Model reference · open weights

japanese-reranker-cross-encoder-large

Available as managed deployment Embeddings hotchpotch · community Reranker 1 variants 2k dl/mo

japanese-reranker-cross-encoder-large is an open-weight embedding model from hotchpotch. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byhotchpotch
TypeEmbedding models
TaskReranker
Parameters (lead)337M
Context512 tokens
Runs withsentence-transformers
Released2024-03-28
Popularity2k downloads / month
LicenceOpen weights

About

What japanese-reranker-cross-encoder-large is

hotchpotch/japanese-reranker-cross-encoder-large-v1

日本語で学習させた Reranker (CrossEncoder) シリーズです。

Read the full model card
モデル名layershidden_size
hotchpotch/japanese-reranker-cross-encoder-xsmall-v16384
hotchpotch/japanese-reranker-cross-encoder-small-v112384
hotchpotch/japanese-reranker-cross-encoder-base-v112768
hotchpotch/japanese-reranker-cross-encoder-large-v1241024
hotchpotch/japanese-bge-reranker-v2-m3-v1241024

Reranker についてや、技術レポート・評価等は以下を参考ください。

使い方

SentenceTransformers

from sentence_transformers import CrossEncoder
import torch

MODEL_NAME = "hotchpotch/japanese-reranker-cross-encoder-large-v1"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = CrossEncoder(MODEL_NAME, max_length=512, device=device)
if device == "cuda":
    model.model.half()
query = "感動的な映画について"
passages = [
    "深いテーマを持ちながらも、観る人の心を揺さぶる名作。登場人物の心情描写が秀逸で、ラストは涙なしでは見られない。",
    "重要なメッセージ性は評価できるが、暗い話が続くので気分が落ち込んでしまった。もう少し明るい要素があればよかった。",
    "どうにもリアリティに欠ける展開が気になった。もっと深みのある人間ドラマが見たかった。",
    "アクションシーンが楽しすぎる。見ていて飽きない。ストーリーはシンプルだが、それが逆に良い。",
]
scores = model.predict([(query, passage) for passage in passages])

HuggingFace transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.nn import Sigmoid

MODEL_NAME = "hotchpotch/japanese-reranker-cross-encoder-large-v1"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.to(device)
model.eval()

if device == "cuda":
    model.half()

query = "感動的な映画について"
passages = [
    "深いテーマを持ちながらも、観る人の心を揺さぶる名作。登場人物の心情描写が秀逸で、ラストは涙なしでは見られない。",
    "重要なメッセージ性は評価できるが、暗い話が続くので気分が落ち込んでしまった。もう少し明るい要素があればよかった。",
    "どうにもリアリティに欠ける展開が気になった。もっと深みのある人間ドラマが見たかった。",
    "アクションシーンが楽しすぎる。見ていて飽きない。ストーリーはシンプルだが、それが逆に良い。",
]
inputs = tokenizer(
    [(query, passage) for passage in passages],
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="pt",
)
inputs = {k: v.to(device) for k, v in inputs.items()}
logits = model(**inputs).logits
activation = Sigmoid()
scores = activation(logits).squeeze().tolist()

評価結果

Model NameJQaRAJaCWIRMIRACLJSQuAD
japanese-reranker-cross-encoder-xsmall-v10.61360.93760.74110.9602
japanese-reranker-cross-encoder-small-v10.62470.9390.77760.9604
japanese-reranker-cross-encoder-base-v10.67110.93370.8180.9708
japanese-reranker-cross-encoder-large-v10.70990.93640.84060.9773
japanese-bge-reranker-v2-m3-v10.69180.93720.84230.9624
bge-reranker-v2-m30.6730.93430.83740.9599

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys japanese-reranker-cross-encoder-large for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (japanese-reranker-cross-encoder-large below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"japanese-reranker-cross-encoder-large","input":"text to embed"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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