6), hence we simply provide the main idea of the proof - source_sentence: ', Hough" Pattern Recognition, Vol' sentences: - 9 Station Keeping scores - "\t\tAGARD CD-410" - ', "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, Vol' - source_sentence: Airlines often ferry from locations fuel prices sentences: - Scheduler Inputs and Order of ConsiderationThe surface model provides EOBT, UOBT, UTOT and other detailed flight-specific modeled input - "\t\t\tKeithWichman" - Airlines often ferry fuel from locations where fuel prices are cheapest --- # 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': True, 'pooling_mode_mean_tokens': False, '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("kathleenge/tsdae-bert-base-uncased") # Run inference sentences = [ 'Airlines often ferry from locations fuel prices', 'Airlines often ferry fuel from locations where fuel prices are cheapest', '\t\t\tKeithWichman', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 100,000 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | selected and reviewed for value current on metroplex | The literature was selected and reviewed for its value to the current research on metroplex operations | | and | , and Dulchinos, V | | , | , Atkins, S | * Loss: [DenoisingAutoEncoderLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `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 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: 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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | |:-----:|:-----:|:-------------:| | 0.04 | 500 | 7.3777 | | 0.08 | 1000 | 6.9771 | | 0.12 | 1500 | 6.8481 | | 0.16 | 2000 | 6.7737 | | 0.2 | 2500 | 6.6935 | | 0.24 | 3000 | 6.6264 | | 0.28 | 3500 | 6.5918 | | 0.32 | 4000 | 6.5504 | | 0.36 | 4500 | 6.4805 | | 0.4 | 5000 | 6.4539 | | 0.44 | 5500 | 6.4242 | | 0.48 | 6000 | 6.4017 | | 0.52 | 6500 | 6.3808 | | 0.56 | 7000 | 6.3595 | | 0.6 | 7500 | 6.3174 | | 0.64 | 8000 | 6.2911 | | 0.68 | 8500 | 6.2917 | | 0.72 | 9000 | 6.2555 | | 0.76 | 9500 | 6.2314 | | 0.8 | 10000 | 6.2223 | | 0.84 | 10500 | 6.1852 | | 0.88 | 11000 | 6.2067 | | 0.92 | 11500 | 6.1562 | | 0.96 | 12000 | 6.1563 | | 1.0 | 12500 | 6.092 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - 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", } ``` #### DenoisingAutoEncoderLoss ```bibtex @inproceedings{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", pages = "671--688", url = "https://arxiv.org/abs/2104.06979", } ```