--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:672 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 widget: - source_sentence: ' 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.' sentences: - ' Please advise of any special needs such as high-chairs and sleeping cots.' - ' 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.' - ' It is strongly advised that you arrange adequate insurance cover such as cancellation due to illness, accident or injury, personal accident and personal liability, loss of or damage to baggage and sport equipment (Note that is not an exhaustive list). We will not be responsible or liable if you fail to take adequate insurance cover or none at all.' - source_sentence: 'Owners are responsible for ensuring that animals are kept quiet between the hours of 10:00 pm and 06:00 am. In the case of failure to abide by this regulation the guest may be asked to leave the hotel without a refund of the price of the night''s accommodation.' sentences: - ' Visitors are not allowed in the rooms and must be entertained in the lounges and/or other public areas provided.' - 'To ensure the safety and comfort of everyone in the hotel, the Management reserves the right to terminate the accommodation of guests who fail to comply with the following rules and regulations.' - 'Owners are responsible for ensuring that animals are kept quiet between the hours of 10:00 pm and 06:00 am. In the case of failure to abide by this regulation the guest may be asked to leave the hotel without a refund of the price of the night''s accommodation.' - source_sentence: ' We ask all guests to behave in such a way that they do not disturb other guests and the neighborhood. The hotel staff is authorized to refuse services to a person who violates this rule.' sentences: - ' Please take note of the limitation specified for the room you have booked. If such number is exceeded, whether temporarily or over-night, we reserve the right to do one or more of the following: cancel your booking; retain all the monies you''ve paid; request you to vacate your room(s) forthwith, charge a higher rate for the room or recover all monies due.' - ' We ask all guests to behave in such a way that they do not disturb other guests and the neighborhood. The hotel staff is authorized to refuse services to a person who violates this rule.' - 'We will only deal with your information as indicated in the booking/reservation and we will only process your personal information (both terms as defined in the Protection of Personal Information Act, act 4 of 2013 [''the POPIA''] and the European Union General Data Protection Regulation – (''GDPR'') and any Special Personal Information (as defined in the GDPR & POPIA), which processing includes amongst others the ''collecting, storing and dissemination'' of your personal information (as defined in GDPR & POPIA).' - source_sentence: ' All articles stored in the luggage storage room are received at the owner’s own risk.' sentences: - "\n Unregistered visitors are not permitted to enter guest rooms or other areas\ \ of\nthe hotel. An additional fee for unregistered guests will be charged to\ \ the\naccount of the guest(s) registered to the room." - 'Please advise us if you anticipate arriving late as bookings will be cancelled by 17:00 on the day of arrival, unless we have been so notified.' - ' All articles stored in the luggage storage room are received at the owner’s own risk.' - source_sentence: ' In the event of a disturbance, one polite request (warning) will be given to reduce the noise. If our request is not followed, the guest will be asked to leave the hotel without refund and may be charged Guest Compensation Disturbance Fee.' sentences: - ' Without limiting the generality of the aforementioned, it applies to pay-to-view TV programmes or videos, as well as telephone calls or any other expenses of a similar nature that is made from your room, you will be deemed to be the contracting party.' - 'Pets are not allowed in the restaurant during breakfast time (7:00 – 10:30) for hygienic reasons due to the breakfast’s buffet style. An exception is the case when the hotel terrace is open, as pets can be taken to the terrace through the hotel''s main entrance and they can stay there during breakfast.' - ' In the event of a disturbance, one polite request (warning) will be given to reduce the noise. If our request is not followed, the guest will be asked to leave the hotel without refund and may be charged Guest Compensation Disturbance Fee.' 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: 0.6745562130177515 name: Dot Accuracy - type: dot_accuracy_threshold value: 49.0201301574707 name: Dot Accuracy Threshold - type: dot_f1 value: 0.4932735426008969 name: Dot F1 - type: dot_f1_threshold value: 35.02415466308594 name: Dot F1 Threshold - type: dot_precision value: 0.32934131736526945 name: Dot Precision - type: dot_recall value: 0.9821428571428571 name: Dot Recall - type: dot_ap value: 0.3294144882113245 name: Dot Ap - type: dot_mcc value: -0.03920743101752848 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/Argu_T1") # Run inference sentences = [ ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.', ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.', '\nWithout limiting the generality of the aforementioned, it applies to pay-to-view TV programmes or videos, as\nwell as telephone calls or any other expenses of a similar nature that is made from your room, you will be\ndeemed to be the contracting party.', ] 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 | 0.6746 | | dot_accuracy_threshold | 49.0201 | | dot_f1 | 0.4933 | | dot_f1_threshold | 35.0242 | | dot_precision | 0.3293 | | dot_recall | 0.9821 | | **dot_ap** | **0.3294** | | dot_mcc | -0.0392 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 672 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 672 samples: | | sentence1 | sentence2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
The pets can not be left without supervision if there is a risk of causing any
damage or might disturb other guests. |
The pets can not be left without supervision if there is a risk of causing any
damage or might disturb other guests. | 0 | |
Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these
rules are available at the Front Desk upon request. |
Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these
rules are available at the Front Desk upon request. | 0 | |
Consuming the products from the minibar involves additional costs. You can find the
prices in the kitchen area. |
Consuming the products from the minibar involves additional costs. You can find the
prices in the kitchen area. | 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" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 169 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 169 samples: | | sentence1 | sentence2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
I understand and accept that the BON Hotels Group collects the personal information ("personal
information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of
all in my party, expressly consent and grant permission to the BON Hotels Group to: -
collect, collate, process, study and use the personal information; and
communicate directly with me/us from time to time, unless I have stated to the contrary below. |
I understand and accept that the BON Hotels Group collects the personal information ("personal
information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of
all in my party, expressly consent and grant permission to the BON Hotels Group to: -
collect, collate, process, study and use the personal information; and
communicate directly with me/us from time to time, unless I have stated to the contrary below. | 0 | | However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which
period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse
against us. | However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which
period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse
against us. | 0 | | In cases where the hotel
suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it
may charge a compensation fee in proportion to the damage. Moral damage may be for
example disturbing other guests, thus ruining the reputation of the hotel. | In cases where the hotel
suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it
may charge a compensation fee in proportion to the damage. Moral damage may be for
example disturbing other guests, thus ruining the reputation of the hotel. | 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 - `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`: 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`: 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 | - | - | 0.3294 | | 2.3333 | 100 | 0.0004 | 0.0000 | - | | 4.6905 | 200 | 0.0003 | 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} } ```