---
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - text-similarity
  - contrastive-learning
  - semantic-alignment
  - text-evaluation
library_name: pytorch
pipeline_tag: sentence-similarity
---

# MATCHA — Matching Text via Contrastive Semantic Alignment

MATCHA is a learned text similarity metric that captures both semantic alignment and contradiction through contrastive training. It learns a dual-view semantic space in which semantically aligned texts are pulled closer while contradictory or irrelevant texts are pushed apart.

**Paper:** [MATCHA: Matching Text via Contrastive Semantic Alignment](https://arxiv.org/abs/2605.27345)
**Code:** [GitHub](https://github.com/Siran-Li/MATCHA)

## Model Details

- **Backbone:** GPT-2 (word embeddings only, no transformer layers)
- **Architecture:** Token-independent MLP processing with a learned transformation and mean pooling
- **Training objective:** Triplet margin loss with cosine similarity
- **Training data:** 15 diverse sources across NLI, factuality, captioning, summarization, and paraphrase tasks

## Files

| File | Description |
|------|-------------|
| `max_diff.pth` | Best checkpoint (selected by max pos–neg similarity difference) |
| `config.yaml` | Training hyperparameters |
| `model_config.json` | Model architecture configuration |
| `model.py` | Model architecture code |
| `matcha.py` | Simple inference interface |

## Installation

```bash
pip install matcha-metric
```

## Usage

```python
from matcha_metric import MATCHA

model = MATCHA.from_pretrained("Siran-Li/MATCHA")

# Score a pair of texts
similarity = model.score("The vaccine was proven effective.", "Clinical trials confirmed the vaccine works.")
print(f"Similarity: {similarity:.4f}")

# Batch scoring
scores = model.score(
    ["The cat sat on the mat.", "It is raining outside."],
    ["A feline rested on the rug.", "The weather is sunny and clear."],
)

# Get embeddings directly
embeddings = model.encode(["Hello world", "Hi there"])
```

### Interpretability

Token-level attribution via Integrated Gradients:

```python
# Get raw token attributions
result = model.interpret("The cat sat on the mat.", "A feline rested on the rug.")
for token, attr in zip(result["tokens"], result["attributions"]):
    print(f"{token:>15s}  {attr:+.4f}")

# Save interactive HTML heatmap
model.visualize(
    "The cat sat on the mat.",
    "A feline rested on the rug.",
    label="Correct",
    output_path="attribution.html",
)
```

**Aligned pair** ("The cat sat on the mat." vs. "A feline rested on the rug."):

![Attribution example - aligned](attrbution_ex1.jpeg)

**Contradictory pair** ("It is raining outside." vs. "The weather is sunny and clear."):

![Attribution example - contradictory](attrbution_ex2.jpeg)

## Evaluation Benchmarks

Evaluated on 7 benchmarks: SNLI, MultiNLI, MedNLI, TruthfulQA, COCO-Caption, NEWTS, and Climate-FEVER.

## Citation

```bibtex
@article{li2026matcha,
  title={MATCHA: Matching Text via Contrastive Semantic Alignment},
  author={Li, Siran and Etoglu, Ece Sena and Eickhoff, Carsten and Bahrainian, Seyed Ali},
  journal={arXiv preprint arXiv:2605.27345},
  year={2026}
}
```