README.md

---
license: mit
datasets:
- dleemiller/wiki-sim
- sentence-transformers/stsb
language:
- en
metrics:
- spearmanr
- pearsonr
base_model:
- chandar-lab/NeoBERT
pipeline_tag: text-classification
library_name: sentence-transformers
tags:
- cross-encoder
- neobert
- stsb
- stsbenchmark-sts
model-index:
- name: CrossEncoder based on chandar-lab/NeoBERT
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9208501169893029
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9211827194606879
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.9123513299488885
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9087449124017827
      name: Spearman Cosine
---
# NeoBERT Cross-Encoder: Semantic Similarity (STS)

Cross encoders are high performing encoder models that compare two texts and output a 0-1 score.
I've found the `cross-encoders/roberta-large-stsb` model to be very useful in creating evaluators for LLM outputs.
They're simple to use, fast and very accurate.

---

## Features
- **High performing:** Achieves **Pearson: 0.9124** and **Spearman: 0.9087** on the STS-Benchmark test set.
- **Efficient architecture:** Based on the NeoBERT design (250M parameters), offering faster inference speeds.
- **Extended context length:** Processes sequences up to 4096 tokens, great for LLM output evals.
- **Diversified training:** Pretrained on `dleemiller/wiki-sim` and fine-tuned on `sentence-transformers/stsb`.

---

## Performance

| Model                          | STS-B Test Pearson | STS-B Test Spearman | Context Length | Parameters | Speed  |
|--------------------------------|--------------------|---------------------|----------------|------------|---------|
| `ModernCE-large-sts`           | **0.9256**         | **0.9215**          | **8192**       | 395M       | **Medium** |
| `ModernCE-base-sts`            | **0.9162**         | **0.9122**          | **8192**       | 149M       | **Fast** |
| `NeoCE-sts`                    | **0.9124**         | **0.9087**          | **4096**       | 250M       | **Fast** |
| `stsb-roberta-large`           | 0.9147            | -              | 512            | 355M       | Slow    |
| `stsb-distilroberta-base`      | 0.8792            | -              | 512            | 82M        | Fast    |


---

## Usage

To use NeoCE for semantic similarity tasks, you can load the model with the Hugging Face `sentence-transformers` library:

```python
from sentence_transformers import CrossEncoder

# Load NeoCE model
model = CrossEncoder("dleemiller/NeoCE-sts")

# Predict similarity scores for sentence pairs
sentence_pairs = [
    ("It's a wonderful day outside.", "It's so sunny today!"),
    ("It's a wonderful day outside.", "He drove to work earlier."),
]
scores = model.predict(sentence_pairs)

print(scores)  # Outputs: array([0.9184, 0.0123], dtype=float32)
```

### Output
The model returns similarity scores in the range `[0, 1]`, where higher scores indicate stronger semantic similarity.

---

## Training Details

### Pretraining
The model was pretrained on the `pair-score-sampled` subset of the [`dleemiller/wiki-sim`](https://huggingface.co/datasets/dleemiller/wiki-sim) dataset. This dataset provides diverse sentence pairs with semantic similarity scores, helping the model build a robust understanding of relationships between sentences.
- **Classifier Dropout:** a somewhat large classifier dropout of 0.3, to reduce overreliance on teacher scores.
- **Objective:** STS-B scores from `cross-encoder/stsb-roberta-large`.

### Fine-Tuning
Fine-tuning was performed on the [`sentence-transformers/stsb`](https://huggingface.co/datasets/sentence-transformers/stsb) dataset.

---

## Model Card

- **Architecture:** NeoBERT
- **Pretraining Data:** `dleemiller/wiki-sim (pair-score-sampled)`
- **Fine-Tuning Data:** `sentence-transformers/stsb`

---

## Thank You

Thanks to the chandra-lab team for providing the NeoBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.

---

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{moderncestsb2025,
  author = {Miller, D. Lee},
  title = {NeoCE STS: An STS cross encoder model},
  year = {2025},
  publisher = {Hugging Face Hub},
  url = {https://huggingface.co/dleemiller/ModernCE-base-sts},
}
```

---

## License

This model is licensed under the [MIT License](LICENSE).