README.md

---
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
datasets:
- PiC/phrase_similarity
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7004
- loss:SoftmaxLoss
widget:
- source_sentence: Google SEO expert Matt Cutts had a similar experience, of the eight
 magazines and newspapers Cutts tried to order, he received zero.
 sentences:
 - He dissolved the services of her guards and her court attendants and seized an
 expansive reach of properties belonging to her.
 - Google SEO expert Matt Cutts had a comparable occurrence, of the eight magazines
 and newspapers Cutts tried to order, he received zero.
 - bill's newest solo play, "all over the map", premiered off broadway in april 2016,
 produced by all for an individual cinema.
- source_sentence: Shula said that Namath "beat our blitz" with his fast release,
 which let him quickly dump the football off to a receiver.
 sentences:
 - Shula said that Namath "beat our blitz" with his quick throw, which let him quickly
 dump the football off to a receiver.
 - it elects a single component of parliament (mp) by the first past the post system
 of election.
 - Matt Groening said that West was one of the most widely known group to ever come
 to the studio.
- source_sentence: When Angel calls out her name, Cordelia suddenly appears from the
 opposite side of the room saying, "Yep, that chick's in rough shape.
 sentences:
 - The ruined row of text, part of the Florida East Coast Railway, was repaired by
 2014 renewing freight train access to the port.
 - When Angel calls out her name, Cordelia suddenly appears from the opposite side
 of the room saying, "Yep, that chick's in approximate form.
 - Chaplin's films introduced a moderated kind of comedy than the typical Keystone
 farce, and he developed a large fan base.
- source_sentence: The following table shows the distances traversed by National Route
 11 in each different department, showing cities and towns that it passes by (or
 near).
 sentences:
 - The following table shows the distances traversed by National Route 11 in each
 separate city authority, showing cities and towns that it passes by (or near).
 - Similarly, indigenous communities and leaders practice as the main rule of law
 on local native lands and reserves.
 - later, sylvan mixed gary numan's albums "replicas" (with numan's previous band
 tubeway army) and "the quest for instant gratification".
- source_sentence: She wants to write about Keima but suffers a major case of writer's
 block.
 sentences:
 - In some countries, new extremist parties on the extreme opposite of left of the
 political spectrum arose, motivated through issues of immigration, multiculturalism
 and integration.
 - specific medical status of movement and the general condition of movement both
 are conditions under which contradictions can move.
 - She wants to write about Keima but suffers a huge occurrence of writer's block.
model-index:
- name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1
 results:
 - task:
 type: binary-classification
 name: Binary Classification
 dataset:
 name: quora duplicates dev
 type: quora-duplicates-dev
 metrics:
 - type: cosine_accuracy
 value: 0.681
 name: Cosine Accuracy
 - type: cosine_accuracy_threshold
 value: 0.8657017946243286
 name: Cosine Accuracy Threshold
 - type: cosine_f1
 value: 0.7373493975903616
 name: Cosine F1
 - type: cosine_f1_threshold
 value: 0.5984358787536621
 name: Cosine F1 Threshold
 - type: cosine_precision
 value: 0.6161073825503356
 name: Cosine Precision
 - type: cosine_recall
 value: 0.918
 name: Cosine Recall
 - type: cosine_ap
 value: 0.7182646093780225
 name: Cosine Ap
 - type: dot_accuracy
 value: 0.678
 name: Dot Accuracy
 - type: dot_accuracy_threshold
 value: 35.86492156982422
 name: Dot Accuracy Threshold
 - type: dot_f1
 value: 0.7361668003207699
 name: Dot F1
 - type: dot_f1_threshold
 value: 26.907243728637695
 name: Dot F1 Threshold
 - type: dot_precision
 value: 0.6144578313253012
 name: Dot Precision
 - type: dot_recall
 value: 0.918
 name: Dot Recall
 - type: dot_ap
 value: 0.6677244029971525
 name: Dot Ap
 - type: manhattan_accuracy
 value: 0.682
 name: Manhattan Accuracy
 - type: manhattan_accuracy_threshold
 value: 75.9630126953125
 name: Manhattan Accuracy Threshold
 - type: manhattan_f1
 value: 0.7362459546925567
 name: Manhattan F1
 - type: manhattan_f1_threshold
 value: 128.1773681640625
 name: Manhattan F1 Threshold
 - type: manhattan_precision
 value: 0.6182065217391305
 name: Manhattan Precision
 - type: manhattan_recall
 value: 0.91
 name: Manhattan Recall
 - type: manhattan_ap
 value: 0.719303642596625
 name: Manhattan Ap
 - type: euclidean_accuracy
 value: 0.682
 name: Euclidean Accuracy
 - type: euclidean_accuracy_threshold
 value: 3.447394847869873
 name: Euclidean Accuracy Threshold
 - type: euclidean_f1
 value: 0.7361668003207699
 name: Euclidean F1
 - type: euclidean_f1_threshold
 value: 6.024651527404785
 name: Euclidean F1 Threshold
 - type: euclidean_precision
 value: 0.6144578313253012
 name: Euclidean Precision
 - type: euclidean_recall
 value: 0.918
 name: Euclidean Recall
 - type: euclidean_ap
 value: 0.7195081644602263
 name: Euclidean Ap
 - type: max_accuracy
 value: 0.682
 name: Max Accuracy
 - type: max_accuracy_threshold
 value: 75.9630126953125
 name: Max Accuracy Threshold
 - type: max_f1
 value: 0.7373493975903616
 name: Max F1
 - type: max_f1_threshold
 value: 128.1773681640625
 name: Max F1 Threshold
 - type: max_precision
 value: 0.6182065217391305
 name: Max Precision
 - type: max_recall
 value: 0.918
 name: Max Recall
 - type: max_ap
 value: 0.7195081644602263
 name: Max Ap
---

# 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) on the [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) dataset. 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 3af7c6da5b3e1bea796ef6c97fe237538cbe6e7f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Dot Product
- **Training Dataset:**
 - [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity)
- **Language:** en
<!-- - **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("Deehan1866/finetuned-sentence-transformers-multi-qa-mpnet-base-dot-v1")
# Run inference
sentences = [
 "She wants to write about Keima but suffers a major case of writer's block.",
 "She wants to write about Keima but suffers a huge occurrence of writer's block.",
 'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
]
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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## Evaluation

### Metrics

#### Binary Classification
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.681 |
| cosine_accuracy_threshold | 0.8657 |
| cosine_f1 | 0.7373 |
| cosine_f1_threshold | 0.5984 |
| cosine_precision | 0.6161 |
| cosine_recall | 0.918 |
| cosine_ap | 0.7183 |
| dot_accuracy | 0.678 |
| dot_accuracy_threshold | 35.8649 |
| dot_f1 | 0.7362 |
| dot_f1_threshold | 26.9072 |
| dot_precision | 0.6145 |
| dot_recall | 0.918 |
| dot_ap | 0.6677 |
| manhattan_accuracy | 0.682 |
| manhattan_accuracy_threshold | 75.963 |
| manhattan_f1 | 0.7362 |
| manhattan_f1_threshold | 128.1774 |
| manhattan_precision | 0.6182 |
| manhattan_recall | 0.91 |
| manhattan_ap | 0.7193 |
| euclidean_accuracy | 0.682 |
| euclidean_accuracy_threshold | 3.4474 |
| euclidean_f1 | 0.7362 |
| euclidean_f1_threshold | 6.0247 |
| euclidean_precision | 0.6145 |
| euclidean_recall | 0.918 |
| euclidean_ap | 0.7195 |
| max_accuracy | 0.682 |
| max_accuracy_threshold | 75.963 |
| max_f1 | 0.7373 |
| max_f1_threshold | 128.1774 |
| max_precision | 0.6182 |
| max_recall | 0.918 |
| **max_ap** | **0.7195** |

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## Training Details

### Training Dataset

#### PiC/phrase_similarity

* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 7,004 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | label |
 |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
 | type | string | string | int |
 | details | <ul><li>min: 12 tokens</li><li>mean: 26.35 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 26.89 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~48.80%</li><li>1: ~51.20%</li></ul> |
* Samples:
 | sentence1 | sentence2 | label |
 |:------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
 | <code>newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> | <code>0</code> |
 | <code>According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> | <code>1</code> |
 | <code>Note that Fact 1 does not assume any particular structure on the set formula_65.</code> | <code>Note that Fact 1 does not assume any specific edifice on the set formula_65.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### PiC/phrase_similarity

* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | label |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
 | type | string | string | int |
 | details | <ul><li>min: 9 tokens</li><li>mean: 26.21 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 26.8 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
 | sentence1 | sentence2 | label |
 |:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------|
 | <code>after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.</code> | <code>after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.</code> | <code>0</code> |
 | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.</code> | <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.</code> | <code>0</code> |
 | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.</code> | <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### 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
- `load_best_model_at_end`: True

#### 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
- `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`: 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`: True
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | loss | quora-duplicates-dev_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------------:|
| 0 | 0 | - | - | 0.6564 |
| 0.2283 | 100 | - | 0.6941 | 0.6565 |
| 0.4566 | 200 | - | 0.6899 | 0.6713 |
| 0.6849 | 300 | - | 0.6467 | 0.7247 |
| 0.9132 | 400 | - | 0.5957 | 0.7231 |
| 1.1416 | 500 | 0.6571 | 0.6093 | 0.7044 |
| **1.3699** | **600** | **-** | **0.5578** | **0.7195** |
| 1.5982 | 700 | - | 0.5626 | 0.7372 |
| 1.8265 | 800 | - | 0.5790 | 0.7413 |
| 2.0548 | 900 | - | 0.5648 | 0.7405 |
| 2.2831 | 1000 | 0.519 | 0.5820 | 0.7467 |
| 2.5114 | 1100 | - | 0.5976 | 0.7455 |
| 2.7397 | 1200 | - | 0.6026 | 0.7335 |
| 2.9680 | 1300 | - | 0.6231 | 0.7422 |
| 3.1963 | 1400 | - | 0.6514 | 0.7376 |
| 3.4247 | 1500 | 0.3903 | 0.6695 | 0.7379 |
| 3.6530 | 1600 | - | 0.6610 | 0.7339 |
| 3.8813 | 1700 | - | 0.6811 | 0.7318 |
| 4.1096 | 1800 | - | 0.7205 | 0.7274 |
| 4.3379 | 1900 | - | 0.7333 | 0.7332 |
| 4.5662 | 2000 | 0.3036 | 0.7353 | 0.7323 |
| 4.7945 | 2100 | - | 0.7293 | 0.7322 |
| 5.0 | 2190 | - | - | 0.7195 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.2.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

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