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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
---
# DunnBC22/sentence-t5-base-FT-Quora_Sentence_Similarity-LG
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-base.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('DunnBC22/sentence-t5-base-FT-Quora_Sentence_Similarity-LG')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=DunnBC22/sentence-t5-base-FT-Quora_Sentence_Similarity-LG)
| Metric | Measure | Value | Notes |
| :--------: | :--------: | :--------: | :--------: |
| Accuracy | **Cosine-Similarity** | 85.93 | Threshold: 0.8320 |
| F1 | Cosine-Similarity | 82.89 | Threshold: 0.8178 |
| Precision | Cosine-Similarity | 77.43 | - |
| Recall | Cosine-Similarity | 89.18 | - |
| Average Precision | Cosine-Similarity | 87.13 | - |
| Accuracy | **Manhattan-Distance** | 85.95 | Threshold: 12.7721 |
| F1 | Manhattan-Distance | 82.89 | Threshold: 13.5008 |
| Precision | Manhattan-Distance | 76.91 | - |
| Recall | Manhattan-Distance | 89.89 | - |
| Average Precision | Manhattan-Distance | 87.13 | - |
| Accuracy | **Euclidean-Distance** | 85.93 | Threshold: 0.5797 |
| F1 | Euclidean-Distance | 82.89 | Threshold: 0.6037 |
| Precision | Euclidean-Distance | 77.43 | - |
| Recall | Euclidean-Distance | 89.18 | - |
| Average Precision | Euclidean-Distance | 87.13 | - |
| Accuracy | **Dot-Product** | 85.93 | Threshold: 0.8320 |
| F1 | Dot-Product | 82.89 | Threshold: 0.8178 |
| Precision | Dot-Product | 77.43 | - |
| Recall | Dot-Product | 89.18 | - |
| Average Precision | Dot-Product | 87.14 | - |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4673 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": 2,
"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-base 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': 768, '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 |