---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:20
- loss:CoSENTLoss
widget: []
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: validation
type: validation
metrics:
- type: pearson_cosine
value: 0.15590647163663807
name: Pearson Cosine
- type: spearman_cosine
value: 0.28867513459481287
name: Spearman Cosine
- type: pearson_manhattan
value: 0.20874094632850035
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.28867513459481287
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.21989747670451043
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.28867513459481287
name: Spearman Euclidean
- type: pearson_dot
value: 0.15590640231031966
name: Pearson Dot
- type: spearman_dot
value: 0.28867513459481287
name: Spearman Dot
- type: pearson_max
value: 0.21989747670451043
name: Pearson Max
- type: spearman_max
value: 0.28867513459481287
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `validation`
* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.1559 |
| spearman_cosine | 0.2887 |
| pearson_manhattan | 0.2087 |
| spearman_manhattan | 0.2887 |
| pearson_euclidean | 0.2199 |
| spearman_euclidean | 0.2887 |
| pearson_dot | 0.1559 |
| spearman_dot | 0.2887 |
| pearson_max | 0.2199 |
| **spearman_max** | **0.2887** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 20 training samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------|
| type | string | string | int |
| details |
search for anti slip shoes
| Mens Shower Shoes With Holes Dry Quickly Bath Slippers Womens Non Slip Indoor Home Bedroom Pool Spa Guest College Dorm
| 1
|
| men slim jeans
| Urbano Fashion Mens Slim Fit Jeans
| 1
|
| Looking for a red cotton shirt
| Cotton Regular Fit Solid Red Shirt
| 1
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 5 evaluation samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | sandal
| Beslip Womens Mens Garden Clogs Shoes with Arch Support Unisex Comfort Slip-on Sandals
| 1
|
| Looking for a men black jeans
| DENNIE FOSTE Men Regular Mid Rise Black Jeans
| 1
|
| comfortable running shoes
| NYKD Everyday Stylish Running Sports Jacket with Pockets for Women
| 0
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `learning_rate`: 3e-05
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `ddp_find_unused_parameters`: False
#### All Hyperparameters