---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
---

# Quora Sentence Similarity

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Semantic_Similarity/Semantic%20Similarity-large.ipynb

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```

## Evaluation Results

| Metric | Measure | Value | Notes |
| :--------: | :--------: | :--------: | :--------: |
| Accuracy | **Cosine-Similarity** | 88.72	| Threshold: 0.8397 |
| F1 | Cosine-Similarity | 85.22 | Threshold: 0.8223 |
| Precision | Cosine-Similarity | 80.72 | - |
| Recall | Cosine-Similarity | 90.25 | - |
| Average Precision | Cosine-Similarity | 89.75 | - |
| Accuracy | **Manhattan-Distance** | 88.71	| Threshold: 12.4351 |
| F1 | Manhattan-Distance | 85.22 | Threshold: 13.2209 |
| Precision | Manhattan-Distance | 80.58 | - |
| Recall | Manhattan-Distance | 90.42 | - |
| Average Precision | Manhattan-Distance | 89.74 | - |
| Accuracy | **Euclidean-Distance** | 88.72	| Threshold: 0.5662 |
| F1 | Euclidean-Distance | 85.22 | Threshold: 0.5962 |
| Precision | Euclidean-Distance | 80.72 | - |
| Recall | Euclidean-Distance | 90.25 | - |
| Average Precision | Euclidean-Distance | 89.75 | - |
| Accuracy | **Dot-Product** | 88.72 | Threshold: 0.8397 |
| F1 | Dot-Product | 85.22 | Threshold: 0.8223 |
| Precision | Dot-Product | 80.72 | - |
| Recall | Dot-Product | 90.25 | - |
| Average Precision | Dot-Product | 89.75 | - |


For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 5055 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 20,
    "weight_decay": 0.01
}
```

**Potential Improvements**

One way to improve the results of this model is to use a larger checkpoint of T5. This was trained with the T5-large checkpoint.

The larger checkpoints are:

| Checkpoint | # of Train Params |
| :--------: | :--------: |
| T5-Base | 220 Million |
| T5-Large | 770 Million* |
| T5-3B | 3 Billion |
| T5-11B | 11 Billion |


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 34, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 1024, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Normalize()
)
```

## Citing & Authors

Dataset Source: https://www.kaggle.com/datasets/quora/question-pairs-dataset