---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- ecommerce
- e-commerce
- retail
- marketplace
- shopping
- amazon
- ebay
- alibaba
- google
- rakuten
- bestbuy
- walmart
- flipkart
- wayfair
- shein
- target
- etsy
- shopify
- taobao
- asos
- carrefour
- costco
- overstock
- pretraining
- encoder
- language-modeling
- foundation-model
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# RexBERT-base-embed-pf-v0.3

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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': 2048, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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, '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("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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.9961, 0.8477, 0.8750],
#         [0.8477, 0.9961, 0.8047],
#         [0.8750, 0.8047, 1.0078]], dtype=torch.bfloat16)
```

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### Downstream Usage (Sentence Transformers)

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## Training Details

### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 5.1.1
- Transformers: 4.53.3
- PyTorch: 2.7.0
- Accelerate: 1.10.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4

## Citation

### BibTeX

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