---
license: apache-2.0
language:
- en
base_model:
- answerdotai/ModernBERT-base
base_model_relation: finetune
pipeline_tag: text-ranking
library_name: transformers
tags:
- sentence-transformers
- transformers.js
- text-embeddings-inference
- mlx
---

# afanjul/gte-reranker-modernbert-base-mlx

The Model [afanjul/gte-reranker-modernbert-base-mlx](https://huggingface.co/afanjul/gte-reranker-modernbert-base-mlx) was converted to MLX format from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) using mlx-lm version **0.1.1**.

## Use with mlx

```bash
pip install mlx-embeddings
```

```python
from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("afanjul/gte-reranker-modernbert-base-mlx")

# For reranking (sequence classification)
pairs = [
    ["what is the capital of China?", "Beijing"],
    ["how to implement quick sort in python?", "Introduction of quick sort"],
]
output = generate(model, processor, texts=pairs, max_length=8192)
scores = output.pooler_output.squeeze()

print("Reranking scores:")
for pair, score in zip(pairs, scores.tolist()):
    print(f"  Query: {pair[0]}")
    print(f"  Document: {pair[1]}")
    print(f"  Score: {score:.4f}")
    print()


```