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Base_T

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:684
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
Base_T / README.md
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:684
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
widget:
- source_sentence: '
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.'
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.'
- '
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.'
- '
Guests who take accommodation after midnight, shall still pay the price for
accommodation for the whole of the preceding night. The hotel’s official Check-in
time is
from 02:00 pm. For a possible early check-in, please consult with the reservation
team, or
the reception in advance.'
- source_sentence: '
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.'
sentences:
- '
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.'
- ' If you do not want someone to enter
your room, please hang the "do not disturb” card on your room’s outside door handle.
It can
be found in the entrance area of your room.'
- '
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.'
- source_sentence: '
Guests may not use their own electrical appliances in the hotel building except
for those
serving for personal hygiene (electrical shavers or massaging machines, hairdryers
etc.), or
personal computers and telephone chargers. The rooms own electrical devices shall
only be
used according to their main purpose.'
sentences:
- '
Pets are allowed in the hotel restaurant only from 12:00, provided the
animal''s behavior and cleanliness are adequate and they do not disturb other
guests. '
- '
Guests may not use their own electrical appliances in the hotel building except
for those
serving for personal hygiene (electrical shavers or massaging machines, hairdryers
etc.), or
personal computers and telephone chargers. The rooms own electrical devices shall
only be
used according to their main purpose.'
- ' For a possible late check-out please consult with the reception
in time, and upon availability we may grant a later check-out for a supplemental
fee.'
- source_sentence: '
The hotel may provide accommodation only for guests who register in the regular
manner. For this purpose, the guest must present a personal document (citizen''s
identification card), or a valid passport to the receptionist. Accepting these
Rules of the
House is also obligatory for the registration.'
sentences:
- '
Hotel guests are obliged to abide by the provisions of these hotel regulations.
In the case of
serious violation, the reception or hotel management may withdraw from the contract
on
accommodation services before the elapse of the agreed period.'
- '
Hotel guests are responsible for given room keys during their whole stay. In case
of loss, the
guests are asked to inform reception staff immediately in order to prevent abusing
the key.
Losing the room key will result in a penalty of 20 Eur, which is to be paid on
the spot, at the
reception.'
- '
The hotel may provide accommodation only for guests who register in the regular
manner. For this purpose, the guest must present a personal document (citizen''s
identification card), or a valid passport to the receptionist. Accepting these
Rules of the
House is also obligatory for the registration.'
- source_sentence: '
Guests are responsible for damages caused to hotel property according to the valid
legal
prescriptions of Hungary.'
sentences:
- '
We shall be happy to listen to any suggestions for improvement of the accommodation
and catering services in the hotel. In case of any complaints we shall purposefully
arrange
the rectification of any insufficiencies.'
- '
Guests are responsible for damages caused to hotel property according to the valid
legal
prescriptions of Hungary.'
- '
Guests are responsible for damages caused to hotel property according to the valid
legal
prescriptions of Hungary.'
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: 0.6549707602339181
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 48.36168670654297
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.5142857142857143
name: Dot F1
- type: dot_f1_threshold
value: 40.011634826660156
name: Dot F1 Threshold
- type: dot_precision
value: 0.36
name: Dot Precision
- type: dot_recall
value: 0.9
name: Dot Recall
- type: dot_ap
value: 0.3570718807651215
name: Dot Ap
- type: dot_mcc
value: 0.03879793956580217
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) <!-- at revision 4633e80e17ea975bc090c97b049da26062b054d3 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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_T")
# Run inference
sentences = [
'\nGuests are responsible for damages caused to hotel property according to the valid legal\nprescriptions of Hungary.',
'\nGuests are responsible for damages caused to hotel property according to the valid legal\nprescriptions of Hungary.',
'\nWe shall be happy to listen to any suggestions for improvement of the accommodation\nand catering services in the hotel. In case of any complaints we shall purposefully arrange\nthe rectification of any insufficiencies.',
]
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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------|:-----------|
| dot_accuracy | 0.655 |
| dot_accuracy_threshold | 48.3617 |
| dot_f1 | 0.5143 |
| dot_f1_threshold | 40.0116 |
| dot_precision | 0.36 |
| dot_recall | 0.9 |
| **dot_ap** | **0.3571** |
| dot_mcc | 0.0388 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 684 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 684 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 42.77 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 42.77 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>0: ~67.11%</li><li>1: ~32.89%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code> If a guest fails to vacate<br>the room within the designated time, reception shall charge this guest for the following<br>night's accommodation fee.</code> | <code> If a guest fails to vacate<br>the room within the designated time, reception shall charge this guest for the following<br>night's accommodation fee.</code> | <code>0</code> |
| <code> If you do not want someone to enter<br>your room, please hang the "do not disturb” card on your room’s outside door handle. It can<br>be found in the entrance area of your room.</code> | <code> If you do not want someone to enter<br>your room, please hang the "do not disturb” card on your room’s outside door handle. It can<br>be found in the entrance area of your room.</code> | <code>0</code> |
| <code><br>Owners are responsible for ensuring that animals are kept quiet between the<br>hours of 10:00 pm and 06:00 am. In the case of failure to abide by this<br>regulation the guest may be asked to leave the hotel without a refund of the<br>price of the night's accommodation.</code> | <code><br>Owners are responsible for ensuring that animals are kept quiet between the<br>hours of 10:00 pm and 06:00 am. In the case of failure to abide by this<br>regulation the guest may be asked to leave the hotel without a refund of the<br>price of the night's accommodation.</code> | <code>0</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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: 171 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 171 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 42.01 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 42.01 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>0: ~64.91%</li><li>1: ~35.09%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br>We shall be happy to listen to any suggestions for improvement of the accommodation<br>and catering services in the hotel. In case of any complaints we shall purposefully arrange<br>the rectification of any insufficiencies.</code> | <code><br>We shall be happy to listen to any suggestions for improvement of the accommodation<br>and catering services in the hotel. In case of any complaints we shall purposefully arrange<br>the rectification of any insufficiencies.</code> | <code>0</code> |
| <code><br>Between the hours of 10:00 pm and 06:00 am guests are obliged to maintain low noise<br>levels.</code> | <code><br>Between the hours of 10:00 pm and 06:00 am guests are obliged to maintain low noise<br>levels.</code> | <code>0</code> |
| <code><br>The hotel’s inner courtyard parking facility may be used only upon availability of parking<br>slots. Slots marked as ’Private’ are to be left free for their owners. For parking fees please<br>consult the reception or see the website of the hotel.</code> | <code><br>The hotel’s inner courtyard parking facility may be used only upon availability of parking<br>slots. Slots marked as ’Private’ are to be left free for their owners. For parking fees please<br>consult the reception or see the website of the hotel.</code> | <code>1</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | dot_ap |
|:------:|:----:|:-------------:|:---------------:|:------:|
| -1 | -1 | - | - | 0.3571 |
| 2.2791 | 100 | 0.0011 | 0.0000 | - |
| 4.5581 | 200 | 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}
}
```
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