---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
base_model: princeton-nlp/unsup-simcse-roberta-large
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on princeton-nlp/unsup-simcse-roberta-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [princeton-nlp/unsup-simcse-roberta-large](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-large). 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:** [princeton-nlp/unsup-simcse-roberta-large](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-large) <!-- at revision d3f863b476c59b0673264042f159cea15842e265 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 1024 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/huggingface/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, 'architecture': 'RobertaModel'})
  (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': False})
  (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## 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("yehzw/wow")
# 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, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.1249, -0.0492],
#         [ 0.1249,  1.0000,  0.3827],
#         [-0.0492,  0.3827,  1.0000]])
```

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## Training Details

### Framework Versions
- Python: 3.10.19
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.9.0
- Accelerate: 1.11.0
- Datasets: 4.4.1
- Tokenizers: 0.22.1

## Citation

### BibTeX

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