hs-rerank-1

hs-rerank-1 is a multilingual cross-encoder reranker built for Hindsight. It reads a query and a document together and returns a relevance score.

Highlights

• Multilingual: English, Spanish, French, German, Portuguese, Japanese, Korean and Chinese (Simplified and Traditional).
• Long inputs: up to 8,192 tokens per query and document pair.
• Small: 114M parameters. 98M of them are the token embedding table, so each forward pass runs a 15M-parameter transformer.
• CPU and GPU: ships an int8 ONNX export for CPU, and safetensors for GPU in FP16.
• Serves on TEI: CPU and GPU images.

Model overview

Evaluation

Usage

Sentence Transformers

pip install -U sentence-transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("hindsight-ai/hs-rerank-1")

query = "¿Qué app de presupuesto estoy creando?"
documents = [
    "El usuario quiere tooltips en su app de presupuesto.",
    "El usuario corre los domingos por la mañana.",
]

print(model.rank(query, documents))
# [{'corpus_id': 0, 'score': 0.9647}, {'corpus_id': 1, 'score': 0.0003}]

predict and rank return scores between 0 and 1. Pass activation_fn=torch.nn.Identity() for raw logits.

Text Embeddings Inference

CPU (uses onnx/model.onnx):

docker run -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:cpu-1.9.3 \
  --model-id hindsight-ai/hs-rerank-1 --auto-truncate \
  --max-client-batch-size 128

GPU:

docker run --gpus all -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:89-1.9.3 \
  --model-id hindsight-ai/hs-rerank-1 --auto-truncate \
  --max-client-batch-size 128

Use the GPU image tag for your GPU: 89-1.9.3 for L4 and other Ada GPUs, 1.9.3 for A100 and H100.

curl 127.0.0.1:8080/rerank \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "¿Qué app de presupuesto estoy creando?",
    "texts": [
      "El usuario quiere tooltips en su app de presupuesto.",
      "El usuario corre los domingos por la mañana."
    ]
  }'

Hindsight

HINDSIGHT_API_RERANKER_PROVIDER=local
HINDSIGHT_API_RERANKER_LOCAL_MODEL=hindsight-ai/hs-rerank-1

Or point Hindsight at a TEI server:

HINDSIGHT_API_RERANKER_PROVIDER=tei
HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8080

Hindsight Inference

hs-rerank-1 is also served at https://inference.hindsight.vectorize.io. To use it from Hindsight:

HINDSIGHT_API_RERANKER_PROVIDER=hindsight
HINDSIGHT_API_RERANKER_HINDSIGHT_API_KEY=<your Hindsight Inference API key>

License

MIT

Citation

@misc{hindsightai2026hsrerank1,
  title  = {hs-rerank-1},
  author = {{Hindsight AI}},
  year   = {2026},
  url    = {https://huggingface.co/hindsight-ai/hs-rerank-1}
}