README.md

---
license: mit
datasets:
- dleemiller/wiki-sim
- sentence-transformers/stsb
language:
- en
metrics:
- spearmanr
- pearsonr
base_model:
- jhu-clsp/ettin-encoder-68m
pipeline_tag: text-ranking
library_name: sentence-transformers
tags:
- cross-encoder
- modernbert
- sts
- stsb
- stsbenchmark-sts
model-index:
- name: CrossEncoder based on jhu-clsp/ettin-encoder-68m
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.9004259392300688
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8926090410746396
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9194308725342207
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9162169914711082
      name: Spearman Cosine
---

# EttinX 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.

The Ettin series followed up with new encoders trained on the ModernBERT architecture, with a range of sizes, starting at 17M.
The reduced parameters and computationally efficient interleaved local/global attention layers make this a very fast model,
which can easily process a few hundred sentence pairs per second on CPU, and a few thousand per second on my A6000.

---

## Features
- **High performing:** Achieves **Pearson: 0.9004** and **Spearman: 0.8926** on the STS-Benchmark test set.
- **Efficient architecture:** Based on the Ettin-encoder design (68M parameters), offering very fast inference speeds.
- **Extended context length:** Processes sequences up to 8192 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** |
| `stsb-roberta-large`           | 0.9147            | -              | 512            | 355M       | Slow    |
| `EttinX-sts-m`                 | 0.9143        | 0.9102          | **8192**       | 149M       | **Fast** |
| `EttinX-sts-s`                 | 0.9004        | 0.8926          | **8192**       | 68M       | **Very Fast** |
| `stsb-distilroberta-base`      | 0.8792            | -              | 512            | 82M        | Fast    |
| `EttinX-sts-xs`                | 0.8763        | 0.8689          | **8192**       | 32M       | **Very Fast** |
| `EttinX-sts-xxs`               | 0.8414        | 0.8311          | **8192**       | 17M       | **Very Fast** |

---

## Usage

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

```python
from sentence_transformers import CrossEncoder

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

# 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.

### Validation Results
The model achieved the following test set performance after fine-tuning:
- **Pearson Correlation:** 0.9004
- **Spearman Correlation:** 0.8926

---

## Model Card

- **Architecture:** Ettin-encoder-68m
- **Tokenizer:** Custom tokenizer trained with modern techniques for long-context handling.
- **Pretraining Data:** `dleemiller/wiki-sim (pair-score-sampled)`
- **Fine-Tuning Data:** `sentence-transformers/stsb`

---

## Thank You

Thanks to the Johns Hopkins team for providing the ModernBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.

[Seq vs Seq: An Open Suite of Paired Encoders and Decoders](https://arxiv.org/abs/2507.11412)
---

## Citation

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

```bibtex
@misc{ettinxstsb2025,
  author = {Miller, D. Lee},
  title = {EttinX STS: An STS cross encoder model},
  year = {2025},
  publisher = {Hugging Face Hub},
  url = {https://huggingface.co/dleemiller/EttinX-sts-xxs},
}
```

---

## License

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