--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3362 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 widget: - source_sentence: ' Guests are responsible for damages caused to hotel property according to the valid legal prescriptions of Hungary.' sentences: - ' Guests are responsible for damages caused to hotel property according to the valid legal prescriptions of Hungary.' - ' We request that guests report any complaints and defects to the hotel reception or hotel management in person. Your complaints shall be attended to immediately.' - ' We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as well as using our best endeavours to advise you if that is not the case.' - source_sentence: ' If we must cancel the reservation due to circumstances beyond our control, the entire payment will be refunded to you without any further obligation on our part and you will have no further recourse against us.' sentences: - ' We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as well as using our best endeavours to advise you if that is not the case.' - ' A hotel guest may not leave the room to another person, even if the time for which he or she has paid has not expired.' - ' If we must cancel the reservation due to circumstances beyond our control, the entire payment will be refunded to you without any further obligation on our part and you will have no further recourse against us.' - source_sentence: 'For safety reasons it is not permitted to leave children under 12 years of age in hotel rooms and other common areas of the hotel without adult supervision, and children under 12 years of age may not use the lift without supervision.' sentences: - 'For safety reasons it is not permitted to leave children under 12 years of age in hotel rooms and other common areas of the hotel without adult supervision, and children under 12 years of age may not use the lift without supervision.' - ' I accept personal responsibility for payment of all amounts arising from my party''s stay at the Hotel. I/we are obligated to vacate my/our room/s at the designated check-out time, unless I have made prior alternative check-out arrangements with the management of the Hotel. My/our failure to do so will result in my liability for the costs of an additional night''s accommodation.' - ' Elevators are to be used for the sole purpose of transporting guests and their luggage to the appropriate floor of the hotel. Misuse and horseplay will not be allowed.' - source_sentence: ' Accommodation in the hotel is permitted only to persons who are not carrying infectious diseases and who are not visibly under the influence of alcohol or drugs.' sentences: - ' Animals may not be allowed onto beds or other furniture, which serves for guests. It is not permitted to use baths, showers or washbasins for bathing or washing animals.' - ' Accommodation in the hotel is permitted only to persons who are not carrying infectious diseases and who are not visibly under the influence of alcohol or drugs.' - ' The pets can not be left without supervision if there is a risk of causing any damage or might disturb other guests.' - source_sentence: ' A hotel guest may not leave the room to another person, even if the time for which he or she has paid has not expired.' sentences: - ' A hotel guest may not leave the room to another person, even if the time for which he or she has paid has not expired.' - ' There is no running, shouting, roughhousing or horseplay accepted while on the hotel property. This includes hallways, lobby areas, stairways, elevators, food service areas and guest rooms.' - 'Orders for accommodation services made in writing or by other means, which have been confirmed by the hotel and have not been cancelled by the customer in a timely manner, are mutually binding. The front office manager keeps a record of all received and confirmed orders.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - dot_mcc model-index: - name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1 results: - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: dot_accuracy value: 0.667063020214031 name: Dot Accuracy - type: dot_accuracy_threshold value: 48.93047332763672 name: Dot Accuracy Threshold - type: dot_f1 value: 0.49865951742627346 name: Dot F1 - type: dot_f1_threshold value: 33.95234298706055 name: Dot F1 Threshold - type: dot_precision value: 0.33253873659118 name: Dot Precision - type: dot_recall value: 0.9964285714285714 name: Dot Recall - type: dot_ap value: 0.31258772254817324 name: Dot Ap - type: dot_mcc value: 0.0 name: Dot Mcc --- # SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). 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:** [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Dot Product ### 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: MPNetModel (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("Marco127/Base_Test1_") # Run inference sentences = [ '\nA hotel guest may not leave the room to another person, even if the time for which he or she has paid has\nnot expired.', '\nA hotel guest may not leave the room to another person, even if the time for which he or she has paid has\nnot expired.', 'Orders for accommodation services made in writing or by other means, which have been\nconfirmed by the hotel and have not been cancelled by the customer in a timely manner, are\nmutually binding. The front office manager keeps a record of all received and confirmed\norders.', ] 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 #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------|:-----------| | dot_accuracy | 0.6671 | | dot_accuracy_threshold | 48.9305 | | dot_f1 | 0.4987 | | dot_f1_threshold | 33.9523 | | dot_precision | 0.3325 | | dot_recall | 0.9964 | | **dot_ap** | **0.3126** | | dot_mcc | 0.0 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,362 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.
Visitors must present a personal document at the hotel reception and register in the visitors'
book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm. | Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.
Visitors must present a personal document at the hotel reception and register in the visitors'
book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm. | 0 | |
We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as
well as using our best endeavours to advise you if that is not the case. |
We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as
well as using our best endeavours to advise you if that is not the case. | 0 | |
Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to
allow efficient operation of the hotel and for the comfort and safety of all guests. |
Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to
allow efficient operation of the hotel and for the comfort and safety of all guests. | 1 | * 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: 841 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 841 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | In the case of fire, guests are obliged to notify the reception without hesitation, either
directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors
of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited! | In the case of fire, guests are obliged to notify the reception without hesitation, either
directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors
of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited! | 0 | |
Children should be accompanied in locations such as stairways etc.
The rooms are for accommodation service. Each individual staying in a room
must be registered at the reception. |
Children should be accompanied in locations such as stairways etc.
The rooms are for accommodation service. Each individual staying in a room
must be registered at the reception. | 0 | |
Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be
taken to the Pool or Fitness Room. |
Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be
taken to the Pool or Fitness Room. | 0 | * 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`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### 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 - `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`: 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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | dot_ap | |:------:|:----:|:-------------:|:---------------:|:------:| | -1 | -1 | - | - | 0.3126 | | 0.4739 | 100 | 0.0011 | 0.0001 | - | | 0.9479 | 200 | 0.0002 | 0.0000 | - | | 1.4218 | 300 | 0.0 | 0.0000 | - | | 1.8957 | 400 | 0.0001 | 0.0000 | - | | 2.3697 | 500 | 0.0 | 0.0000 | - | | 2.8436 | 600 | 0.0 | 0.0000 | - | | 3.3175 | 700 | 0.0 | 0.0000 | - | | 3.7915 | 800 | 0.0 | 0.0000 | - | | 4.2654 | 900 | 0.0 | 0.0000 | - | | 4.7393 | 1000 | 0.0 | 0.0000 | - | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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} } ```