---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:14356
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-en-v1.5
widget:
- source_sentence: Pear trees are usually productive for 50 to 75 years though some
still produce fruit after 100 years .
sentences:
- In the late 1950s , he studied cinema in France .
- Pear trees are usually productive for 50 to 75 years though some still produce
fruit after 100 years .
- A recording medium is a physical material that holds data expressed in any of
the existing recording formats .
- source_sentence: On poor , dry soils there are tropical heathlands .
sentences:
- On poor , dry soils there are tropical heathlands .
- There are plans to build a new library at my school .
- These are forest birds that tend to feed on insects at or near the ground .
- source_sentence: According to Statistics Canada , the county has a total area of
2004.44 km2 .
sentences:
- In 2018 , there are eleven senators holding ministerial positions and the head
of state , the First mayor .
- According to Statistics Canada , the county has a total area of 2004.44 km2 .
- There are some common ways used to stretch piercings , of different origins and
useful for different people .
- source_sentence: Oll , who was married , fell into severe depressions after he divorced
.
sentences:
- Tide pools are a home for hardy organisms such as sea stars , mussels and clams
.
- Endgames can be studied according to the types of pieces that remain on board
.
- Oll , who was married , fell into severe depressions after he divorced .
- source_sentence: She often shared her boots with her sister .
sentences:
- She often shared her boots with her sister .
- Many of the Greek city -states also had a god or goddess associated with that
city .
- Until 1 April 2010 the Departments were as follows .
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en-v1.5
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dev
type: dev
metrics:
- type: pearson_cosine
value: 0.15520756564467134
name: Pearson Cosine
- type: spearman_cosine
value: 0.13351347160793242
name: Spearman Cosine
---
# SentenceTransformer based on BAAI/bge-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(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("Mr-FineTuner/Eval_01_Final_3")
# Run inference
sentences = [
'She often shared her boots with her sister .',
'She often shared her boots with her sister .',
'Until 1 April 2010 the Departments were as follows .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `dev`
* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.1552 |
| **spearman_cosine** | **0.1335** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 14,356 training samples
* Columns: sentence_0
, sentence_1
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details |
Construction of the temple complex started in approximately 1264 BC and lasted for about 20 years , until 1244 BC .
| Construction of the temple complex started in approximately 1264 BC and lasted for about 20 years , until 1244 BC .
| 3.0
|
| He knew which bag to buy for his older sister 's birthday .
| He knew which bag to buy for his older sister 's birthday .
| 3.0
|
| The precise origin of absinthe is unclear .
| The precise origin of absinthe is unclear .
| 4.0
|
* Loss: [MultipleNegativesRankingLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters