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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:3362 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 |
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widget: |
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- source_sentence: ' |
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Guests are responsible for damages caused to hotel property according to the valid |
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legal |
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prescriptions of Hungary.' |
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sentences: |
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- ' |
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Guests are responsible for damages caused to hotel property according to the valid |
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legal |
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prescriptions of Hungary.' |
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- ' |
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We request that guests report any complaints and defects to the hotel reception |
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or hotel |
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management in person. Your complaints shall be attended to immediately.' |
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- ' |
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We do not guarantee that any special requests will be met, but we will use our |
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best endeavours to do so as |
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well as using our best endeavours to advise you if that is not the case.' |
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- source_sentence: ' |
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If we must cancel the reservation due to circumstances beyond our control, the |
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entire payment will be |
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refunded to you without any further obligation on our part and you will have no |
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further recourse against us.' |
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sentences: |
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- ' |
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We do not guarantee that any special requests will be met, but we will use our |
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best endeavours to do so as |
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well as using our best endeavours to advise you if that is not the case.' |
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- ' |
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A hotel guest may not leave the room to another person, even if the time for which |
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he or she has paid has |
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not expired.' |
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- ' |
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If we must cancel the reservation due to circumstances beyond our control, the |
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entire payment will be |
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refunded to you without any further obligation on our part and you will have no |
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further recourse against us.' |
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- source_sentence: 'For safety reasons it is not permitted to leave children under |
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12 years of age in hotel |
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rooms and other common areas of the hotel without adult supervision, and children |
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under |
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12 years of age may not use the lift without supervision.' |
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sentences: |
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- 'For safety reasons it is not permitted to leave children under 12 years of age |
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in hotel |
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rooms and other common areas of the hotel without adult supervision, and children |
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under |
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12 years of age may not use the lift without supervision.' |
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- ' |
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I accept personal responsibility for payment of all amounts arising from my party''s |
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stay at the Hotel. |
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I/we are obligated to vacate my/our room/s at the designated check-out time, unless |
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I have made prior |
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alternative check-out arrangements with the management of the Hotel. My/our failure |
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to do so will result in |
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my liability for the costs of an additional night''s accommodation.' |
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- ' |
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Elevators are to be used for the sole purpose of transporting guests and their |
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luggage to the appropriate |
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floor of the hotel. Misuse and horseplay will not be allowed.' |
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- source_sentence: ' |
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Accommodation in the hotel is permitted only to persons who are not carrying infectious |
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diseases and who are not visibly under the influence of alcohol or drugs.' |
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sentences: |
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- ' |
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Animals may not be allowed onto beds or other furniture, which serves for |
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guests. It is not permitted to use baths, showers or washbasins for bathing or |
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washing animals.' |
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- ' |
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Accommodation in the hotel is permitted only to persons who are not carrying infectious |
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diseases and who are not visibly under the influence of alcohol or drugs.' |
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- ' |
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The pets can not be left without supervision if there is a risk of causing any |
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damage or might disturb other guests.' |
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- source_sentence: ' |
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A hotel guest may not leave the room to another person, even if the time for which |
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he or she has paid has |
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not expired.' |
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sentences: |
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- ' |
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A hotel guest may not leave the room to another person, even if the time for which |
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he or she has paid has |
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not expired.' |
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- ' |
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There is no running, shouting, roughhousing or horseplay accepted while on the |
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hotel property. This |
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includes hallways, lobby areas, stairways, elevators, food service areas and guest |
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rooms.' |
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- 'Orders for accommodation services made in writing or by other means, which have |
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been |
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confirmed by the hotel and have not been cancelled by the customer in a timely |
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manner, are |
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mutually binding. The front office manager keeps a record of all received and |
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confirmed |
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orders.' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- dot_accuracy |
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- dot_accuracy_threshold |
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- dot_f1 |
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- dot_f1_threshold |
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- dot_precision |
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- dot_recall |
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- dot_ap |
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- dot_mcc |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1 |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: dot_accuracy |
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value: 0.667063020214031 |
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name: Dot Accuracy |
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- type: dot_accuracy_threshold |
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value: 48.93047332763672 |
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name: Dot Accuracy Threshold |
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- type: dot_f1 |
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value: 0.49865951742627346 |
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name: Dot F1 |
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- type: dot_f1_threshold |
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value: 33.95234298706055 |
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name: Dot F1 Threshold |
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- type: dot_precision |
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value: 0.33253873659118 |
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name: Dot Precision |
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- type: dot_recall |
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value: 0.9964285714285714 |
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name: Dot Recall |
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- type: dot_ap |
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value: 0.31258772254817324 |
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name: Dot Ap |
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- type: dot_mcc |
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value: 0.0 |
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name: Dot Mcc |
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--- |
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# SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1 |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) <!-- at revision 4633e80e17ea975bc090c97b049da26062b054d3 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Dot Product |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Marco127/Base_Test1_") |
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# Run inference |
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sentences = [ |
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'\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.', |
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'\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.', |
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'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.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Binary Classification |
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
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| Metric | Value | |
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|:-----------------------|:-----------| |
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| dot_accuracy | 0.6671 | |
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| dot_accuracy_threshold | 48.9305 | |
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| dot_f1 | 0.4987 | |
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| dot_f1_threshold | 33.9523 | |
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| dot_precision | 0.3325 | |
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| dot_recall | 0.9964 | |
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| **dot_ap** | **0.3126** | |
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| dot_mcc | 0.0 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 3,362 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 48.75 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 48.75 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~69.20%</li><li>1: ~30.80%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.<br>Visitors must present a personal document at the hotel reception and register in the visitors'<br>book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm.</code> | <code>Hotel guests may receive visits in their hotel rooms from guests not staying in the hotel.<br>Visitors must present a personal document at the hotel reception and register in the visitors'<br>book. These visits can last for only a maximum of 2 hours and must finish until 10:00 pm.</code> | <code>0</code> | |
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| <code><br>We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as<br>well as using our best endeavours to advise you if that is not the case.</code> | <code><br>We do not guarantee that any special requests will be met, but we will use our best endeavours to do so as<br>well as using our best endeavours to advise you if that is not the case.</code> | <code>0</code> | |
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| <code><br>Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to<br>allow efficient operation of the hotel and for the comfort and safety of all guests.</code> | <code><br>Pool and Fitness Room hours and guidelines are provided at check in. All rules and times will be enforced to<br>allow efficient operation of the hotel and for the comfort and safety of all guests.</code> | <code>1</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 841 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 841 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 48.1 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 48.1 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~66.71%</li><li>1: ~33.29%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>In the case of fire, guests are obliged to notify the reception without hesitation, either<br>directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors<br>of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited!</code> | <code>In the case of fire, guests are obliged to notify the reception without hesitation, either<br>directly, or on the phone (0) and may use a portable fire extinguisher located at the corridors<br>of each floor to extinguish the flames. The use of the elevator in case of fire is prohibited!</code> | <code>0</code> | |
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| <code><br>Children should be accompanied in locations such as stairways etc.<br> The rooms are for accommodation service. Each individual staying in a room<br>must be registered at the reception.</code> | <code><br>Children should be accompanied in locations such as stairways etc.<br> The rooms are for accommodation service. Each individual staying in a room<br>must be registered at the reception.</code> | <code>0</code> | |
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| <code><br>Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be<br>taken to the Pool or Fitness Room.</code> | <code><br>Towels for the Fitness Room and Pool are located in those areas. Towels from guest rooms are not to be<br>taken to the Pool or Fitness Room.</code> | <code>0</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | dot_ap | |
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|:------:|:----:|:-------------:|:---------------:|:------:| |
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| -1 | -1 | - | - | 0.3126 | |
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| 0.4739 | 100 | 0.0011 | 0.0001 | - | |
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| 0.9479 | 200 | 0.0002 | 0.0000 | - | |
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| 1.4218 | 300 | 0.0 | 0.0000 | - | |
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| 1.8957 | 400 | 0.0001 | 0.0000 | - | |
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| 2.3697 | 500 | 0.0 | 0.0000 | - | |
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| 2.8436 | 600 | 0.0 | 0.0000 | - | |
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| 3.3175 | 700 | 0.0 | 0.0000 | - | |
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| 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 |
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- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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|
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## Citation |
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|
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### BibTeX |
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|
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#### 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", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
|
``` |
|
|
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#### MultipleNegativesRankingLoss |
|
```bibtex |
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@misc{henderson2017efficient, |
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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}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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