README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:72
- loss:ContrastiveLoss
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
widget:
- source_sentence: What was the original purpose of the Basilica di San Lorenzo's
    design by Filippo Brunelleschi in 1419?
  sentences:
  - ' It is one of several churches that claim to be the oldest in Florence, having
    been consecrated in 393 AD, at which time it stood outside the city walls.'
  - The Palazzo Pitti, in English sometimes called the Pitti Palace, is a vast, mainly
    Renaissance, palace in Florence, Italy. It is situated on the south side of the
    River Arno in Pitti Square, a short distance from the Ponte Vecchio.
  - ' The architects were Mariano Falcini, Professor Vincenzo Micheli, and Marco Treves,
    who was Jewish. '
- source_sentence: What is the name of the architect who expanded the façade and the
    rear section of the Palazzo Pitti in 1549?
  sentences:
  - ' The palace was left incomplete by Simone del Pollaiolo (il Cronaca), who was
    in charge of the construction of the palace until 1504. '
  - The Palazzo Pitti, in English sometimes called the Pitti Palace, is a vast, mainly
    Renaissance, palace in Florence, Italy. It is situated on the south side of the
    River Arno in Pitti Square, a short distance from the Ponte Vecchio.
  - ' In 1939, these were joined by the Palestrina Pietà, discovered in the Barberini
    chapel in Palestrina, though experts now consider its attribution to Michelangelo
    to be dubious. '
- source_sentence: When did the Uffizi Gallery officially open to the public?
  sentences:
  - ' The project was intended to display prime artworks of the Medici collections
    on the piano nobile; the plan was carried out by his son, Grand Duke Francesco
    I.'
  - ' The gallery had been open to visitors by request since the sixteenth century,
    and in 1769 it was officially opened to the public, formally becoming a museum
    in 1865.'
  - ' In 1939, these were joined by the Palestrina Pietà, discovered in the Barberini
    chapel in Palestrina, though experts now consider its attribution to Michelangelo
    to be dubious. '
- source_sentence: When was the first church on the site of the current Santa Felicita
    church in Florence probably built?
  sentences:
  - ' The project was intended to display prime artworks of the Medici collections
    on the piano nobile; the plan was carried out by his son, Grand Duke Francesco
    I.'
  - ' It was employed as a prison; executions took place in the Bargello''s yard until
    they were abolished by Grand Duke Peter Leopold in 1786, but it remained the headquarters
    of the Florentine police until 1859.'
  - 'Santa Felicita (Church of St Felicity) is a Roman Catholic church in Florence,
    region of Tuscany, Italy, probably the oldest in the city after San Lorenzo. '
- source_sentence: What was the original purpose of the building in 1255?
  sentences:
  - ' The palace was built to house first the Capitano del Popolo and later, in 1261,
    the ''podestà'', the highest magistrate of the Florence City Council.'
  - The Ponte Vecchio is a medieval stone closed-spandrel segmental arch bridge over
    the Arno, in Florence, Italy. It is the only bridge in Florence spared from destruction
    during World War II and is noted for the shops built along it, a practice that
    was once common on bridges. Initially, these shops were occupied by butchers,
    tanners, and farmers, but today they are home to jewellers, art dealers, and souvenir
    sellers.
  - ' The door retains its original massive, iron-clad doors. '
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# 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/D1_finetuned_2_test_1")
# Run inference
sentences = [
    'What was the original purpose of the building in 1255?',
    " The palace was built to house first the Capitano del Popolo and later, in 1261, the 'podestà', the highest magistrate of the Florence City Council.",
    ' The door retains its original massive, iron-clad doors. ',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## 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: 72 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 72 samples:
  |         | sentence1                                                                          | sentence2                                                                         | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                             |
  | details | <ul><li>min: 14 tokens</li><li>mean: 24.06 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 36.08 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                 | sentence2                                                                                                                                                                                                                                                                                               | label          |
  |:--------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What was the name of the first owner of the Palazzo Pitti, and in which year did he die?</code>                     | <code>The Palazzo Pitti, in English sometimes called the Pitti Palace, is a vast, mainly Renaissance, palace in Florence, Italy. It is situated on the south side of the River Arno in Pitti Square, a short distance from the Ponte Vecchio.</code>                                                    | <code>1</code> |
  | <code>What is the name of the architect who expanded the façade and the rear section of the Palazzo Pitti in 1549?</code> | <code><br>The palace became a great treasure house as generations of the Medici and subsequent dynasties amassed paintings, plates, jewelry, and luxurious possessions. Today, the Palazzo Pitti is the largest museum complex in Florence, divided into several principal galleries or museums.</code> | <code>1</code> |
  | <code>What was the name of the first owner of the Palazzo Pitti, and in which year did he die?</code>                     | <code><br>The palace became a great treasure house as generations of the Medici and subsequent dynasties amassed paintings, plates, jewelry, and luxurious possessions. Today, the Palazzo Pitti is the largest museum complex in Florence, divided into several principal galleries or museums.</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Training Hyperparameters

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `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`: 3.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->